UBC Undergraduate Research

LiteFarm Sustainability Assessment Framework Wohlers, Amelia; Le, Edward; Stewart, Megan; Frame, Mikaela 2021-04

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 2 LiteFarm Sustainability Assessment Framework     Source: Wohlers (2019) Abstract The primary research objective of this project is the development of a preliminary sustainability assessment framework that can be incorporated into LiteFarm’s goal of supporting and educating smallholder farmers (e.g. family-based farmers in lower/middle income countries on land ~2 hectares) on various facets of the economic, environmental, and social sustainability of their agricultural practices. The framework is made up of 9 indicators measuring 17 different approved parameters (data collection points) of sustainability. These parameters were checked against a list of criteria that was developed with a strong emphasis on accessibility. In addition to the 17 approved parameters, 19 conditionally approved parameters were found, and 7 parameters were rejected based on the developed criteria. The input required to quantify each parameter was developed with an exploration of the feasibility of data collection methods. These methods may be completed by the farmers themselves or data may be accessed from a third-party source such as global maps of farmland water availability risk data. A proposed set of designs for integrating potential indicators with the current LiteFarm app have also been drafted, along with suggestions for further research for the future development of the LiteFarm sustainability assessment framework Authors: Amelia Wohlers Edward Le  Megan Stewart Mikaela Frame  3 About the Authors: ● Amelia Wohlers: My name is Amelia and I am a fourth-year Environmental Science Major based out of Vancouver BC. I was inspired to study sustainability after living and working for a year in a subsistence farming community in rural Ecuador. Since starting at UBC, my studies have focused heavily on resource management and I have become passionate about the idea of problem solving how to build sustainability into any system, whether it be a farm, a business, or a country.   ● Edward Le:  My name is Edward and I am a fourth-year Environmental and Computer Sciences major with experience working on geospatial analyses and software development. In previous roles I have contributed to analyses of natural climate solutions across Canadian agricultural lands, along with other work on hydrological analyses of flow data.   ● Megan Stewart: My name is Megan and I am also a fourth-year Environmental Sciences Major at UBC. I have a strong interest in small-scale agricultural sustainability and have field experience working on a small organic produce farm. I have utilised R to develop meteorology applications that analyze and display weather data in my role as a Junior Air Specialist with Environment and Climate Change Canada as well as experience using Matlab in a research setting.   ● Mikaela Frame: My name is Mikaela and I am a part-time student pursuing a Bachelor in Earth and Ocean Science and a Masters in Management. Outside of UBC, I have worked as the Director of Sales & Sustainability at Susgrainable, a Vancouver-based start-up that partners with local microbreweries to upcycle beer “waste” into a nutrient dense flour. I have over 4 years of research experience working with projects to measure the net benefit of circular production systems relative to traditional linear systems it aims to replace. Otherwise, I have experience working with software development teams on a greywater treatment and reuse project to provide off-grid communities in the middle-east with an alternative water source for agricultural purposes.       4 Table of Contents 1. Glossary 5 2. Background Information 8 Project Overview 8 Background on LiteFarm 8 Our Project: 9 Our Project: Theoretical Context 9 Comparison to TAPE 12 Our Project: Assessing Frameworks 13 Our Project: Data Collection 13 Our Project: Goals and End Products 14 3. Research Objectives 15 4. Methodology 16 Literature Review of Sustainability Measurement Criteria 17 Assumptions for Developing Criteria 18 How Criteria Can Be Applied 20 Criteria for Selecting Individual Agricultural Sustainability Parameters 21 Criteria for Balancing Parameters in Larger Framework 26 Requirements for Implementation of Balancing Criteria 27 5. Results: Indicators and Parameters Analyzed 28 Results of Criteria Application 30 Approved Parameters: 30 Conditionally-Approved Parameters 35 Rejected Parameters 40 6. Meta-Analysis of Evaluated Parameters 42 7. Proposed Visual Designs in the LiteFarm app 46 User Interface Research and Inspiration 47 Proposed Design for Insights Integration 52 a.)    Summary View 53 b.)    Indicator View 56 c.)    Parameter View 59 8. Recommendations for Future Research 63 9. Conclusion 65 10. References 66 Appendix A: Indicator Analysis Against Project-Developed Criteria 69 Appendix B TAPE Comparison and Analysis 136 Appendix C External Farm Sustainability Tool Survey: 153 5  1. Glossary Ecosystem Services: services such as nutrient cycling, biotic pollination, and pest control, can enhance biodiversity. These positive effects may compliment, interact synergistically, or even replace the need for ‘external inputs’, such as fertilizers, introduction of pollinator colonies, and pesticides.  (Garibaldi et al., 2016).  Evaluation Criteria: used to refer to the criteria developed for the purpose of evaluating indicators, based on a review of relevant literature, during this project for the purpose of evaluating the Preliminary List of Indicators and Parameters at the parameter level.  External Data: External inputs are information that the app can access without any farmer input. External inputs may include weather data or regional water supplies.  Farm Data: Inputs that require the farmer to input in the app. These data may include, field size or the price of a crop.  Indicator: An indicator is a sustainability theme or area like soil or working conditions. Indicators are made up of parameters. Insights: In LiteFarm, used synonymously with ‘Indicators’ to describe the theme of information or data collected in the app. All Sustainability Indicators (in LiteFarm) are Insights, but not all Insights are Sustainability Indicators, as they also include other farm management variables, such as those already in existence.  LiteFarm: “a free and open source farm management tool made for current and aspiring sustainable farmers” (LiteFarm, 2020). “LiteFarm was born out of the Centre for Sustainable Food Systems at the University of British Columbia and is now being developed by a global team of staff, students, and contributors” (LiteFarm, 2020).  Parameter: A parameter is a single metric of sustainability like off-season plant cover. Preliminary List of Indicators and Parameters: The indicators and parameters selected from the sustainability assessment frameworks, RISE, IDEA and TAPE used as a baseline for this report’s subsequent evaluations. This list was then analyzed using a developed set of criteria in order to produce the final LiteFarm Indicators and LiteFarm Framework. Smallholder Farms: A small agricultural operation, typically less than 2 hectares in size (Rapsomanikis, 2015). For LiteFarm, this has typically included assumptions for the smallholder farm as a family-based operation, generally farms within (but not limited to) developing economies, and those farms who may/may not hire seasonal workers to aid with seeding and harvesting (Cussen, 2020).   Sustainability: How well a system can reliably produce output in the long-term (Kroese, 2019).  6 Sustainable Agriculture:  Agriculture that "fulfills a balance of several goals including some expression of maintenance or enhancement of the natural environment, provision of human food needs, economic viability, and social welfare through time [17]... Sustainable agriculture has been described as a term encompassing several ideological approaches including organic farming, biological agriculture, ecological agriculture, biodynamic agriculture, regenerative agriculture, permaculture, and agroecology [16, 18, 19, 20]". "Neher [21] considered it as an approach or a philosophy that integrates land stewardship with agriculture, where land is managed with respect to allow a future for next generations" (Cruz et al., 2018).  Sustainability Assessment Framework: also referred to in this report as “Assessment Framework”, “Sustainability Framework”, and “Framework”, is a measurement, estimation or appraisal of the level of sustainability of a defined system (Kroese, 2019). For the purpose of this project, a framework uses a collection of Indicators to give an overall picture of the sustainability of a system. IDEA: Indicateurs de Durabilité des Exploitations Agricoles (IDEA) is a sustainability framework developed by the European Union’s desire to build sustainable development into agricultural practices policies (Zahm et al., 2008). MESMIS: Natural Resource Management System (Marco para la Evaluación de Sistemas de Manejo de recursos naturales incorporando Indicadores de Sustentabilidad, Spanish) is a framework that outlines a process for cooperative sustainability framework development with stakeholders (Kroese, 2019). LiteFarm Framework: The sustainability assessment framework developed as a product of this project to be incorporated into the current LiteFarm farm management webapp. This framework focuses on ensuring metrics of sustainability are accessible and feasible for collection by smallholder farmers. RISE: Regulatory Indicators for Sustainable Energy (RISE) is a sustainability assessment framework developed with the goal of measuring sustainability of agricultural production in the context of education, agricultural extension, and supply chain management (Häni & Keller, 2017). SAFA: Sustainability Assessment of Food and Agriculture (SAFA) is a sustainability assessment framework developed by the Food and Agriculture Organization of the United Nations for assessment of sustainability in agricultural settings (Kroese, 2019). TAPE: The Tool for Agroecology Performance and Evaluation (TAPE) is an agroecological  sustainability framework created by the UN using sustainability indicators pulled from various different frameworks (FAO, 2019).  Sustainability Assessment Tool: used in this report to refer to a functional tool used to assess the sustainability of a system. In the context of LiteFarm, this would refer to the component of LiteFarm used by their user (presumably, the farmer) as a self-assessment of their farm’s sustainable performance. The Sustainability Assessment Framework developed in this project will be used as the basis of LiteFarm’s sustainability assessment tool. In practice, this will involve incorporating the constituent Indicators into the farm management app alongside the existing “Insights”. 7  Sustainable Development:  A form of human development, typically along social, political and economic lines that “[meets] the needs of the present without compromising the ability of future generations to meet their own needs” (World Commission on Environment and Development, 1987).      8 2. Background Information Project Overview This report outlines the development of a preliminary sustainability framework that will be implemented into LiteFarm’s existing farm management webapp. This framework leads to quantification of the sustainability of smallholder agricultural operations and can help to inform decisions and actions to improve the sustainability of these operations. To achieve the framework, a list of indicators from current theoretical sustainability frameworks were compiled. A set of criteria based on LiteFarm’s mission and demographics was created. Finally, indicators were evaluated based on this criteria to create the final preliminary framework. Items beyond the scope of this project include the implementation of LiteFarm indicators into the LiteFarm app, and stakeholder engagement.  Background on LiteFarm The LiteFarm webapp is a free, open-source, community-driven, non-profit farm management tool started at the University of British Columbia under the Centre for Sustainable Food Systems (LiteFarm, 2020). It is an open source agricultural technology (AgTech) app intended for use by smallholder, sustainable farms across the globe. It is intended to be used for day to day operations and in long term management decisions with sustainability at the core.  Core features the app currently supports include operational management, financial management, and organic certification processing. Operationally, it provides farm users with notifications on tasks, visual mapping of the farm, and with record-keeping (ex. seeding, inventory, and payroll, LiteFarm, 2020). Financially, it helps provide insights through crop-based analysis of profitability, historical price analysis, and support for Payment for Ecological Services (PES) and organic and agroecological certification schemes. This project aims to support and build from current operations performed by the LiteFarm app. Figure 1 shows how notifications are handled within the app. LiteFarm moved out of the beta testing phase and launched in October 2020. It was rolled out in 6 countries across North and South America and they aim to reach more than 10,000 farmers in the next 3 years (LiteFarm, 2020). This international demographic was important to the development of this project. Figure 1. A view of sample notification for a farm that features carrots, lettuce, and blueberries, with associated tasks such as seeding, harvest, and fertilizing.  9 Aspirationally, LiteFarm aims to empower farmers with useful, actionable insights to help them grow food more sustainably, and improve their farms, their communities, and our planet. Key to that would be finding tangible ways to measure how a farm is doing, and how it can be improved. This is the component of the app that this report concerns. This report examines sustainability indicators to support new analytics features on the app which will provide farmers with the information they need to improve the sustainability of their operations.  The LiteFarm Team LiteFarm is the community partner for this project and the LiteFarm Team is the group of people who are developing the webapp. Our primary point of contact with the team was Kevin Cussen, LiteFarm’s product lead. The team is made up of researchers, students, and software developers who are collaborating with the goal of making sustainable farming more accessible to farmers of all backgrounds.   Our Project: Our Project: Theoretical Context A key distinction of LiteFarm that separates it from other farm management tools is its emphasis on sustainability. The working definition of sustainability for this project is the capacity of a system to function perennially or to produce a reliable output in the long-term (Kroese, 2019).  Sustainability can be broken down into three core pillars: environmental, economic, and social. These approaches will ultimately benefit farmers in a variety of ways. For example, regenerative agricultural practices which include strategies such as avoidance of synthetic fertilizers and employing multiple crop rotations, has been shown to improve water and nutrient uptake in crops and encourage the proliferation of vital microbes (Project Drawdown, 2020). These outcomes lead to higher yields, more stability in crops inter-annually, and higher profits for farmers while also contributing to carbon sequestration (Project Drawdown, 2020). LiteFarm aims to improve outcomes for farmers while simultaneously addressing global environmental concerns. Sustainability frameworks are used to incorporate sustainability into quantifiable measurements and data inputs (Cussen, 2020). Frameworks use a collection of indicators to give an overall picture of the sustainability of a system. In this project, a system is a farm. Potential indicators are broad categories such as water, biodiversity, or economics. The collection of indicators make up the sustainability framework (Figure 2).  10  Figure 1. A visual representation of the LiteFarm Framework. The framework is made of multiple indicators which are made up of parameters. Parameters outlined in blue were approved and those outlined in yellow were conditionally approved. Rejected parameters are not shown. 11 Each indicator is made up of a collection of parameters which are quantifiable data inputs. In Figure 3 below, the indicator of water and its relationship to 3 parameters are outlined. (B1: Percentage of Farmland irrigated, B2: Water Management, B4: Water Supply).  Note that parameter B3: Track Devices was rejected after analysis. More details can be found in Appendix A.  Figure 2. A view of the relationships between farm data, external data, methodology, approved LiteFarm parameters and a final indicator output for the water indicator of our LiteFarm indicator framework. Both farm data and external data are input into the app. The app follows scoring methodology unique to each parameter and assigns a score out of 100 to each parameter. The indicator is then scored based off the average of parameter scores. The arrows indicate the flow of information from base data, through our framework to a final score. Background research into compatible frameworks for LiteFarm was completed by Adrian Kroese (2019) in their thesis entitled “Integrated Sustainability Assessment for Small-Scale Diversified Farms”, using the UBC farm as a pilot for research and applications. Kroese narrows down four effective sustainability frameworks: IDEA, MESMIS, RISE, SAFA, and TAPE. IDEA (Indicateurs de Durabilité des Exploitations Agricoles) is a sustainability framework developed by France (Zahm et al., 2008). MESMIS (Natural Resource Management System) outlines a process for cooperative sustainability framework development with stakeholders (Kroese, 2019). RISE (Regulatory Indicators for Sustainable Energy) is a framework developed for the assessment of agricultural sustainability at a national level (Häni & Keller, 2017). Finally, SAFA (Sustainability Assessment of Food and Agriculture) is a framework developed by the Food and Agriculture Organization of the United Nations for assessment of sustainability in agricultural settings (Kroese, 2019). We evaluated the four frameworks and identified IDEA and RISE most relevant to smallholder agriculture and LiteFarm. MESMIS offered useful evaluation tools but no actual indicators. SAFA offered indicators but was found to be less relevant to the target demographics of LiteFarm and more relevant to larger scale policy creation. The RISE and IDEA frameworks both use data from accounting, agricultural practices, remote sensing and interviews to generate sustainability assessments (Kroese, 2019). RISE was created with the goal of measuring sustainability of agricultural production in the context of education, agricultural extension, and supply chain management (Häni & Keller, 2017). A sample assessment using RISE was done for the UBC 12 Farm as seen in Figure 4 (below) with different Indicators represented around the exterior of the spider chart.  IDEA was created in response to the European Union’s desire to build sustainable development into agricultural practices policies (Zahm et al., 2008). It was designed to allow farmers to self-assess their farms and provide operational guidance for the assessment of agricultural sustainability (Zahm et al., 2008). An IDEA spider chart assessment for the UBC farm was also completed and is illustrated in Figure 5 (below).    Both frameworks seek to provide a holistic view of sustainability on farms and create data to support farmer decision-making (Häni & Keller, 2017; Zahm et al., 2008). However, both of these frameworks were also created with larger scale commercial agriculture, mostly in developed countries in mind (Kroese, 2019). This differs significantly from LiteFarm’s target audience of smallholder farmers globally which may jeopardise their applicability to LiteFarm’s users. Adjustments must still be made to RISE and IDEA to account for accessibility and feasibility of data inputs as well as for the noncommercial nature of LiteFarm user’s economic goals before they will cohesively mesh with the needs of LiteFarm. Comparison to TAPE The final framework that was used for the initial indicator list is TAPE. It was selected for comparison at the request of the LiteFarm team. The Tool for Agroecology Performance and Evaluation (TAPE) is an agricultural sustainability framework created by the UN using sustainability indicators pulled from various different frameworks, similar to the LiteFarm framework (FAO, 2019). TAPE sets out 10 core criteria that must be met by a framework’s indicators to accurately and holistically assess sustainability of a system (FAO, 2019). In this comparison, LiteFarm indicators which meet the core criteria are noted, while gaps in the framework are filled by pulling indicators directly from TAPE. However, the scoring of TAPE indicators is based on a three tiered (Unsustainable, Acceptable, and Desirable) system, while the LiteFarm Framework utilizes scores out of 100 for each parameter (see Appendix A) (FAO, 2019). For the purposes of integrating TAPE indicators into LiteFarm, the following scores are assigned to each outcome. Figure 4. A sustainability spider chart generated through IDEA to represent sustainability at the UBC Farm, from Integrated Sustainability Assessment for Small-Scale Diversified Farms: A Pilot Study at the UBC Farm, Vancouver, Canada by Adrian Kroese (2019).   Figure 3.  A spider chart generated through RISE to represent the sustainability of UBC Farm, from Integrated Sustainability Assessment for Small-Scale Diversified Farms: A Pilot Study at the UBC Farm, Vancouver, Canada by Adrian Kroese (2019). Scores for each indicator are given out of 100 where a higher score represents better sustainability practices in relation to that indicator. 13   Table 1. A translation between TAPE outcomes and our LiteFarm indicator points. Scoring for indicators is out of 100 with a higher score correlating to higher perceived sustainability.  TAPE Outcome LiteFarm Indicator Scoring (points) Unsustainable 0 Acceptable 50 Desirable 100  Our Project: Assessing Frameworks One of the greatest challenges in creating a sustainability framework for LiteFarm is ensuring that sustainability assessments translate to feasible actions that improve the overall sustainability of farms. De Olde et al. found that the factors that limit effectiveness of frameworks are credibility, legitimacy, and salience (2018). Credibility describes the quality of data. If the quality of data being collected is poor, the accuracy of the sustainability assessment is also going to be poor. If the assessments are inaccurate, the framework is no longer useful. In attempts to make indicators accessible to facilitate understanding and feasible for farmers of any socio-economic and geographic background to collect data on, the quality of the data collection must not be compromised. For LiteFarm, this means that data inputs must be carefully selected for accessibility without compromising the accuracy and quality of data used to support each indicator.  Legitimacy is another key factor in sustainability framework creation. Legitimacy relates to the perception of the assessment tool by its users and stakeholders. If LiteFarm is poorly perceived by its users and its stakeholders, it will not be funded, supported, or used.  The final key factor to making an effective framework is salience. Salience refers to the relevance of the assessment to decision makers. If farmers are being asked to input a large amount of data into LiteFarm, but are not receiving useful management suggestions or actionable changes based on that data, data collection is a waste of their time. Therefore, the assessment tools must have direct benefits that farmers can see in order to become a tool they use and value. This was further supported by Coteur et al. who strongly linked the effectiveness of assessment tools with focus on implementation (2020). Any framework implemented in LiteFarm must keep these three factors in mind in order to be an effective assessment tool. Our Project: Data Collection For LiteFarm, there are two potential methods of data collection. The first and primary method of data collection is completed by the farmer (Cussen, 2020). This method is significantly limited by accessibility levels which require data inputs that farmers with no scientific tools can collect themselves. Extension officers working with farmers directly offer valuable insight to the exact limitations of accessibility (Cussen, 2020). As LiteFarm expands, there is potential to introduce more complex data inputs that only farmers with access to the appropriate tools would complete. This set of parameters would be optional and only 14 used by farmers who are able to collect the relevant data. The focus of this report is on the base level parameters that are accessible to all farmers. The secondary method of data collection that LiteFarm may utilize open-source software tools. These would not require any involvement from the farmer. Existing applications that utilize remote sensing data include weather analysis, environmental data integrations, and modelling which can directly input data into the app. See Appendix C for a list of tools that may be relevant in the collection of this data.  All of these data types inform parameters that farmers would be unable to input on their own. However, their inclusion offers key sustainability insights. By adjusting IDEA and RISE, while bringing in new and more applicable parameters and data inputs, this project will inform the holistic sustainability framework implemented in LiteFarm so that most farmers can successfully use and get value out of the app, regardless of their education or access or tools.  Our Project: Goals and End Products The goal of our project was to develop a preliminary sustainability framework that covers environmental, social, and economic dimensions of sustainability. A short form explanatory document was created for the LiteFarm team to help briefly explain the assessment framework to stakeholders. This document can be used to bring stakeholders, farmers, extension officers, or other researchers up to speed quickly on the broad details of the project. The short form explanatory document will include a visual breakdown of our final framework, the major findings from this report, and a broad overview of the methodology followed. A brief written explanation will be provided alongside these visuals.  This report will be provided to LiteFarm. In addition to the final created framework, this report contains details on background research of theoretical frameworks, the creation of criteria for included indicators, and the methodology of analysis of indicators and parameters. The report may be used for researchers building off this framework in the future.  Finally, a long form explanatory document that includes a complete list of indicators and their parameters has been created; along with the analysis, scoring, and relevant data collection methods for each parameter. Each parameter will be scored out of 100. The score for each indicator will then be made up of an average of it’s parameters. All indicators will be equally weighted in the framework. These indicators will be ready to be integrated into the LiteFarm app where further research on stakeholder engagement and feedback can be conducted. This document will exclude irrelevant data for the implementation team in order to communicate our findings in a concise manner to our client.        15 3. Research Objectives The goal of this project was the development of a preliminary sustainability framework that can be implemented in LiteFarm. The inclusion of a sustainability framework will add more utility to the existing management applications that the app already performs for farmers. Necessary data inputs for the framework utilize accessible data collection methods for farmers. LiteFarm was developed with farmers in low and lower-middle income countries in mind. These farmers may have minimal formal education, modest resources and are generally operating a smallholder family farm that sells products in local markets (Cussen, 2020). Other important stakeholders include agricultural extension officers who may be utilizing the application with these farmers or educating them on the use of LiteFarm (Cussen, 2020).  The framework is made up of indicators stemming from theoretical frameworks of sustainable agriculture which were examined to determine their compatibility with LiteFarm’s goals. Each indicator is informed by parameters which were assessed based on a set of criteria created for this report. Major components of criteria include, accessible data collection methods, the development of quantitative inputs for the application, analysis method for this input, and a sustainability score for that particular indicator communicated through a visual graph  (e.g. Figures 4 & 5, Background Information). Each parameter includes a general description, inputs and data collection methods, scoring suggestions, and analysis. Future work includes stakeholder engagement which will be conducted through the LiteFarm app.   Source: Wohlers (2019) 16 4. Methodology Overview The objective of this project was to develop the LiteFarm Framework that will serve as the farm-level sustainability assessment tool built into the LiteFarm farm management app. This section describes how the LiteFarm Framework was developed, including how the evaluation criteria was developed as well as how the preliminary indicators and parameters were evaluated.  Developing the LiteFarm Framework required a literature review of criteria used to evaluate sustainability assessment indicators. Based on the findings of this literature review, we developed our own evaluation criteria for evaluating sustainability indicators at the parameter level, which we subsequently used to analyze our preliminary set of parameters. This resulted in a final list of approved, conditionally approved, and rejected parameters. This section describes these processes. An overview of the stages of indicator analysis for framework creation are outlined in Figure 6 below. Finally, to support the implementation of these parameters into the LiteFarm app, a description of the value, required inputs, data collection methods, scoring, and analysis for each parameter can be found in Appendix A.     Figure 5. Overview of the stages of indicator analysis completed in order to develop the LiteFarm Framework. 17 Literature Review of Sustainability Measurement Criteria A literature review was conducted to explore how indicators are created and the criteria they are measured against to ensure they are valuable to the LiteFarm framework. This literature review led to the development of our own criteria utilized to evaluate indicators. There are a wide array of sustainability frameworks already within existence and use all around the world (Coteur et al., 2016; Chaudhary et Al., 2018; Lv et Al., 2019; Streimikis & Baležentis, 2020; Chopin et al., 2021). Most recognizable are the UN Sustainable Development Goals, an internationally used framework used to assess sustainable development at the country level (U.N., 2016). The applications of existing frameworks range across any imaginable system, many functioning by using their own unique collection of indicators appropriate for their own unique applications. In order to ensure that frameworks are using ‘appropriate’ indicators without requiring subjective judgments, they utilize strict and clearly defined criteria that establish clear justifications for selection as well as balancing the single indicator amongst the collection (de Olde et al., 2017). Thus, when developing a sustainability framework for LiteFarm, to function as a sustainability assessment instrument for farmers, the utility and efficacy of the indicators, and therefore the overall framework, is dependent on their relevance, feasibility, and practicality given the economic, environmental, and social context in which they are applied. The purpose is thus to inform strategic decision making at the farm-level (Coteur et al., 2016).   Given the lack of standardized criteria for selecting sustainability indicators for our specified purpose, to ensure each indicator’s suitability for implementation within LiteFarm, we found it necessary to develop precise selection criteria based on the priorities outlined by the LiteFarm team. As explained in de Olde et al. (2017), there is no consensus among experts as to what criteria must be used and it oscillates widely from framework to framework. For example, researchers working with commercial goals ranked “affordability” and “easily communicated” as being of higher importance in their frameworks while researchers aiming to derive technical comparisons between farms ranked “sensitivity” and “specificity” criteria much higher (de Olde et al., 2017).  Neither approach leads to more “effective” frameworks, though they certainly serve different purposes.   A specifically defined criteria is then critical for the long term endurance of a framework as it allows future adjustments with the ever-changing needs and definitions of sustainability (Sauvenier et al., 2006). It is also necessary to avoid assessment subjectivity (Lebacq et al., 2013). The specific priorities in developing selection criteria for LiteFarm include accessibility, stakeholder engagement, and internal decision making. Rather than contributing to the external decision-making process, such as policy decisions, as is a common objective of sustainability appraisal frameworks (Sala et Al., 2015), this research is directed towards internal decision making. The decision-maker, in the context of LiteFarm, is the smallholder farmer. It is thus necessary for each indicator to serve a functional purpose with tangible benefits; the indicators need to contribute to sustainability at the farm-level.         18 Assumptions for Developing Criteria EEach sustainability assessment framework and their respective set of indices and metrics differ according to their I) context, II) range of users, and III) purpose (Hak et al., 2012). The specific definitions of these factors for the LiteFarm framework have been defined here and were taken into consideration throughout development of the framework.  I) Context: The purpose of this report is the development of a holistic sustainability framework to be incorporated into the LiteFarm application. The LiteFarm app is a farm management tool for smallholder farmers while providing an assessment of the sustainability performance of their farm. The complexity of inputs should be minimized while background calculations or processing information within the app should be maximized. Feasibility refers to how easy incorporating and implementing the parameter in question into the LiteFarm app. This takes into account the specific context in which these parameters are designed for. This includes taking into account the limitations and needs of the LiteFarm application, LiteFarm, personnel, and lastly, the users, which in this case refers to LiteFarm’s prescribed target user, which is the smallholder farmer (as previously defined). Cost and resources as well as regional changes may present barriers to farmers. Other factors like the perceived value of an indicator to the user could also hinder collection of indicator data.  II) Range of Users: Stakeholders involved • LiteFarm personnel (backend: app formation, maintenance, and development) - the abilities and resources of this team serve as an additional limitation that needs to be taken into consideration.   • LiteFarm users: Smallholder farmers (primary), Extension officers (secondary).  III) Purpose: the purpose is to provide a sustainability assessment tool for smallholder farmers to monitor the sustainable performance of their farms and facilitate and promote more sustainable farming practices as it relates to the economics, environment, and social pillars of sustainability.   19      Source: Wohlers (2019)  20 How Criteria Can Be Applied For the purposes of our framework, our criteria was applied to the lowest level of indicator complexity possible, which is application at the parameter level rather than the entire indicator. For example, instead of applying the criteria to the indicator water, it is applied to water’s parameters like irrigation. The process of parameter evaluation can be broken into two stages: 1) selection and 2) balancing. Selection concerns the use of parameters on their own and balancing concerns how the parameters all work together. Within our criteria, there are 11 selection criteria and 9 balancing criteria. In order to be approved for use within LiteFarm, a parameter would ideally pass all 20 criteria, however criteria that affect the accessibility of the parameter have been prioritized in importance over criteria that simply affect quality of data or ability to extrapolate. Criteria that concern accessibility include criteria 8, ease of communication, criteria 9, ease of use and criteria 11, affordability of measurement. Some exceptions may be made for approving parameters which do not meet all 20 criteria points in order to better the holistic understanding of the system without compromising the universal accessibility of the framework, as this is LiteFarm’s top priority. However, a parameter should be automatically disqualified from being categorized as an approved parameter if found to fail more than 1 of the selection criteria. In order for a parameter to be evaluated the following values must first be defined (Bockstaller, 2009):  a. Reference values b. Calculation method c. Time and spatial scale d. Periodicity of calculation e. Time for implementation f. Recommendations for interpretation and similar parameters The selection criteria must be used first to evaluate the relevance of an individual parameter to the framework. These criteria can be met through the process of literature review. Parameters may be altered in methodology in order to better meet the criteria, so long as the various elements are appropriately re-weighted after altering. The Food and Agriculture Organization (FAO) of the UN’s Tool for Agroecology Performance Evaluation (TAPE) identifies 10 core indicators covering the five categories that represent the minimum requirement to provide sufficient understanding of a particular system (2019): - Governance 1) Secure land tenure - Economy 2) Productivity 3) Income 4) Added Value - Health and Nutrition 5) Exposure to pesticides 6) Dietary Diversity - Society and Culture 7) Women’s Empowerment 8) Youth Employment  Opportunity - Environment 9) Agricultural biodiversity 10) Soil Health Once parameters have been pulled that could sufficiently cover this minimum requirement, the balancing criteria can be used. The balancing criteria are meant to evaluate the overall collection of selected parameters and the robustness of the framework. The balancing criteria require much broader stakeholder engagement in order to meet the criteria.     21 Criteria for Selecting Individual Agricultural Sustainability Parameters The following are the 11 criteria used to select parameters for the LiteFarm framework. Criteria was applied on a parameter level and details for each parameter’s application can be found in Appendix A.   This collection of criteria is based heavily on the criteria developed by Moller and MacLeod (2013). These criteria were used as Moller and MacLeod found them to be the most emphasized for importance in their own literature review of sustainability monitoring (de Olde et al., 2017).  However, this too was designed with a larger scope in mind then LiteFarm, so it was adjusted in order to reflect the intended use of the LiteFarm Framework by farmers rather than by policy makers. This included removing one of the criteria (capacity to upscale) and adjusting  the specific thresholds for approval for the LiteFarm Framework as the purpose of  the LiteFarm Framework is not to support policy makers  or researchers in comparing farms, rather its primary purpose is to help inform the individual farmer.   Each criterion includes: a) definition,  b) the threshold for approval within LiteFarm, and c) the justification. The definition explains the individual criteria. The threshold for approval explores the details of how a criteria will be measured. Additionally, it explores the level of adherence necessary for each parameter to meet the criteria. If the threshold is excluded it is assumed to be  a binary decision for approval based off of  the definition. The justification explains the importance of a criteria’s inclusion and its relevance to maintaining the quality of the LiteFarm Framework.  1. Sustainability Relevance  a. Parameters must measure one of the three key pillars of sustainability: environment, economy, or society. Sustainability is specifically defined for the purposes of this project as the ability of a system to function perennially.  While other frameworks may look at specific governance and policies, that is considered too large a scale for LiteFarm.  b. Related to the environmental, economics, or social aspects of the farm.   c. Sustainability relevance affects the salience of the framework. In order for a parameter to support a larger evaluation of sustainability, it must measure data that affects the ability of a farm system to function perennially. This criteria is met within our LiteFarm Framework project by pulling parameters from other, similar agricultural frameworks that have already established the sustainable value of each parameter for us. However, any novel parameters must be evaluated for their relevance, as the inclusion of parameters in frameworks that they cannot support weakens the overall assessment. Additionally, any parameters motivated by informing government or policy makers will not be necessary for the scope of the LiteFarm project.  2. Clearly defined and standardized  a. Parameters must “be based on clearly defined, verifiable, and scientifically acceptable data collected using standardized methods so they can reliably be repeated and compared against each other” (de Olde et al., 2017) b. While not all of the testing methods utilized/recommended within LiteFarm may be the cutting edge of science or accuracy, ability to be consistent in methodology will be key to maintaining standards across evaluations and approval of a parameter. c. Criteria included due to its effect on the credibility of the framework. If the measurements  are not  standardized or clearly defined, the measurements lose their larger meaning and can no longer be used to make broader claims or interpretations about the sustainability of the system.   3. Performance rather than practice based a. Parameters must measure the actual performance of the system rather than measuring/monitoring practices that are expected to promote sustainability (Moller & MacLeod, 2013). 22 b. N/A c. Criteria included due to its effect on  the credibility of  the framework; measurements that focus on practices that promote sustainability may give a false sense of the overall sustainability of the system.  4. Sensitivity a. Parameters must be capable of detecting changes within the system on relevant time and spatial scales for decision making as well as risk management.  b. Variable depending on the parameter c. Standardizing the sensitivity of parameters allows the framework to detect trends or changes as they occur, and therefore allows problems to be addressed while there is still adequate time for correction. This is important for maintaining the credibility of the framework.  5. Quantification a. Parameters should be quantifiable, rather than qualitative, whenever feasible.  b. “Counts and continuous variables (interval and ratio scales) are more favoured than ranks (ordinal scales) or ‘yes’/no’ binary scores” (de Olde et al., 2017) c. Quantifiable measurements provide higher quality data that is more easily compared and standardized than any qualitative measurements.  The more quantifiable and therefore high-quality data can be used, the better the credibility of the overall framework.   6. Specificity for interpretability  a. Parameters should be adequately specific; only measuring one specific driver of sustainability rather than many. b. At the lowest level of complexity, an indicator should measure at most one driver c. Parameters must be interpretable on their own in addition to interpretations as a part of a larger sustainability assessment; they must not be affected by many things/multiple stressors. This criterion helps maintain the credibility of the framework by ensuring that there are not multiple correct interpretations.  7. High precision and statistical power  a. Parameters “must have sufficient precision and accuracy and sufficiently low natural variance for monitoring to detect trends and probability that some limit or threshold has been breached”  b. For the purposes of our project, the threshold is very low and high precision and statistical power is not required.  c. Criteria included due to its impact on the credibility of the framework. However, the threshold for approval is low, as high precision and statistical power is much more important to data that is collected for the purpose of comparison, rather than individual management. The smaller the scale, the lower the required precision.  8. Easily communicated and understood by LiteFarm users* a. Able to be presented within LiteFarm with sufficient explanation for comprehension by farmers.  b. Priority should be given to the easiest parameters to explain, assuming little to no academic background. Visuals should be heavily prioritized over written explanations, and simple ideas should also be prioritized over more complex system measures. “The unit used to express [parameters] influences the results and has therefore to be taken into account during interpretation” (Lebacq et al., 2013). c. This criterion affects the accessibility of a parameter. Without being able to clearly communicate each parameter and its specific purpose and requirements, stakeholder engagement will remain consistently low. A parameter that cannot be communicated or understood decreases the legitimacy of the framework (de Olde et al., 2018). 9. Ease of Use*  23 a. “reproducibility and clarity of calculations, simplicity of collecting the necessary information” (Lobietti et al., 2018).  b. Parameters should be adequately simple so as not to confuse or put any extra or unnecessary burden upon the farmer. Highest priority should be given to parameters with data inputs that  are already being collected by the farmer, followed by data that can be used across multiple parameters. c. This criterion impacts the accessibility of a parameter, additionally it is included due to its impact on the legitimacy of the framework.  10. Broad Acceptance  a. Parameters must be accepted by major stakeholders b. Accepted by  i. Farmers ii. LiteFarm Research Team (this includes us) iii. Extension officers iv. Partner Universities c. Criteria must be included in order to ensure the salience of the framework. 11. Affordable measurement* a. Having low associated additional cost relative to their existing practices  b. For the purposes of LiteFarm, a significantly higher priority should be given to parameters that can be measured without cost.  c. Having affordable measurements increases accessibility of the framework and encourages increased participation and regularity of measurements. The affordability has a large impact on the legitimacy of the framework. If all the measurements are cost prohibitive, the framework will become poorly received by the most important stakeholder, the farmers. Local benchmarking for acceptable cost should be considered.   *Criteria which affect the accessibility of The LiteFarm Framework      24 Example of Criteria Application For each parameter, a description of the parameter and an analysis table was created. The parameter was checked against all eleven criteria. These tables are all included in Appendix A.  An example evaluation is provided below for parameter B1, irrigation. Our evaluation process is primarily composed of two stages i.) scoring/data input and ii.) sustainability criteria assessment.  As background, Irrigation constitutes one of the major uses of water in agriculture (Lobietti et al., 2018). Where rainwater and natural watering sources are not sufficient, irrigation may be used to meet the needs of crop and animal production (Lobietti et al., 2018). It can be an important factor in ensuring that crops receive enough water to achieve high productivity and there are ways to manage irrigation to minimize water needs (Lobietti et al., 2018).   i. Scoring The scoring for irrigation is summarized in Table 2. This information helps quantify the exact ranges of values that a parameter might measure and their relevance to the LiteFarm framework in point values (out of 100, with 100 being most sustainable)  Table 2. Scoring of Parameter B1: Irrigation Calculations Scoring < ⅓ of land is irrigated 100 > ⅓ of land is irrigated but adaptive irrigation is present* < 25% of land has adaptive irrigation: 12.5 25 - 50% of land has adaptive irrigation: 50 > 50% of land has adaptive irrigation: 88 When does watering occur? Morning or evening: 12.5 During the day: -12.5 *Examples of adaptive irrigation include systems that have automatic timers, systems that irrigate based on rainwater, etc.  ii. Sustainability Criteria Assessment Using information about the indicator from the literature, the criteria is applied as shown in Table 3. The thought process explaining each Y/N determination is explained in the following:  Criteria 1 is met by  default by all of our parameters as they are all pulled from existing sustainability frameworks. Table 2 shows how to standardize as well as quantify this parameter, helping it meet criteria two & five. The scoring rewards specific practices and therefore passes criteria three. As irrigation practices change, scoring will also change which means this parameter also meets criteria four. Criteria 6 requires measures to be adequately specific, the breakdown of Indicators into parameters means that most parameters easily pass this criteria. Criteria seven is met since estimation of land irrigated and time of day of watering are both relatively straightforward measures. Water use is a simple concept to communicate and minimizing use of water resources is widely accepted meaning this parameter meets criteria nine and ten. Broad acceptance is a criteria where on-going consultation with extension officers may be required. Finally, there is no cost to measuring this parameter since farmers just report their current practices. Although criteria three is not fully met, this parameter was included in our accepted parameter list. Since performance is difficult to measure, the indirect measure of adaptive irrigation was viewed as adequate.     25 Table 3. Evaluation of Parameter B1: Irrigation Criteria Number: Pass? Notes: 1. Sustainability Relevance Y - Low flow conditions impact crops - Cyclones and droughts can damage crops through too much or too little water 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y - Remote sensing/farmer-self reports of land (eg. 6 out 12 100 foot rows have drip tape = 50% irrigated) - No statistical analysis required.  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y - On-going consultation 11. Affordable Measurement Y - Each farmer can measure - Cost of irrigation should provide equal or higher profits in yield   Implementation Todos:   26 Criteria for Balancing Parameters in Larger Framework This is a step beyond our project but it is important to explain that if parameters are chosen and evaluated with our above criteria, that is still not enough to definitively say that it should be included within LiteFarm’s sustainability framework. Table 4 below describes some considerations we thought were important overall to develop an effective framework.   Table 4.  Balancing criterion taken directly from de Olde et al. (2017). However, any reference to policy or government (noted with a *) should be removed for LiteFarm’s criteria as it is not meant to help larger policy but rather exclusively help the farmers and local-level stakeholders.   Criterion Description Participatory co-development [Parameter] sets and frameworks that are co-designed by  key stakeholders are more likely to be relevant, trusted, practical, heeded for use and learning  Wide scope and integration The framework and [parameter] sets must cover and cross-link multiple dimensions of sustainability and values encompassing environment, economics, social, and governance* dimensions Linked to targets/thresholds [Parameters] should be linked to realizable, action-oriented, measurable, and time-delimited targets or  critical  thresholds of risk, performance, or best professional practice Transparency and accessibility Datasets are accessible to all stakeholders (including public)*  and explain assumptions, uncertainty, and sources are more likely to be trusted and used  Policy relevant* and meaningful [Parameters] should send a clear message and provide information at an appropriate level for policy* and management decision-making  by assessing changes in the status of and risks to agricultural sustainability.  Just enough [parameter]s The fewer the [parameters], the better, provided the critical determinants of sustainability have been covered. Having just enough [parameter]s will result in more participation, improved accuracy in reporting, and clearer communication of  the overall picture to farmers, policymakers*, and the  public* Mix of generalized and specific [parameter] sets must include enough general [parameter]s to allow cross-comparison between agricultural sectors, regions, countries, and diverse social-ecological systems. However, some highly specific and locally grounded [parameters] must be included to guide fine-grained management adjustments that are especially relevant to one sector or region/country.  Balance of current and future Monitoring is part of risk management, so it must inform current options and drivers while preparing actors for future turbulence (shocks and drivers). At least some of the [parameters] and measurements should monitor potential new threats and opportunities just over the horizon Explanatory and context info  Management guidance is more focused, effective, and reliable and benchmarking is more fair if additional information is gathered to identify covariates and additional information to determine why the [parameters] change.  27 Requirements for Implementation of Balancing Criteria The balancing criteria differ from the selection criteria in that some of them require active participation by multiple stakeholders. Literature review is not sufficient to pass a parameter on these criteria, as a key part of finalizing a framework is maintaining its relevance to stakeholders. Moller & MacLeod (2013) explains that frameworks must be developed through an inclusive and collaborative process that involves all relevant stakeholders. This participatory co-development is a key step in fitting the function of a framework to the needs of the farmers, and as such should involve as many stakeholders and as much engagement as is feasible. The goal of a complete framework is to effectively communicate  and summarize sustainability information important to all stakeholders. Therefore, parameters must meet the needs of the farmers, researchers/extension officers working with them, and the LiteFarm research team, with priority given to the needs of the farmers. The practical knowledge of farmers also is often overlooked, suggesting that significant effort should go into engaging with their expertise.            Source: Wohlers (2019)28 5. Results: Indicators and Parameters Analyzed Overview The results of the application of the evaluation criteria (Section 5) to the preliminary indicator list resulted in approved, conditionally approved, and rejected parameters. In this section, parameters are summarized. Each parameter includes a short description, its value to farmers, additional notes, and whether it was approved, conditionally approved, or rejected. Details on required data inputs, scoring, and analysis for each parameter can be found in Appendix A. In addition, a meta-analysis of criteria application is presented which explores which criteria were most commonly met or rejected among parameters.   The following pages in this section describe tables that show an overview of all of our evaluated parameters (Table 5), all of our approved parameters (Table 6), all of our conditionally approved parameters (Table 7), and all of our rejected parameters (Table 8). Source: Wohlers (2019)29 Table 5. A list of indicators and parameters analyzed. Green indicates it was approved after this project’s analysis, yellow indicates conditional approval, and red indicates rejection. There should be 17 approved parameters, 19 conditionally approved parameters, and 7 rejected parameters.  Indicator A: Soil (7) Indicator B: Water (4) Indicator C: Economic Viability (5) Indicator D: Economic Risk Factors (3) Indicator E: Production Efficiency (3) Indicator F: Energy and Climate (3) Indicator G: Working Conditions (3) Indicator H: Biodiversity (4) Indicator I: Quality of Life (6) Indicator J: Health & Nutrition (2) Indicator K: Society & Culture (2) A1: Tillage: Soil Tilling and Direct Seeding B1: Irrigation C1: Available Income D1: Client Reliability E1: Cost Efficiency F1: Energy Management G1: Personnel Management H1: Biodiversity Management I1: Occupation and training J1: Exposure to Pesticides K1: Women’s Empowerment A2: Off-Season Plant Cover B2: Water Management C2: External Income Streams D2: Product Versatility E2: Crop Productivity F2: Energy Intensity G2: Working Hours H2: Ecological Infrastructures I2: Financial Situation J2: Dietary Diversity K2: Youth Employment Opportunities A3: Soil Nutrient Management (NPK) B3: Tracking Devices C3: Liquidity D3: Climate Risks & Climatic Risk Mitigation E3: Productivity F3: Greenhouse Gas Balance G3: Safety at Work H3: Intensity of Agricultural Production I3: Social Relations   A4: Soil Organic Matter (SOM) B4: Water Supply C4: Income     H4: Distribution of Ecological Infrastructure I4: Personal Freedom and Values   A5: Soil Reactivity (pH, Salinization, Acidification)  C5: Added Value     H5: Diversity of Agricultural Production I5: Health   A6: Soil Erosion        I6: Other Areas of Life   A7: Soil Compaction            Legend:  Approved  Conditionally Approved  Rejected 30 Results of Criteria Application: A complete, holistic framework has been selected through the application of criteria, followed by a comparison and supplementation using TAPE guidelines and indicators. Balancing of the framework, with particular care given to participatory co-development, should still be completed.   Approved Parameters: The following parameters are approved based on the results of our analysis. All approved parameters met almost all of our developed criteria, and passed all criteria related to the accessibility of the parameter. All fully-approved parameters fail at most 1 criteria. Please see Section 6 “Meta-Analysis of Evaluated Parameters” for a detailed overview of our analytical process, or Appendix 1 “Indicator Analysis Against Project-Developed Criteria” for a detailed analysis of each parameter.  Table 6 (below) lists out approved parameters, along with a short description of what they are, their value for farmers, and some notes from our analysis.  Table 6. All approved parameters after application of the parameter evaluation process.  Indicator/Parameter: (Code + Title) Short Description: Value for Famers: Notes (from analysis of parameters): A1: Soil Tilling and Direct Seeding Percentage of farmed land that is not overtilled/seeded with direct sowing Reduces the loss of fertility and the creation of soil pans (hard compacted soil layers below the surface) May need to convince farmers of value of low/no-till or direct sowing techniques A2: Off-Season Plant Cover Percentage of farmland that has cover crops on it during off-seasons (regionally determined) Reduces the loss of topsoil during non-productive seasons Need to determine off-seasons & cover crops by region A7: Soil Compaction Estimating how compacted soil is on farm agricultural areas Provides a measure of hard the soil is to penetrate for agricultural operations (e.g. plant roots) Need to provide pictures of soil insertion process of a testing device (e.g. small metal rod/flag) 31 B1: Irrigation Evaluates the presence and use of irrigation by farms Monitoring water usage helps farmers to ensure that crops receive enough water to achieve high levels of productivity Performance over practice-based measurement of farm practices B2: Water Management Measuring the effectiveness of farm water-saving practices  Saving water when possible helps to decrease pressure on external water sources  Performance over practice-based measurements of farm practices B4: Water Supply Parameter scores quality and quantity of water supply  Ensuring a good supply of clean water is important for all farm operations from growing plants, to raising livestock, and for human consumption. Aqueduct Water Risk Atlas required for input (https://www.wri.org/applications/aqueduct/water-risk-atlas/#/)  C3: Liquidity Measures a farm’s ability to survive off of their cash reserves.  In the case of unexpected circumstances, liquid assets are an important factor in a farm’s survival.  32 C4: Income Measures the net income produced for the operator of the farm. Available income serves sustainability as it ensures that the farm can address and survive unforeseen circumstances like a bad harvest. Additionally, income can be invested in the operation to improve yields in other manners. It is very similar to parameter C1: Available Income. However, the calculation is more straightforward than parameter C1 and will be approved instead of C1.  Requires reference to the national poverty line determined by the world bank. C5: Added Value Measures the wealth produced by the farm beyond simple crop values (e.g. jobs created, rent, or GDP).  The creation of wealth is an important factor in the long-term ability of a farm to sustain itself economically.  Requires comparison to stats on gross added value per capita from FAOSTAT (http://www.fao.org/faostat/en/#data/MK) D1: Client Reliability Measures the diversity of a farm’s clientele Selling to more clients allows the operation to continue with fewer losses if a single client stops purchasing from the farm.  D2: Product Versatility This parameter concerns the number or diversity of products made and their relative economic importance.  The diversity of crops allows for an operation to pivot in the case of crop failure in a season or changing market demands. Regional benchmarking may be required, consultation with an extension officer could illuminate a reasonable benchmark for the percent of income coming from a farm’s most important crop. 33 E1: Cost Efficiency  Measures net profits from a farm’s overall operations Profits are core to overall sustainability for farmers to keep its business running over the long-term.   F1: Energy Management Monitors the awareness and implementation of energy-saving measures and clean energy usage on farms. Proper energy management can save money and increase the reliability of energy for tasks when it is most needed. More factors can be added at farmer or extension officer recommendation. F2: Energy Intensity This parameter assesses the degree of reliance a farm has on unsustainable energy sources. The transition to sustainable energy is an important part of lowering greenhouse gas emissions and improving human health.  Embodied energy in machines and infrastructure is not considered. May have a slightly confusing title (may need further explanation for farmers) F3: Greenhouse Gas Balance Measures the greenhouse gas emissions of a farm. Some emissions are typically required in regular operations but limiting these emissions is vital to stopping climate change and creating an environmentally sustainable future. Further, the impacts of greenhouse gases on climate alter the conditions that farmers have adapted to and can negatively impact their operation. Cool Farm Tool required for input (https://coolfarmtool.org/)  34 G3: Safety at Work  Measures workplace safety related incidents and the exposure of workers to hazards at work. Protecting employees not only ensures that the farm has able-bodied workers but also improves worker satisfaction and retention.  H5: Diversity of Agricultural Production Measures the agricultural diversity of a farm across livestock species, plant species, and land use (as applicable).  Diverse agricultural production helps preserve and maintain the diversity of genetic material and helps hedge against the failure of any one crop. Regionalization and sensitivity to economic circumstances is strongly recommended J1: Exposure to pesticides Measurement of exposure to pesticides based of usage patterns and type Pesticides can impact long term health of farmers and consumers   J2: Dietary Diversity Measurement of dietary diversity based off of how many food groups out of 10 are consumed in a 24 hour period Long term health of workers impacts long term productivity of  the farm Requires surveying of farm personnel (minimally invasive)   35 Conditionally-Approved Parameters: All conditionally-approved parameters met criteria 1 (Sustainability Relevance), however the entire set of conditionally-approved parameters failed to fully meet all of the other criteria (such as feasibility, standardization, sensitivity to change, etc). Please see Section 6 “Meta-Analysis of Evaluated Parameters” for a detailed overview of our analytical process, or Appendix 1 “Indicator Analysis Against Project-Developed Criteria” for a detailed analysis of each parameter.  Table 7 (below) lists out approved parameters, along with a short description of what they are, reason(s) for conditional approval, and some notes from our analysis.  Table 7. All approved parameters after application of the parameter evaluation process.  Parameter:  (Code + Title) Short Description: Reason for Conditional Approval: Notes (from analysis of parameters): A3: Soil Nutrient Management (NPK) Soil management data collection and analysis of key nutrient variables (NPK levels) A crop-adjusted nutrient balance analysis can help farmers make good decisions on what each crop might need.  List of measurement equipment/materials of soil testing equipment Need regional standardization, creation of approved  (See indicator analysis for further details on potential avenues) A4: Soil Organic Matter How much (as a % of the soil) is composed of organic matter? Soil Organic Matter typically analyzed in labs in the developed world and can be very expensive  A5: Soil Reactivity (pH, Salinization, Acidification)  This parameter includes measures of soil pH levels as well as estimating the risk of salinization or acidification. Need to find cheap, standardized way of Measuring pH See A6 indicator analysis for potential tools that can be used 36 A6: Soil Erosion Quantification of the risks and implementations of anti-erosion techniques Remote sensing data has been identified as being not always reliable, many calculations within the parameter require remote sensing data (if risk is to be quantified).  Otherwise, it is more of a practice-based measurement which would be of limited value  C2: External Income Streams Any external sources of income that the farm is receiving as a one time payment or as on-going regular payments can help ensure the viability of the farm. External profits can ensure that there are finances available in the event of a poor growing season and provides additional finances for investment. The relevance of this parameter to LiteFarm’s demographic is uncertain. Consultation with an extension officer may be required D3: Climate Risks & Climate Risk Mitigation Climate change continues to change the ecological landscape which can impact agricultural operations.  Climatic changes can have an impact on agricultural operations. For example, in some regions, drought may be worsening. In order to ensure long-term sustainability of the farm, farmers must anticipate and work towards mitigating these increasing challenges. Again, climate risks vary regionally and specific benchmarks for certain areas may be useful to identify.  Performance not practice based 37 E2: Crop Productivity How well do a farmer’s yields compare to their local peers. A regionalized look at how a farmer is doing relative to their neighbours can give them an idea how well they are running their operations relative to their peers. Requires sufficient data on neighbouring farms in order to compare to a regional average.  G1: Personnel Management This parameter measures opportunities available to employees and other general measures of employee satisfaction. The relevance of the scoring factors is uncertain. Written payslips may not be appropriate for workers being paid a daily wage. Insurance for employees who fall sick also may not be relevant to the operations that LiteFarm is reaching.  G2: Working Hours A measure of hours worked on a weekly basis by employees Consultation with extension officers required. This parameter may not be relevant if daily rates are used or workers help for short periods of the season. Additionally, it is difficult to score and quantify.  38 H2: Ecological Infrastructures Assessment of the agricultural area that has high ecological value as a percentage of the total land value, 17% defined by UN as the appropriate  Ecologically valuable areas are important to maintain to preserve the biodiversity of local ecosystems. Having enough lands can help promote the ecosystem services that farms can benefit from.  May need regionalization based on local ecosystems H3: Agricultural Intensity Measurement of fertilization, PPP use, and stocking density The intensity of agricultural production can make it very hard for the land to maintain the species diversity needed for ecosystem services such as biological pest control, soil fertility, and crop pollination. Moderating these impacts can improve the functioning of ecosystem services. May need to be responsive to the needs of farmers to grow crops as intensively as they may need H4: Distribution of Ecological Infrastructure How spread apart are ecological infrastructures distributed over a farmer’s land (ex. hedgerows), defined as good at <50m away. May need to define special/additional refinements/regionalization based on the farmers’ individual land circumstance (land is not something every farmer can modify)  I1: Occupation and training Survey of personnel satisfaction with their  occupation and training Any survey responses would need background context questions Data may not be comparable, regional benchmarking still needed  39 I2: Financial Situation Survey of personnel satisfaction with their  financial situation Any survey responses would need background context questions Data may not be comparable, regional benchmarking still needed  I5: Health Survey of personnel satisfaction with their  health Any survey responses would need background context questions Data may not be comparable, regional benchmarking still needed  K1: Women’s Empowerment Survey of women’s empowerment based on 5 domains of women’s involvement Extensive questioning with very little useful management outcomes Need to compile a comprehensive yearly survey with every question (including other parameters) that would need to be asked of personnel.  K2: Youth Employment Opportunities Survey of youth employment opportunities based on 2 domains of youth involvement Unclear scoring and little useful management outcomes  Need to compile a comprehensive yearly survey with every question (including other parameters) that would need to be asked of personnel.    40 Rejected Parameters: All rejected parameters met criteria 1 (Sustainability Relevance) and 2 (Clear Definition & Standardization), however they  failed to fully meet any of the remaining 9 criteria. On average, rejected parameters met 6 out of the 11 criteria, suggesting that even with some adjustments these parameters would still fail to meet the sufficient criteria to meet the approval threshold. Rejected parameters as a whole failed to meet the key accessibility criteria of Ease of Communication, Ease of Use, and Affordability of Measurement   Please see Section 6 “Meta-Analysis of Evaluated Parameters” for a detailed overview of our analytical process, or Appendix 1 “Indicator Analysis Against Project-Developed Criteria” for a detailed analysis of each parameter.  Table 8 (below) lists out approved parameters, along with a short description of what they are, reason(s) for rejection, and some notes from our analysis.  Table 8. All approved parameters after application of the parameter evaluation process.  Parameter: (Code + Title) Short Description: Reason for Rejection: Notes (from analysis of parameters): B3: Tracking Devices Tracking of water usage using various technologies The measurements  seem to reward wealth over actual water usage, deducting  points for lack of resources    C1: Available Income Available income is a measure of how much capital a farm has available. Covered in a more straightforward manner by Parameter J2  E3: Productivity Measure of yield or productivity of crops Previously covered by parameter E2  H1: Biodiversity Management Tracking the planning, implementation, and monitoring of biodiversity management schemes The measurements do not appear to offer value or measure performance of how a farm is doing, rather it tracks work such as if plans are implemented and if expert consultations are being done.   41 I3: Social Relations Survey of personnel satisfaction with their  social relationships High difficulty of  cultural and regional benchmarking  See potential survey questions instead I4: Personal Freedom and Values Survey of personnel satisfaction with their personal freedom and values High difficulty of  cultural and regional benchmarking   I6: Other Areas of Life Survey of personnel satisfaction with other areas of life  High difficulty of  cultural and regional benchmarking                  Source: Wohlers (2019)  42 6. Meta-Analysis of Evaluated Parameters Upon the completion of criteria application to parameters, Figures 6-8 below were generated in order to provide a broad overview and meta-analysis of our criteria’s application.   The 11 criteria are as follows: 1. Sustainability Relevance 2. Clearly Defined & Standardized 3. Performance-Based (over Practices) 4. Sensitivity to Change,  5. Quantification, 6. Specificity for Interpretability, 7. High Precision and Statistical Power, 8. Ease of Communication/Understanding 9. Ease of Use 10. Broad Acceptance 11. Affordable Measurement. Please see Section 4 “Methodology” for a detailed description.   This analysis also provides valuable context for the priorities of the framework. It is important to note that by default, all parameters passed criteria 1)Sustainability relevance as all parameters were pulled from existing sustainability frameworks and were not novel concepts. Additionally, parameters which were removed from the framework due to an alternate, preferable parameter being found were not included in the rejected parameters analysis, as they were not rejected based on the criteria.   Source: Wohlers (2019) 43 Approved Parameters All approved parameters met criteria 1, 2, 4, 5, 6, 7, 8, 9, 11 (Figure 7 below). These contain all criteria related to the accessibility of the parameter. Additionally, each approved parameter fails at most 1 criteria.    Approved parameters passed an average of 11 out of the 11 criteria (rounded to nearest whole number) indicating that most parameters succeed in meeting all 11 criteria. The criteria which are not met but still allowed into the framework are criterion 3. Performance over practice based and 10. Broad acceptance. Parameters which were practice over performance-based (failed criterion 3) were allowed into the framework in the case of Parameters B1: Irrigation and B2: Water Management. Both parameters are scored based on the presence or absence of water related equipment and measures, however were allowed into the framework due to the universality of water usage on farms and its central importance to holistically understanding the function of a system. Parameters which failed to be “broadly accepted” were included due to the limitations in knowledge on our part as undergraduate science students. Judgements made on the broad acceptance of the parameter are more educated guesses than hard yes or no answers as the criteria application demands. It is then critical as the balancing criteria is applied that the broad acceptance of each parameter be re-evaluated by stakeholders with a knowledge base rooted in the communities, rather than the science or literature review.        Figure 6. Percentage of approved parameters against the criteria they met. A total of 19 parameters were approved. Criteria numbering is as follows: 1) Sustainability Relevance 2) Clearly Defined & Standardized 3) Performance-Based (over Practices) 4) Sensitivity to Change 5) Quantification 6) Specificity for Interpretability 7) High Precision and Statistical Power 8) Ease of Communication/Understanding 9) Ease of Use 10) Broad Acceptance 11) Affordable Measurement. 44 Conditionally-Approved Parameters All conditionally-approved parameters met criteria 1, however the entire set of conditionally-approved parameters failed to fully meet all of the other criteria (Figure 8 below).   Figure 7. Percentage of conditionally-approved parameters which met each criterion. A total of 17 parameters were conditionally-approved. Criteria numbering is as follows: 1) Sustainability Relevance 2) Clearly Defined & Standardized 3) Performance-Based (over Practices) 4) Sensitivity to Change 5) Quantification 6) Specificity for Interpretability 7) High Precision and Statistical Power 8) Ease of Communication/Understanding 9) Ease of Use 10) Broad Acceptance 11) Affordable Measurement Conditionally-approved parameters on average met 8 out of the 11 criteria (rounded to the nearest whole number). Parameters were conditionally-approved when failing more than 1 criteria with the assumption that adjustments could be made in order to allow parameters to better address the needs of LiteFarm, or because a parameter was failed due to the need for regional benchmarking and local knowledge in order to fully evaluate adherence to the criteria. conditionally-approved parameters may also be useful for LiteFarm users with additional resources or access than the base level that is assumed for criteria definition (e.g. low-income farmer with minimal additional time/material resources).   These “conditionally-approved parameters” may be considered a higher tier of parameters for those to whom the parameters are accessible. Fewer than half of the parameters met criterion 9) Ease of Use. This is significant as ease of use is a core aspect of accessibility. Parameter 10. Broad acceptance was also failed by many conditionally-approved parameters, however broad acceptance was an assumed failure of criteria in cases where it was unclear to our team without a deeper connection to local knowledge. Seven out of the seventeen parameters (I1, I2, I5, L1, L2, K1, K2) that were conditionally-approved required extensive surveying of farm personnel annually. While these parameters have an important impact on farm sustainability, they require a significant time investment in order to get that information, without providing many obvious or easily implemented farm management suggestions that would increase farm productivity. In order to increase the ease of use of these parameters, LiteFarm should compile all annual survey questions meant for farm personnel in one location to limit difficulty on the part of the farmer, and to easily allow bypassing of these parameters for farmers on to whom it would place an unnecessary burden.   45 Additionally, regional benchmarking should occur in order for survey data (e.g. for the social sustainability parameters of) and scoring to provide comparative value for individual farms against other local farms. A minimum viable product for the social Indicators would be to have everything focused down to the individual farm level. conditionally-approved parameters were also categorized as such in order to acknowledge gaps in researcher’s  knowledge. In order to maintain the standards of approved parameters, parameters for which the binary Y/N of each criteria was ambiguous or unknown, the default was rejection rather than approval. This again points to the necessity of consulting with stakeholders who have a more intimate knowledge of the communities to evaluate the accessibility of each parameter; rather strict adherence to guidelines set by stakeholders primarily with a scientific/literature-based understanding of global agriculture.   Rejected Parameters All rejected parameters met criteria 1 and 2, however failed to fully meet any of the remaining 9 criteria (Figure 9 below). On average, rejected parameters met 6 out of the 11 criteria, suggesting that even with some adjustments these parameters would still fail to meet the sufficient criteria to meet the approval threshold, and the base level of accessibility required for each parameter is too high to be considered useful to the majority of LiteFarm users. Rejected parameters as a whole failed to meet the key accessibility criteria of: Ease of Communication, Ease of Use, and Affordability of Measurement.      Figure 8. Percentage of rejected parameters which met each criterion. A total of 6 parameters were rejected. Criteria numbering is as follows: 1) Sustainability Relevance, 2) Clearly Defined & Standardized, 3) Performance-Based (over Practices), 4) Sensitivity to Change, 5) Quantification, 6) Specificity for Interpretability, 7) High Precision and Statistical Power, 8) Ease of Communication/Understanding, 9) Ease of Use, 10) Broad Acceptance, 11) Affordable Measurement.  46 7. Proposed Visual Designs in the LiteFarm app Overview The goal of the development of a Sustainability Assessment Framework for LiteFarm is the integration of elements of this framework into the app. In this section, we offer examples for how our Sustainability Assessment Framework can be implemented into LightFarm through user interface designs. This section also describes some of the research and review that went into finding inspiration for possible designs and choices we made to design a user interface for the app. Context The demographic of smallholder farms in developing countries means that design should be focused on how we can best fit these indicators into a simple and elegant user interface. Farmers can then use this interface to understand how to improve their farming practices. These indicators will then be organized into a broad sustainability dashboard for a farmer’s farming operations.    Source: Wohlers (2019) 47 User Interface Research and Inspiration: Background literature research was done on sustainability dashboard user interface design to find effective ways to communicate sustainability data. Figures 10 & 11 (below), show sustainability-related data can be tracked over time to show how an organization is doing against defined sustainability metrics.        Figure 9. A sustainability dashboard produced for Harvard’s sustainability-related operations with a bar chart of water usage (gallons) over time (years). The bottom line graph tracks the waste produced by Harvard operations in pounds/capita over time (Adapted from Shields & Shelleman, 2020 and with updated information from Harvard University, 2020). Author remarks: Unfortunately, the original source does not have bigger titles available for the charts. 48  Flora & Banerjee’s (2014) research argues that spider/radar plots are less effective than bar or line plots (Figure 11 above). Given this research, we primarily took a line and bar graph approach to displaying our metrics. We produced radar/spider plots as a secondary visualization method to maintain consistency with previous literature research on sustainability frameworks (e.g. Lobrietti et al, 2018 or Grenz et al, 2018) and to provide flexibility to the LiteFarm team. Examples are listed in Figure 12 (bar chart), Figure 13 (spider chart), and Figure 14 (radar plot) next page.   Figure 10. A set of sustainability plots produced by Flora & Banerjee (2014) to display energy use data over a given day. There are a few variations of plots ranging from radial (top), bar (middle), and line (bottom). They concluded that bar and line charts were rated more favourably in terms of general positivity (e.g. likeability) and ease of understanding than radial plots for their set of sustainability-related measurements (Flora & Banerjee, 2014). 49       Figure 11. A proposed view of accepted LiteFarm Indicators in a barchart format. Figure 12.. A proposed view of LiteFarm Indicators in a radar chart format using the same data as Figure 14 50                Figure 13. A proposed view of LiteFarm Indicators in a spider chart format using the same data as Figure 13. 51 Finally, to format our indicators/parameters’ greater integration into the app, we took a layered approach similar to other industry-available apps such as Mint (Figure 15) and Apple’s Health app (Figure 16) since LiteFarm’s current visuals are similar to these styles. These UI layouts emphasize layering and abstraction of data. Important key insight and values are presented at the “front”/top layers of the app, while underlying data is abstracted away into smaller, more detailed sections that can be revealed by interacting deeper with the app itself.       Figure 14. A look at the abstracted approach by Intuit’s mint app (Intuit, 2021). The app provides a high-level overview of a person’s financial situation to hide away (abstract) the details. Other screens provide a more detailed breakdown. Figure 15. An overview of a person’s health statistics from the front page of the Apple Health app (Apple, 2021). Detailed overviews are abstracted (hidden) from the main view to allow a user to get a sense of their most important (e.g. “Favourite”) statistics. The lower portion of the screen provides a general analysis of a person’s statistics compared to previous time periods.  52 Proposed Design for Insights Integration: One of the primary outputs of our project will be finding ways to integrate our choices of indicators/parameters into the app. To that end, the following section includes some mock-ups of how a design could look with the app. We have separated our design into three major levels, a summary view, indicator views, and parameter views. The summary view is meant to provide an at-a-glance view of farm operations. An “Indicator” refers to a general category of systems that can help provide insight into farm operations. indicators are composed of parameters and are meant to provide a high-level outlook of how a farm is doing in a general area.  A “Parameter” refers to a given way to measure farm sustainability and is also meant to provide specific insights on how a farm can improve its operations.  Data entry is enabled at each level following a similar hierarchy of data from overview, to indicator, and down to the parameter level. In the future to simplify data entry, data can be entered in a consolidated data entry screen, or through data upload (e.g. CSV spreadsheet) to help provide flexibility of data input.  We try to follow some of the existing design language of the app. The app currently uses a design that is focused on simplicity and minimalism. Lighter colours are featured where appropriate and sans-serif fonts are consistently deployed throughout the different screens of LiteFarm. We also try to use some of the same navigational motifs (ex. back buttons) to preserve consistency with the current app. We do make a distinction between white and light-gray coloured rectangles to indicate “clickable” vs. “non-clickable” rectangles (i.e. which rectangles are buttons), respectively. Work relies heavily on publicly available screenshots and efforts by the LiteFarm team as presented by Gurvi Singh (n.d.) - a former student for the project. The following figures use dummy data and are meant to be a suggestion of what the final design may look like. The captions are meant to explain the design and the choices made to display a given set of buttons and app designs (rather than scientific data itself). The top bar of each screen is provided by LiteFarm and exists right now in the current version of the app. The designs are presented categorically at the a.) Summary, b.) Indicator, and c.) Parameter levels.    53 a.)    Summary View: The summary view (Figure 17, above) is meant to provide a broad overview of a farm’s current operations. The view is clustered into two major sections, a general overview and theme-specific overviews. The general overview clusters the indicators into a few themes (< 5) and provides an at-a-glance view of how the farm is doing overall. The theme-based views are meant to a similar high-level summary but thematically clustered by our indicators. Hitting either view will direct users to deeper levels of the app so that the user can see more details. Hitting the summary view can lead to a generalized sustainability report on potential action items (Figure 18), and hitting each indicator will lead users to an Figure 16. The main entry point for the “Insights” section of the app. It provides an overview of all of the various indicators and suggested actions for the farmer along with indicators clustered by themes (e.g. “Natural Systems”). Developed in combination with some assets from LiteFarm (Singh, n.d.). 54 indicator view of how a farm is doing.   We hope this can help farmers make decisions without them needing to go deeper.  At the overview level, data entry is handled and separated by normal farmer activities and also by indicators to organize the areas that farmers can enter data (Figures 19 and 20 respectively, below). Users can switch between groupings using a dropdown menu. Actual data entry is hidden away at this level until a user goes into a given area (e.g. soil -> soil organic matter).  Selecting one of the categories leads to existing LiteFarm data entry screens where data can be drawn to update LiteFarm indicator values. Selecting one of the categories by indicator will lead to some indicator-based data screens following an overview -> indicator -> parameter hierarchy (further described in the following sections). Within the indicator grouped data screen, indicators are grouped by theme (e.g. natural systems). Figure 17. A Summary-screen listing of a potential action item that a farmer can take to improve their farm’s operations; accessible by hitting the summary box in the summary screen. Developed in combination with some assets from LiteFarm (Singh, n.d.).  55    Figure 18. Data entry grouped by activities, very similar to work already available on LiteFarm, except with a drop down menu to group/sort data entry modes. Developed in combination with some assets from LiteFarm (Singh, n.d.). Figure 19. Data entry grouped by indicators (with only water and soil available for demonstration purposes). Can be expanded to include more. Buttons are grouped by indicator themes (e.g. natural systems). Developed in combination with some assets from LiteFarm (Singh, n.d.). 56 b.)    Indicator View   At an indicator level, the views are meant to show more detail than with the high-level abstraction on the summary view (Figure 21 above). Starting at the top, there is still meant to be the at-a-glance view of operations to allow users to see how their farm is doing for a given indicator. It can also be “clickable” to reveal some further short suggestions/summarizations, similar to the full sustainability report that can be found in the summary view (Figure 22, next page).  Figure 20. A detailed view of an indicator (soil). This view provides a more detailed breakdown with quantitative farm sustainability scores and plotting of these scores. Starting from the top left, we have a similar qualitative summary with supporting quantitative metrics (units in sustainability points), followed by a bar plot of indicator levels, finally followed on the right by a clustering of parameters (data that measures this indicator). Developed in combination with some assets from LiteFarm (Singh, n.d.). 57 Under the “at-a-glance summary”, we have the parameters that make up the indicator plotted using a bar plot - a method identified as easy to use and positively received by Flora & Banerjee (2020). On the next page (i.e. scrolling down with a phone), we have the individual indicators to allow for the user to get a detailed breakdown of the parameter, categorized by parameters of concern (Good/Warning/Critical) – depending on their state. Similar to the entry page, this provides a simple organization of data, providing first a high-level overview, then some details, and some further information for a farmer, if they choose to dig deeper. The design also allows farmers to see which areas of a given indicator require action, which indicators do not and helps engage them with data-driven improvements on their farm’s operations. Figure 21. An Indicator-screen listing of a potential action item that a farmer can take to improve their farm’s operations; accessible by hitting the summary box in the indicator screen. Developed in combination with some assets from LiteFarm (Singh, n.d.).  58 On a parallel track, data entry at the indicator level follows a similar organizational structure (Figure 23 below). The data entry task is separated into different areas (e.g. soil organic matter, soil erosion, soil reactivity, etc). Selecting one of these buttons will bring the user to the actual data entry screen for a given parameter. Icons can be added to the buttons to enhance visual appeal, but may need to be customly drawn given the lack of specifically relevant logos for a given area (e.g. soil reactivity). Buttons are sorted alphabetically left to right, and top to bottom by title.       Figure 22. The soil indicator data screen, separating off data entry into various demonstration parameters for actual data entry screens (e.g. soil compaction, soil erosion, soil organic matter, soil reactivity, off-season plant cover, etc.). Developed in combination with some assets from LiteFarm (Singh, n.d.). 59 c.)    Parameter View: The final level of abstraction features the parameter view – a ground-level measurement of a farm’s operations (Figure 24 above). The top of the screen shows a historical line plot of the sustainability score of a parameter over time – a method identified as easy to use and positively received by Flora & Banerjee (2020). Below the plot is a summary of how a given parameter is doing and if there are any suggested actions to improve a farmer’s score (See Figure 25, next page for an alternative screen with an action item). Next is the current data value (i.e. original measurement) of the data so that a farmer can get a sense of the real values of a given measurement on their farm. Finally, a short scientific briefing is provided to help a farmer get a big-picture idea of what each parameter measures and how it could benefit their farm. Figure 23. A view of the parameter interface for the LiteFarm app. This screen is focused on providing details and the science behind each parameter. Developed in combination with some assets from LiteFarm (Singh, n.d.). 60  Parameter-level data entry can take a variety of forms, such as classic numerical data (Figure 26, next page) or geospatial-based data (e.g. off-season cover crop cover, Figure 27 on the following page). Additional screens can be drafted and developed depending on the needs of the data. As well, the app could provide support for data entry from other formats (e.g. CSV spreadsheets) to speed the data entry process.  Figure 24. A parameter-screen listing of a potential action item that a farmer can take to improve their farm’s operations. Developed in combination with some assets from LiteFarm (Singh, n.d.). 61    Figure 25. A quantitative data entry screen for soil organic matter with some general buttons and icons for data entry (e.g. calendar, field selection, and the actual measurement of soil organic matter values/units). Developed in combination with some assets from LiteFarm (Singh, n.d.).  62      Figure 26. A series of screens (starting clockwise from top-left) outlining how a user could enter data for crop cover. They could define custom field sizes/new fields or quantitative data for rough cover crop coverage on a field. These screens draw heavily from existing publicly available LiteFarm screenshots on how geospatial data can be entered (Singh, n.d.). 63 8. Recommendations for Future Research There are few areas of potential future work on this project. Within the whole pipeline, this project represents the initial stages of rolling out a sustainability measurement framework for the LiteFarm app. Future methodological work on these sustainability work should include some additional research in general indicator/parameter balancing, alternative areas of farm sustainability, stakeholder engagement/ground-truthing, alternative quantification methods, and research into other frameworks (e.g. TAPE). 1. General Balancing As discussed in Section 5, we have outlined some balancing criteria that can then be applied to our LiteFarm sustainability framework to ensure that it effectively and fairly measures sustainability in a holistic way. These criteria include:  a. Participatory co-development b. Wide scope and integration c. Linked to targets/thresholds d. Transparency and accessibility e. Policy relevant* and meaningful f. Just enough [parameter]s g. Mix of generalized and specific h. Balance of current and future i. Explanatory and context info (Adapted from de Olde et al, 2017).  Additional user data, field testing, and deployment analysis can illuminate if we have successfully met these criteria in developing an effective framework. This data collection and analysis can take place during or after the implementation of a test run of the LiteFarm indicator (e.g. a LiteFarm beta). 2. Alternative Areas of Farm Sustainability  Some areas that were not included in this framework included the management of wastes or deeper analyses of a farms finances. In more advanced economies, there are stricter regulations on waste management in farms, such as agricultural runoff/pollution (OECD, 2017). As well, a deeper analysis of farm agricultural revenues (e.g. if a farm has different lines of business), revenue forecasting, or integrations with reputable agribusiness news sources can help provide relevant insights for farmers to make decisions with.  3. Stakeholder Engagement & Ground-Truthing Stakeholder engagement and ground-truthing are two vital areas of future work. While this was discussed in the balancing section, prior to any deployment of the framework (e.g. beta testing or full deployment) specialized stakeholder engagement and ground truthing to ensure the effectiveness and validity of our work is necessary.  In terms of stakeholder engagement, it is necessary to have a sustainability framework that makes sense and is effective for farmers to use and apply. Contacting extension officers and some farmer focus groups and soliciting feedback on the potential implementation of the framework can be illuminative of any deficiencies in our framework.  Coming from a Canadian perspective, it is difficult to determine the feasibility of the LiteFarm framework. For example, with regard to soil health, soil testing and pH kits were identified as important tools to help a farmer understand their soil. These two items were identified as having non-zero costs associated with their implementation in the app as farmers would have to pay for these kinds of kits. Examples of both have been found in Canada for under a total of $50 CAD (Amazon.ca, 2021). It is 64 important to note that there may be significant variances in the actual quality of individual test kits/individual brands. Overall, these kinds of costs may be out of reach for some farmers, depending on their financial status (assuming these kits are even sold in target communities). Even then, some digital resources used in our parameters (e.g. accurate remote sensing data and regional trends tracked by external organizations) could also inform indicators for farmers operating in particular regions but may not always be up to date or freely licensed. 4. Quantification Simple quantification for some indicators explored throughout this report was challenging and more regional information and benchmarking should be explored to best serve farmers. Examples of this include water supply where regional water levels and average seasonal changes can inform what an adequate versus dangerously low water level may look like in different regions. 5. Alternative Frameworks The academically developed RISE, IDEA,  and TAPE frameworks were utilized as a starting point in this project. Utilizing or examining indicators from alternative sustainability assessment frameworks could further improve the LiteFarm Framework. Source: Wohlers (2019)  65 9. Conclusion The project objective was the creation of a completed sustainability assessment framework that holistically measures and tracks sustainability and supports the provision of useful management suggestions and guidance to farmers. We have come up with 17 parameters making up 9 different sustainability indicators to create a holistic sustainability assessment framework.  This framework encompasses the environmental, financial, and social sustainability of  smallholder farms. The framework was produced through the development of a two-stage evaluation criteria (1. Selection & 2. Balancing) that addressed the specific needs and restrictions of LiteFarm users, and the analysis of Preliminary Indicators and Parameters using this evaluation criteria. The development of the LiteFarm Framework will provide a road map for the integration of sustainability scoring and farm management decision aids in the LiteFarm app. The indicator scoring will provide an overall summary of an operation’s sustainability in a given area. Each parameter score can help provide guidance to farmer’s decision making. For example, parameter B1: Irrigation asks farmers to input the time of day that they water (Appendix A). Points are given if the farmer waters primarily in the early morning or evening when evaporation rates are the lowest. If a farmer has a low score for this parameter, the app could suggest they water during optimal times to limit water loss to evaporation. These types of suggestions can be produced for all parameters. Overall, the framework will empower farmers with a better understanding of the sustainability of their current operations and knowledge on how to improve their practices going forward! Limitations of this report include time and resource constraints. Additionally, there was no direct contact with farmers or extension officers during the development and completion of the report. Farmers and stakeholders may be able to provide insight into which parameters align with their values and utilize accessible data collection methods. These insights are impossible to gain through only literature review alone. 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Agricultural sustainability assessment framework integrating sustainable development goals and interlinked priorities of environmental, climate and agriculture policies. Sustainable Development, 28(6), 1702-1712. https://doi-org.ezproxy.library.ubc.ca/10.1002/sd.2118 Wohlers, A. (2019). Personal image collection.  World Commission on Environment and Development. (1987). Report of the World Commission on Environment and Development: Our Common Future Towards Sustainable Development 2. Part II. Common Challenges Population and Human Resources 4. Retrieved from http://www.un-documents.net/our-common-future.pdf      69  Appendix A: Indicator Analysis Against Project-Developed Criteria Indicator Analysis Criteria Indicators and parameters were adapted from the RISE and IDEA frameworks. The scoring system was based off of RISE and gives each parameter a score between 0 and 100. Scoring for parameters adapted from IDEA utilized a percentage system to convert them to a 0-100 scoring system. Each indicator is scored based on the average score of its parameters. Each indicator is then given equal weight in the framework. Each parameter outlined in this appendix includes the source, a description, inputs including data collection methods, scoring, and analysis.  Indicator A: Soil Parameter A1: Tillage: Soil Tilling and Direct Seeding Source: IDEA Description: This parameter quantifies the percentage of farmed land that is not overtilled or is seeded with direct sowing techniques. Tilling the soil, particularly more than 30% of the land can cause erosional losses of topsoil, destroy soil structure, and reduce biological activity (Lobietti et al., 2018). Together, these factors can cause a loss of fertility in the soil (Lobietti et al., 2018). Deep plowing can cause lower yields through over-spreading organic matter, organic matter mineralization, and a separation of the soil into two layers (worked and deep horizons/hard pans) (Lobietti et al., 2018). Some superficial tilling can be used with minimal negative effects (Lobietti et al., 2018). Additionally, direct sowing into the soil can help reduce erosional losses from land tilling (Lobietti et al., 2018). This can be done with or without cover crops or perennial crops, or even on permanent grasslands (Lobietti et al., 2018).   Input:  • The percentage of total crop surface tilled without overturning. • Percent of total crop surface with direct sowing under plant cover, without tillage, with a perennial crop, or with permanent grassland.   Data Collection Methods: Performed by the farmer once a year. The following inputs of percentage of crop surface are estimated by the farmer. 1. Total crop surface tilled without overturning 2. Total crop surface with direct sowing under plant cover 3. Total crop surface without tillage 4. Total crop surface with a perennial crop 5. Total crop surface with permanent grassland  Scoring:  Input: Percent of total crop surface tilled without overturning Scoring: 70 < 30% 0 30 - 50% 12.5 50 - 80% 25 > 80% 50 Input: Percent of total crop surface with direct sowing under plant cover, without tillage, with a perennial crop, or with permanent grassland Scoring: < 30% 0 30 - 50% 12.5 50 - 90% 25 80% 50  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y Environment 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance N • Need to convince farmers of benefits and evaluate the effectiveness of no-till methods 11. Affordable Measurement Y Likely consider under operating costs of farm 71 Implementation Todos: • Argentina - extent of tillage (are there any no or low till practices in place? What are the regional benchmarks) • No-till farming may need to be sold to farmers accustomed to tilling • Can alter weighting and scoring of low/no till vs. direct seeding  Parameter A2: Off-Season Plant Cover Source: IDEA Description: This parameter estimates the percentage of farmed land that has cover crops planted over it during off-seasons. Cover crops can help cut down on the loss of topsoil during non-productive times of the year (Lobietti et al., 2018). This helps promote future yields by  providing a physical barrier against soil losses from erosion (ex. Wind or water) and helps maintain natural soil structures (Lobietti et al., 2018).  Input: Percentage of plant cover used during off-season.  Data Collection Methods: Performed by the farmer once a year. An estimate of percentage of plant cover during off season is input into the app.  Scoring:  (Linear Scale) Input: % Plant Cover during off-season Scoring: Absence (total or partial) of plant cover on off-seasons 0 Presence of cover crops on 100% of agricultural lands 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y Environmental 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y Binary, can area weight scores 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  72 9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Define off-season by region  Parameter A3: Soil Nutrient Management (NPK) Source: RISE Description: Good soil management of key nutrients is a key component of sustainable agriculture and can help farmers make good decisions (Grenz et al., 2018). Soil analytics can give farmers a sense of what might be missing in the soil and what they can do to help improve the soil to get better yields (ex. Which type of NPK fertilizers need to be applied? Am I using too much fertilizer?) (Grenz et al., 2018).  Input: This parameter was rejected as unfeasible for all farmers to complete and no inputs, data collection methods or scoring are included. pH and NPK values of soil would be potential inputs.  Data Collection Methods: Parameter fails analysis, no data collection methods were created.  Scoring: Potential scoring could be based off: • Crop based scaling of pH, NPK ranges separated by optimal, good, acceptable, and poor, danger (i.e. crop at risk of failure).  • Assign score (out of 100) based on these ranges (e.g. 100, 75, 50, 25, 0)  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  73 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding N • Depending on farmer and awareness of nutrient breakdowns 9. Ease of Use N  10. Broad Acceptance N  11. Affordable Measurement N • Lab/scientific equipment analyses required (high cost) • Provides deep insight and analysis for farmers and what nutrients are required • Soil test kits (pH, N, P, K) can cost about $50 CAD (https://www.amazon.ca/Luster-Leaf-1663-Professional-Tests/dp/B004W6JC2U/ref=sr_1_8?dchild=1&keywords=soil+test+kit&qid=1612131576&sr=8-8)  Implementation Todos: • Find cheap and broadly available way for farmers to conduct useful soil analyses • Can do a crop-based method of comfortable ranges for plants, alternatives possible • Define soil-sampling procedure Parameter A4: Soil Organic Matter (SOM) Source: IDEA Description: Soil Organic Matter (SOM) refers to the plant or animal matter in the soil at varying degrees of decay (Grenz et al., 2018). SOM helps support nutrient levels in the soil, prevent inefficient fertilization, and to avoid greenhouse gas emissions (GHG) (Grenz et al., 2018).  Input: Lab required, parameter fails analysis. Can refer to Grenz et al (2018) for suggested methods.   Data Collection Methods: Lab required, parameter fails analysis. No data collection methods were created.  Scoring: Lab required, refer to Grenz et al (2018) for suggested scoring. Scale to 100 pt scale.   Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  74 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y • Lab required 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding N • Farmer dependent  9. Ease of Use N  10. Broad Acceptance N • Farmers with low educational backgrounds may not know about SOM 11. Affordable Measurement N • Needs specialized equipment/facilities that may not be available to every farmer Implementation To dos: • Need cheap way to analyse soil for SOM • Chose a RISE method or create custom scoring method for SOM  Parameter A5: Soil Reactivity (pH, Salinization, Acidification) Source: RISE Description: This parameter includes measures of soil pH levels as well as estimating the risk of salinization or acidification. This is important because most plants need soil pH to be around 5.5-7.0 for optimal nutrient uptake, otherwise crop productivity might decrease (Grenz et al., 2018). Below a pH of 5.0 chemical mineralization is reduced, harmful aluminum ions are more present, and alkaline nutrient availability drops(Grenz et al., 2018). At alkaline pH values above 7.0 soil biological activity and the availability of metallic nutrients falls (Grenz et al., 2018).  Input: pH value of soil  Data Collection Methods: • Various possible methods such as lab analyses, pH strips, digital meters. Can adjust based on regional availability.  Scoring: 75 Input: pH Scoring: < 2 or >12 0 2 - 5.5 or 7 - 12 50 5.5 - 7 100 > 100 kg per hectare of acidic fertilizers are used -25 *if soil liming is used the deduction for acidic fertilizers may be adjusted.  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  76 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding N  9. Ease of Use N  10. Broad Acceptance N  11. Affordable Measurement N • Not every farmer can access pH meters • Costs about $20 in Canada: (https://www.amazon.ca/Soil-pH-Tester-Kit-Required/dp/B08JJB8WNG/ref=asc_df_B08JJB8WNG/?tag=googleshopc0c-20&linkCode=df0&hvadid=459603863994&hvpos=&hvnetw=g&hvrand=11698216035529382441&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9001542&hvtargid=pla-1098945987004&psc=1) Implementation Todos: • Tiered approach? • Find cheap and broadly available methods to test soil pH. • Define soil sampling protocol  Parameter A6: Soil Erosion Source: RISE Description: Soil erosion concerns the frequency and intensity of recent erosion events (that occurred within the past 5 years). Aggregated data can be used to calculate erosion risk, including: climatic and slope gradients, soil type measurements, soil cover, and farming practices (Grenz et al., 2018). Water and wind are both major drivers of erosion which can lead to soil loss which can impact the health of the soil and ultimately the productivity of the farm in seasons yet to come (Lobietti et al., 2018).  Note: Within Grenz et al (2018), they outline potential approaches to quantify risk 77  Input: Use of anti-erosion practice.  Data Collection Methods: Further regional benchmarking is required to gain insight into relevant and quantifiable anti erosion practices.  Scoring:  Input: Anti-Erosion Practice Scoring Erosion management or anti-erosion practices to effectively mitigate identified risks (ex. Terracing, low walls, grass strips, field edges, plowing in contour lines) 100   Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding N • Might need to communicate the risks of soil erosion 9. Ease of Use N  10. Broad Acceptance Y  11. Affordable Measurement N  Implementation Todos: • Reliably update remote sensing data • Might need to communicate the risks of soil erosion • Access to historical data for slope angle • Scoring erosion is a bit unclear  78 Parameter A7: Soil Compaction Source: TAPE Description: Soil compaction can occur during a variety of agricultural activities. Natural soil pores are generally around 0.05 mm in diameter (Grenz et al., 2018). Larger pores allow for soil to be aerated, roots to spread, and promote water infiltration (Grenz et al., 2018). All of these factors help to increase yield and are at risk with high levels of compaction (Grenz et al., 2018).  Input:  • Place 30cm tall agricultural flag (rod) into soil, estimate ease of insertion (can vary based on region), such as those found on field marking flags: https://www.suttonag.com/marking_supplies.html  Data Collection Methods: Performed once a year by the farmer and input into the app.  1. A 30 cm tall wire flag is placed into the soil 2. Farmer estimates the ease of insertion 3. Regional benchmarking may be required  Scoring:  Input: Flag Insertion Scoring: Compacted soil, flag bends readily 33 Thin compacted layer, some restrictions to a penetrating wire 66 No compaction, flag can penetrate all the way into the soil 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y Measurement of many  factors that influence soil compaction. Consider breaking up into multiple parameters of a Compaction indicator  79 7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • There are more scientific interpretations, however a flag approach is the most simple • Provide pictures of flag-insertion process Indicator B: Water Water used in agricultural settings makes up the majority of freshwater used globally each year (Grenz et al., 2018). The preservation of freshwater is an important step to building a sustainable future (Grenz et al., 2018). This indicator seeks to address concerns around water conservation and quality to ensure the availability of this precious resource for future generations. Certain parameters that inform this indicator did not pass the criteria. Tracking devices failed due to the prohibitive cost of solarimeters. Additionally, use of water-saving devices during breeding failed due to ease of communication and understanding as well as difficulty setting proper quantitative parameters for this factor.  Parameter B1: Irrigation Source: RISE Description: Irrigation constitutes one of the major uses of water in agriculture (Lobietti et al., 2018). Where rainwater and natural watering sources are not sufficient, irrigation may be used to meet the needs of crop and animal production (Lobietti et al., 2018).. It can be an important factor in ensuring that crops receive enough water to achieve high productivity and there are ways to manage irrigation to minimize water needs (Lobietti et al., 2018).. High intensity use of irrigation can deplete water sources. Adapting irrigation to minimize water needed depending on crops and time of watering can ensure long term use of water sources (Lobietti et al., 2018).  Input:  • Percentage of land that is irrigated. • Percentage of land where adaptive irrigation is used. • Time of day watering occurs.  Data Collection Methods: Performed once a year by the farmer and input into the app. The following inputs are estimated: 1. Percentage of land that is irrigated 2. Percentage of land where adaptive irrigation methods are used 3. Time of day that watering typically occurs  Scoring Methodology: 80 Input: Land area calculations Scoring: < ⅓ of land is irrigated 100 > ⅓ of land is irrigated but adaptive irrigation is present* < 25% of land has adaptive irrigation: 12.5 25 - 50% of land has adaptive irrigation: 50 > 50% of land has adaptive irrigation: 88 When does watering occur? Morning or evening: 12.5 During the day: -12.5 *Examples of adaptive irrigation include systems that have automatic timers, systems that irrigate based on rain water, etc. Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y • Low flow conditions impacts crops • Cyclones and droughts can damage crops through too much or too little water 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y • Measure multiple drivers (parameter) 7. High Precision and Statistical Power Y • Remote sensing/farmer-self reports of land (eg. 6 out 12 100 foot rows have drip tape = 50% irrigated) • No statistical analysis required.  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y • On-going consultation 11. Affordable Measurement Y • Each farmer can measure 81 • Cost of irrigation should provide equal or higher profits in yield   Implementation Todos: • Define an appropriate scale for quantifying points Parameter B2: Water Management Source: RISE Description: Water management refers to water saving measures such as the capture, storage and use of rainwater which can decrease pressure on external water sources (Lobietti et al., 2018).  Input: Presence or absence of water saving measures.  Data Collection Methods:  Once a year, the farm will complete a survey on water saving methods. Additionally, they may input if there are no water saving methods, some, or many for simplicity sake.  Scoring:  Input: Water saving farming practices Scoring: Absent 0 Present But insufficient 80 Present and sufficient 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y • Low flow conditions impacts crops • Cyclones and droughts can damage crops through too much or too little water 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N regional differences -unclear quantification instructions 4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y • Measure multiple drivers (parameter) 82 7. High Precision and Statistical Power Y • Remote sensing/farmer-self reports of land (eg. 6 out 12 100 foot rows have drip tape = 50% irrigated) • No statistical analysis required.  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y • On-going consultation 11. Affordable Measurement Y • Water-saving measures may actually increase affordability Implementation Todos: • Define mathematical scales for scoring (ex. How many points to allocate for a hygienic water recycling)? • Define water-saving measures for individual farms?  Parameter B3: Tracking Devices Source: RISE Description:  Tracking of water can inform farmers on how to water most efficiently. Solarimeters were determined to be financially prohibitive and should not be considered.  Input:  • Presence of meter on water source. • Presence of hygrometer or rain gauge. • Presence of solarimeter. • Tracking of daily drainage rates.  Data Collection Methods: Once a year, the farmer will input yes or no into the app to the following questions: 1. Is there a lack of meter on the water source? 2. Is a hygrometer or rain gauge present? 3. Is a solarimeter present? 4. Are daily drainage rates tracked?  Scoring:  Input: Presence of device(s) Scoring: No meter on water source -33 Hygrometer or rain gauge present 17 Solarimeter present 50 83 Daily drainage rates tracked 33  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y • Low flow conditions impacts crops • Cyclones and droughts can damage crops through too much or too little water 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y • Measure multiple drivers (parameter) 7. High Precision and Statistical Power Y • Remote sensing/farmer-self reports of land (eg. 6 out 12 100 foot rows have drip tape = 50% irrigated) • No statistical analysis required.  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance - • On-going consultation 11. Affordable Measurement N • Parameter: solarimeter fails Implementation Todos: • Define an appropriate scale for quantifying points/scoring  Parameter B4: Water Supply Source: RISE Description: This parameter addresses the short and long term quality and quantity of the water supply. The stability of the water supply can impact the availability of water during warm/dry months (Grenz et al., 2018). Additionally, regional effects can take time to be visible on the local scale. Quality of water is also important in the yield of crops (Grenz et al., 2018).  Input: Aqueduct Water Risk Atlas Score for farm’s region. 84  Data Collection Methods: The app would access the Aqueduct Water Risk Atlas’ score depending on the farm’s region. No farmer action required.  Scoring:  Input:Aqueduct Water Risk Atlas Score Scoring: Low 100 Low-Medium 75 Medium-High 50 High 25 Extremely High 0  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y Environmental 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y -Requires comparison to the WRI Aqueduct Water Risk Atlas 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Define sources of groundwater and regional benchmarking 85 • Regional differences (sometimes farms may not be near certain sources of waters, such as farms not near underground aquifers) Indicator C: Economic Viability Economic viability addresses short and long term profits required to maintain and improve the productivity of an operation (Lobietti et al., 2018). Viability assesses disposable income available to the household (Lobietti et al., 2018). One limitation of this indicator is that it does no account for depreciation. Parameter C1: Available Income Source: IDEA Description: Available income is a measure of how much capital a farm has available. Higher available income can be used in unforeseen situations or to address costs that come up unexpectedly  or it can be re-invested in the operation (Lobietti et al., 2018). Further, it can be re-invested in the operation.  Input:  • Annual Payments • Short Term Interest • Total Income (Excluding Subsidies)  Data Collection Methods: If the app does not track the following financial inputs then, once a year, the farmer inputs values for the following inputs: 1. Annual payments (in local currency) 2. Short term interest (in local currency) 3. Total income, excluding subsidies (in local currency)  Scoring:    Input: Ratio from scoring formula Scoring: < 0.5 0 0.5 - 0.75 27 0.75 - 1 47 1 - 1.5 67 1.5 - 2 80 2 - 2.5 87 86 2.5 - 3 93 >3 100 Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y *(Annual Payments + Interest (Short Term))/ Total Income (Excluding Subsidies) 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y Background calculation completed by LiteFarm 10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Scoring  o <0.5: 0 o 0.5 - 0.75: 4 o 0.75 - 1: 7 o 1 - 1.5: 10 o 1.5 - 2: 12 o 2 - 2.5: 13 o 2.5 - 3: 14 o >3 : 15 (Source: Lobrietti et al, 2018)  Parameter C2: External Income Streams Source: IDEA Description: Any external sources of income that the farm is receiving as a one time payment or as on-going regular payments can help ensure the viability of the farm. External profits can ensure that there 87 are finances available in the event of a poor growing season and provides additional finances for investment  (Lobietti et al., 2018).  Input:  • Type and amount of external sources of income. • Percent of operation’s income that comes from external sources.  Data Collection Methods: If a farm receives external sources of income and the app does not track the following financial inputs then, once a year, the farmer inputs values for the following inputs: 1. Total amount of external sources of income (in local currency) 2. Operation’s total income (in local currency) 3. Are the payments from the external source on-going or one-time?  Scoring:  Input: Counts of external income source(s) Scoring: No external sources 0 Regular external source < 50% of operation’s income 60 Regular external source > 50% of operation’s income 100 One time external source < 50% of operation’s income 20 One time external source > 50% of operation’s income 60  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized N -Difficult to standardize as acceptable levels of external income must be regionally benchmarked  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  88 8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: No external sources:  Regular external source <50%: 3 Regular external source >50%: 5 One-time >50%: 1 One-time <50%: 3 (Source: IDEA) Argentina: Are a substantial amount of farmers receiving external sources of funding? Are they subsidies? Are they loans? Are they reliable/stable forms of income (suggestions on benchmarking)  Parameter C3: Liquidity Source: RISE Description: This is a measure of a farm’s ability to survive off of their cash reserves. In the case of unexpected circumstances this is an important factor in a farm’s survival.  Input:  • Value of cash reserves • Cost of weekly spending (including wages and operating costs)  Data Collection Methods: the app does not track the following financial inputs then, once a year, the farmer inputs values for the following inputs: 1. Value of cash reserves (in local currency) 2. Cost of weekly spending, including wages and operating costs (in local currency)  Scoring:  Value of Cash ReservesCost of Weekly Spending  Input: Number of weeks of cash reserves Scoring: 0 0 5 12.5 10 25 15 37.5 89 20 50 25 62.6 30 75 35 87.5 40 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos:   Parameter C4: Income Source: TAPE Description: A measure of net income produced for the operator of the farm. Available income serves sustainability as it ensures that the farm can address and survive unforeseen circumstances like a low yield. Additionally, income can be invested in the operation to improve yields in other manners. It is very similar to parameter C1: Available Income. However, the calculation is more straightforward than parameter C1 and will be approved instead of C1.   90 Input:  1. Gross product 2. Subsidies 3. Cost of inputs and taxes 4. Cost of hired labor and equipment 5. Loans, interest, and cost of renting land 6. Depreciation of machine and equipment  Data Collection Methods: If the app does not track the following financial inputs then, once a year, the farmer inputs values for the following inputs: 1. Gross product 2. Subsidies 3. Cost of inputs and taxes 4. Cost of hired labor and equipment 5. Loans, interest, and cost of renting land 6. Depreciation of machine and equipment  Scoring:Net Income = Gross Product + Subsidies - Cost of Inputs and Taxes - Cost of Hired Labor and Equipment - Loans, Interest, and Cost of Renting Land - Depreciation of machinery and Equipment  Input: Net income Scoring: National poverty line (defined by world bank) > Net income  0 Median income of similar operations in the region > Net Income > National poverty line (defined by world bank) 50 Net Income > Median income of similar operations in the region 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  91 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: Requires regional benchmarking through the comparison to national minimum wages determined by the world bank  Parameter C5: Added Value Source: TAPE Description: This parameter is a measure of wealth produced by the farm beyond simply the value of crops. It takes into account factors like jobs created. The creation of wealth is an important factor in the long term ability of a farm to sustain itself economically. This parameter is not addressed in the IDEA and RISE parameter analyzed and should therefore be included in the final framework.  Input:  • Net Income (See parameter C4) • Subsidies and income from rented land • Cost of hired labor • Loans interests and cost of renting land • National agricultural GDP per agricultural worker from FAOSTAT  Data Collection Methods: If the app does not track the following financial inputs then, once a year, the farmer inputs values for the following inputs: 1. Net Income (Collected according to parameter C4) 2. Subsidies and income from rented land 3. Cost of hired labor 4. Loans interests and cost of renting land In addition to these inputs, the app will collect the national agricultural GDP per agricultural worker from FAOSTAT (http://www.fao.org/faostat/en/#data/MK, no farmer action required). Scoring:  Compare gross added value (GAV) to national agricultural GDP per agricultural worker (FAOSTAT): http://www.fao.org/faostat/en/#data/MK Eg. In 2018 the GDP per capita in Argentina based on FAOSTAT was about 11 602 USD. In order to calculate this parameter for an Argentinian farm, you would calculate gross added value or (GAV) and complete the following comparison: 92 1. Gross Added Value = Net Income (Parameter J2) - Subsidies and Income from Rented Land +Cost of Hired Labour + Loans Interests and Cost of Renting Land 2. GAV11602 = Added Value of Farm  Input: Added Value of Farm Scoring: < 0.8 0 0.8 - 1.2 50 > 1.2 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: Requires comparison with FAOSTAT national gross added value per capita statistics. Alternatively if sufficient data was obtained, a comparison could be done with the median values of comparable operations in the area. 93 Indicator D: Economic Risk Factors Economic risk factors is an indicator that addresses the future financial state of an operation and its exposure level to certain measurable risks like loss of clients or susceptibility to a poor growing season (Lobietti et al., 2018). Parameter D1: Client Reliability Source: IDEA Description: This parameter looks at the diversity of clients that a farm sells to since more clients will allow the operation to continue with fewer losses if a client stops purchasing from the farm  (Lobietti et al., 2018).  Input:  • Percent of income from the most important client. • Farmer’s estimation of the reliability of the most important client.  Data Collection Methods: Performed by the farmer once a year. An estimate of the following inputs is put into the app: 1. Percent of income from most important client 2. A sliding scale from 0-60 of their most important client (with 60 being most reliable)  Scoring:  Input: Client information Scoring: Percent of income from most important client < 25 % 40 25 - 50% 20 > 50 % 0 Reliability of most important client Sliding scale from 0 - 60 based on farmer's perception  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y -Percent of income from most important client (<25%: 2 94 25-50%: 1 >50%: 0) -Estimation of reliability of marketing channels (0-3 points) 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Argentina Question - what is a reasonable benchmark for percentage of income from most important client  Parameter D2: Product Versatility Source: IDEA Description: This parameter concerns the number or diversity of products made and their relative economic importance to the operation. Diversity of crops allows for an operation to pivot in the case of a failure of a crop in one season or changing market demands (Lobietti et al., 2018).  Input: Percent of income from the most important crop.  Data Collection Methods: If the app does not collect the following income data then, once a year, the farmer inputs the following value into the app: 1. Percent of income from the most important crop  Scoring: Input: Percent of income from most important crops Scoring: < 25% 100 25 - 50% 60 50 - 80 % 40 > 80 % 0  Analysis: Criteria Number: Pass? Notes: 95 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y • Income from most important crops 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Argentina Question: What is a reasonable benchmark for the percentage of income from the most important crop(s)  Parameter D3: Climate Risks & Climatic Risk Mitigation Source: IDEA Description: Climate change continues to change the ecological landscape which can impact agricultural operations (Lobietti et al., 2018). Climatic changes can have an impact on agricultural operations (Lobietti et al., 2018). For example, in some regions, drought may be worsening. In order to ensure long-term sustainability of the farm, farmers must anticipate and work towards mitigating these increasing challenges.  Input: Measures taken to lower climate risks.  Data Collection Methods: Further regional benchmarking is required to gain insight into relevant and quantifiable measures that can be taken to lower climate risks.  Scoring: Input: Measure(s) taken to lower climate risks Scoring: No measures 0 Some measures 33 96 Many measures 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification N • Measures taken to lower  climate risks is not quantitative 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Regional benchmarking of climate risks and strategies to avoid them  Indicator E: Production Efficiency Production efficiency is an indicator that investigates all resources required to achieve a product (Lobietti et al., 2018). It is dependent on a value system which can be difficult to quantify (Lobietti et al., 2018). Cultural norms may value things like time, quality of life, and the preservation of the environment differently (Lobietti et al., 2018). This indicator approaches production efficiency from an economic perspective and focuses on building financial autonomy.  Parameter E1: Cost Efficiency Source: IDEA Description: A measure that describes the profits associated with the costs of producing an item. The amount of time and economic inputs to a product should correlate to the profits they generate.  Input:  97 • Value of products • Value of inputs • Product value  Data Collection Methods: If the app does not track the following financial inputs then, once a year, the farmer inputs values for the following inputs: 1. Value of products (in local currency) 2. Value of inputs like labor (in local currency) 3. Product value (in local currency)  Scoring:  (Value of Products - Value of Inputs)Product Value* 100 = Cost Efficiency  Input: Cost efficiency from formula Scoring: < 10% 0 10 - 20% 12 20 - 30% 24 30 - 40% 36 40 - 50% 48 50 - 60% 60 60 - 70% 72 70 - 80% 84 80 - 90% 96 90 - 100% 100  Analysis: Criteria Number: Pass? Notes: 98 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y • (Value of Products - Value of Inputs)/(Product Value) * 100 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y • Calculation done by LiteFarm in the background 10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos Decide on standardized scoring system <10%: 0 10 - 20%: 3 20 - 30%: 6 30 - 40%: 9 40 - 50%: 12 50 - 60%: 15 60 - 70%: 18 70 - 80%: 21 80 - 90%: 24 90 - 100%: 25 (Source: IDEA)  Parameter E2: Crop Productivity Source: RISE Description: This parameter outlines a local and regional measure of crop productivity. Crop productivity is at the core of a farmer’s work and is essential to the validity of the business. Regional comparisons can help a farmer understand how their field might be doing in relation to the fields of their local area and to help them make decisions on if they need to act to improve their operations (Grenz et al., 2018). 99  Input:  • Crop yield of farm per hectare • Regional average crop yield per hectare  Data Collection Methods: This parameter is not standardized and relies on regional averages. It may take time before LiteFarm has sufficient clients to create regional averages of crop productivity. If LiteFarm has enough data to calculate regional average crop yield per hectare then this parameter may be used. The regional average crop yield would be gathered from other LiteFarm client data, no farmer input required. In addition, if the app does not collect the following data point then, once a year, the farmer estimates the following input: 1. Crop yield of farm per hectare  Scoring:   Input: Yield quality Scoring: No Yield 0 Low Yield 34 Average Yield 67 High Yield 100 Quality  +20  *Further regional benchmarking may be required. These yields should be based upon regional averages, linear interpolation for gaps, and area weighting average to get final score.  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized N This parameter is not standardized and relies on regional averages. It may take time before LiteFarm has sufficient clients to create regional averages of crop productivity. 3. Performance-Based (over Practices) Y  4. Sensitivity to Change N May not be widely available data to to which compare farms 5. Quantification Y  100 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • LiteFarm’s database of crops/crop measurements  • Update yield database over time  • Can be placed in operations  Parameter E3: Productivity Source: TAPE Description: A measure of value produced per hectare of land. This parameter is addressed by parameter E2: Crop Productivity and was not analyzed. Indicator F: Energy and Climate Energy and climate are important components of a farm’s sustainability. The use of energy intensive practices can be expensive so proper energy management is a factor in the economic sustainability of a farm (Grenz et al., 2018). Furthermore, depending on the primary energy source, energy use can contribute to environmental concerns by contributing to greenhouse gas emissions (Grenz et al., 2018). Parameter F1: Energy Management Source: RISE Description: This parameter investigates the awareness and implementation of energy saving measures and clean energy usage on farms.  Input:  • Presence of monitoring system for energy use • Use of renewable energy • Use of energy-saving strategies  Data Collection Methods: Performed once a year by the farmer and input into the app. The farmer would answer yes or no to  the following questions. Further benchmarking or research in more quantifiable measures is required. 1. Is energy use monitored? 101 2. Are renewable sources of energy used? 3. Are energy-saving strategies employed?  Scoring: Input: Energy-saving measure(s) Scoring: Energy use is monitored 33 Renewable energy is used (may be produced on or off site) 33 Energy-saving strategies are implemented 34  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y • More qualitative than quantitative 6. Specificity for Interpretability Y • Not super specific 7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos   Parameter F2: Energy Intensity Source: RISE Description: This parameter assesses the degree of reliance a farm has on unsustainable energy sources. Embodied energy in machines and infrastructure is not considered.  Input:  • Energy Consumption (MJ/ha) 102 • Percentage of renewable energy  Data Collection Methods: If the app does not track these energy inputs then, once a year the farmer estimates the following inputs: 1. Energy consumption (in MJ/ha) 2. Percentage of total energy that is from a  renewable energy source  Scoring: Two inputs are needed, the percentage of renewable energy and the energy consumption per hectare. Using these two values, the score can be read from the table below.  Percent of renewable energy Energy Consumption (MJ/ha) Scoring 0-29 <2500 100 2500 - 5000 95 5000 - 10 000 75 10 000 - 15 000 20 15 000 - 20 000 4 >20 000 0 30-59 <2500 100 2500 - 5000 95 5000 - 10 000 80 10 000 - 15 000 30 15 000 - 20 000 10 20 000 - 25 000 5 >25 000 0 60-89 <5000 100 5000 - 10 000 95 10 000 - 15 000 85 15 000 - 20 000 30 20 000 - 25 000 10 25 000 - 30 000 5 103 >30 000 0 90-100 <10 000 100 10 000 - 15 000 95 15 000 - 20 000 80 20 000 - 25 000 35 25 000 - 30 000 10 >35 000 0  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos   Parameter F3: Greenhouse Gas Balance Source: RISE Description: The catastrophic impacts of rising greenhouse gas emissions is well documented. This parameter addresses the contributions of an agricultural operation to this issue. Some emissions are typically required in regular operations but limiting these emissions is vital to creating an 104 environmentally sustainable future. Further, the impacts of greenhouse gases on climate alter the conditions that farmers have adapted to and can negatively impact their operation.  Input: CO2 equivalent per hectare in tonnes. Can be found using the Cool Farm Tool.  Data Collection Methods: Performed once a year by the farmer. LiteFarm can potentially integrate the Cool Farm Tool’s survey for an estimate of  CO2 equivalent per hectare. This may need to be further vetted by the team prior to integration. A first look into the Cool Farm Tool shows the following required data inputs: 1. Harvested yield and marketable yield product weights 2. Growing area 3. Fertiliser applications: type and rate 4. Number of pesticide applications 5. Energy use (kWh and fuel use) 6. Optionally: transport: mode, weight of product and distance 7. Herd or flock size 8. Feed 9. Manure management 10.  Energy use (kWh and fuel use) 11.  Transport of feed and other inputs  Scoring: The Cool Farm Tool allows farmers to get a quick estimate of their greenhouse gas usage using easily accessible data. Values taken from the Cool Farm Tool can be compared to the scoring below.  Input: CO2 equivalent per hectare (tonnes) Scoring: > 5 0 2.5 50 2.0 67 1.1 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  105 5. Quantification Y • Farmers must use the cool farm tool to get an estimate of CO2 emissions 6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos   Indicator G: Working Conditions Poor working conditions can lead to employees requiring time off or difficulty in employee retention (Grenz et al., 2018). Ensuring safe and healthy working conditions is both ethically important as well as important for lowering costs of hiring (Grenz et al., 2018). Parameter G1: Personnel Management Source: RISE Description: Employee satisfaction is an important component of a well-run farm. Retention of workers can improve the quantity and quality of work being completed and lowers costs of training and hiring new workers.  Inputs: • Survey questions  Data Collection Methods: Once a year the farmer answers the following questions. Points are given if the answers are ‘yes’.  This is a parameter where regional benchmarking and research into more quantifiable measures would be useful. 1. Are there adequate workers employed to complete all tasks required? 2. Are written employment contracts and pay stubs supplied? 3. Does protection against wrongful termination exist? 4. Does an apprenticeship program exist? (In a family-run context this could be the training of a family member) 5. Do workers have the ability to form labour unions? 6. Do incentive systems for workers exist? 7. Does a form of insurance for employees who fall ill or are injured exist?  106 Scoring: Input: Survey Question Scoring: Notes: Adequate workers are employed to complete all tasks required 15  Written employment contracts and pay stubs are supplied 15 This question may not apply if most workers are short-term workers for harvesting Protection against wrongful termination exists 14  An apprenticeship program exists 14  Workers have the ability to form labour unions 14 May be irrelevant in a family-run context Incentive systems (eg. through pay) exist 14  A form of insurance for employees who fall ill or are injured exists 14   Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification N  6. Specificity for Interpretability N Not all factors relevant to the questions are within farmer control, would need to be adapted for LiteFarms needs 7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  107 9. Ease of Use N  10. Broad Acceptance N Relevance of questions needs to be confirmed through stakeholder engagement 11. Affordable Measurement Y  Implementation Todos   Parameter G2: Working Hours Source: RISE Description: Related to Personnel Management (Parameter G1), tracking working hours ensures that workers are receiving adequate hours while also maintaining a decent work-life balance. This parameter informs the sustainability of the farm by again measuring a component of employee satisfaction and emphasizing the importance of employee retention. It could be beneficial to include measures of vacation time or sick leave in the future.  Inputs:  • Average hours per week of full time workers • A system for reimbursing overtime exists (yes/no)  Data Collection Methods: This parameter is difficult to quantify and will likely vary according to regional contexts. Once a year, the farmer could input the following values: 1. Average hours worker each week by full time employees 2. Does a system for reimbursing overtime exist?  Scoring: Average hours per week of full time workers Scoring: >30 or <50 0 30-39 40 49-60 40 40-48* 80 * Needs regionalization/adjustment for sowing/harvest season Input: Factor Scoring: A system for reimbursing overtime exists 20  Analysis: Criteria Number: Pass? Notes: 108 1. Sustainability Relevance Y  2. Clearly Defined & Standardized N  Need for regional benchmarking to make scoring appropriate  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance N Cultural relevance needs to be addressed through stakeholder engagement and understanding of cultural work standards 11. Affordable Measurement Y  Implementation Todos   Parameter G3: Safety at Work Source: RISE Description: Agricultural work can be dangerous. Both incidents and long-term exposure to pesticides are of concern when considering the safety of workers. Protecting workers is important not only from an ethical perspective but also ensures that the farm continues to have able-bodied workers who can take on the tasks required to keep the operation running.  Inputs: • Time since last work-related accident (in years) • High toxicity plant protection products (e.g. LD50 < 500 mg/kg) are used (yes/no) • A safety strategy exists (yes/no)  Data Collection Methods: Once a year the farmer answers the following questions: 1. How many years have passed since the last work-related accident? 2. Are high toxicity plant protection products (with an LD50 value of less than 500 mg/kg) used? (yes/no) - this question should be answered by checking packaging of products used. 3. Does a safety strategy exist? (yes/no) 109  Scoring: The score for this parameter can be found by adding scores from the following two tables. Input: Time since last work-related accident (in years) Scoring:  0 0 1 15 2 30 3 45 4 60 5 75  Input: Safety factor Scoring: High toxicity plant protection products are used (LD50 < 500 mg/kg)*, ** -20 A safety strategy exists, ** 25 *Source: Government of British Columbia (), in the case that synthetic pesticides or plant protection products are used, the toxicity should be listed on the product and can be input into LiteFarm by the farmer. (https://www2.gov.bc.ca/assets/gov/farming-natural-resources-and-industry/agriculture-and-seafood/animal-and-crops/plant-health/pesticide-toxicity-hazard.pdf) ** Dependent on regional regulations/safety standards  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  110 8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos • Regionalization of safety protocols   Indicator H: Biodiversity The diversity of living things on a farm helps promote the long-term resilience and productivity of farms. Services provided by diverse ecosystems can (among other functions) help regulate water levels, maintain good nutrient levels, moderate gas balances, promote pollination, and help soil formation (Grenz et al, 2018). It can also help make sure a fam is able to resist environmental changes such as climatic or parasitic hazards (Lobietti et al., 2018).  Alternative Measurement Methodology: Cool Farm Tool Parameter H1: Biodiversity Management Source: RISE Description: Farms who are aware, seek expert consultation, implementing, and monitoring biodiversity promoting activities are farms that are better able to preserve the biodiversity of their farms.  Farmers can then take better advantage of the various benefits of a biodiverse farm that results from their conservational work (Grenz et al, 2018).  Input:  • Farmer knowledge of farming practices, updated regularly annually (or as required) • Bio-Suisse biodiversity measures table (https://www.bio-suisse.ch/media/en/pdf2012/rl_2012_e.pdf)  o Alternative: Use area-weighted scale based on type of farmland and biodiversity measures used (Grenz et al, 2018, p. 59) Data Collection Methods: Currently a rejected parameter, more research on Bio-Suisse data requirements could be done if it is included later on in LiteFarm’s future.  Scoring: Input: Number of Bio-Suisse measures Scoring: 0 0 - Very Negative 6 33 - Negative 9 50 - Average 111 12 67 - Positive 17 100 - Optimal Source: Grenz et al, 2018, p. 60  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N Not strictly a performance measure, just if “practices are being followed” 4. Sensitivity to Change Y As long as farmer regularly follows through on sustainability practices 5. Quantification N Not specifically quantifiable, numbers of measures used as a table.  6. Specificity for Interpretability N Easy to confuse people as this is an aggregation of many potential actions 7. High Precision and Statistical Power N  8. Ease of Communication/Understanding Y  9. Ease of Use N  10. Broad Acceptance Y It should be relatively obvious what activities are/are not promoting biodiversity 11. Affordable Measurement Y  Implementation Todos:  • Determine localized list of biodiversity promoting practices  o What farmers may already do  o What species live in a geography and which practices might help them best  Parameter H2: Ecological Infrastructures Source: RISE/TAPE Description: Ecologically valuable areas are important to maintain to preserve the biodiversity of local ecosystems. Having enough lands can help promote the ecosystem services that farms can benefit from (Grenz et al, 2018). 112  Input:  • Assessment of the agricultural area that has high ecological value as a percentage of the total land value, 17% defined by UN as the appropriate land area that should be managed for nature with regional variation possible (Grenz et al, 2018)  Data Collection Methods:  • Once a year the farmer marks which areas are ecologically valuable (e.g. in the farm’s map) • Requires regionalization on what would be considered to be “ecologically valuable”  Scoring: (Linear Scale) Input: % of Agricultural Area with High Biodiversity Potential Scoring: 0% 0 17% 100 Source: Grenz et al, 2018, p. 60  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability N  7. High Precision and Statistical Power Y Depends on exactly how farmers measure it (can be standardized with the app so that farmers can plot sustainability areas easier) 8. Ease of Communication/Understanding N  9. Ease of Use Y  10. Broad Acceptance Y  113 11. Affordable Measurement Y  Implementation Todos:  • Define high biodiversity areas, per localized area  • Define threshold, per localized area  Parameter H3: Intensity of Agricultural Production Source: RISE Description:  The intensity of agricultural production can make it very hard for the land to maintain the species diversity needed for ecosystem services such as biological pest control, soil fertility, and crop pollination. Moderating these impacts can improve the functioning of ecosystem services. Activities such as nitrogen fertilizer use can change the ecosystem balance so that very fast growing species dominate over others (Grenz et al, 2018).   Input:  • Fertilization, PPP use, and stocking density(Grenz et al, 2018)  Scoring: Final Score is an average of three components (N fertilization, pesticide/plant protection product use, livestock units). Input: N fertilization intensity (per ha agricultural area) Scoring: 0 kg N per ha agricultural area 100 1-100 kg N per ha agricultural area (linear scale) 99-35 100-300 kg N per ha agricultural area (linear scale) 35 - 0 Source: Grenz et al, 2018, p. 63; exact formulas and charts available  Input: Number of Pesticides/Plant Protection Products applications per unit area (Linear Scale, with room for regional adjustment) Scoring: 3+ 0 0 100 Source: Grenz et al, 2018, p. 63  Input: Livestock units per ha  (Linear Scale, with room for regional adjustment) Scoring: 114 3+ 0 0-1 100 Source: Grenz et al, 2018, p. 64 Livestock Units Conversion: https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Livestock_unit_(LSU)   Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability N Regional benchmarking needed 7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance N A potential for concern is that we could be unfairly penalizing farmers for using farming methods that they have no alternatives (ex. Pest swarms)  11. Affordable Measurement Y  Implementation Todos:  • Regionalization for livestock intensity or plant protection product use  Parameter H4: Distribution of Ecological Infrastructure Source: RISE Description: Having ecologically valuable structures can help preserve important habitats for local species and help provide natural pest control. With well-distributed structures, it can allow animal 115 species from one spot to another. These can take the form of hedgerows, trees, rock piles, or more depending on the local farm (Grenz et al, 2018).  Input:  • The “interconnectedness” of ecologically valuable structures and their evolution over the last 10 years(Grenz et al, 2018)  Scoring: (Linear Scale) Input: % of land in close proximity to ecologically valuable structures (< 50m away) Scoring: 0% 0 100% 80  Input: Trend changes (over last 10 years) Scoring: Negative Trend 0 No Change 10 Positive Trend 20 Source: Grenz et al, 2018, p. 65  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change N  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  116 9. Ease of Use N The change component may or not be useful as it may not be attainable (ex. Farmers cannot add more ecologically valuable lands) 10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos:  • Define ecologically important structures (depending on locale)  Parameter H5: Diversity of Agricultural Production Source: RISE Description: Diverse agricultural production helps preserve and maintain the diversity of genetic material and helps hedge against the failure of any one crop. Modern science has distilled agriculture down to only a few high-performance breeds that can make it hard for farmers to respond to disease threats and changing environmental conditions. Bees can help pollinate crops and wild plants to help improve the yields of a farm’s crops while providing useful outputs (e.g. honey or beeswax)  (Grenz et al, 2018, Lobrietti et al, 2018).   Input:  • Knowledge of farm operations on livestock species counts, plant species counts, and land use of agricultural areas (Grenz et al, 2018)  Scoring: Input: Species Diversity of Livestock Scoring: • Only 1 species of livestock breed • No bees 0 • 3 different breeds (1 rare breed) • Bees on farm 50 • 6 Different livestock breeds (3 rare and/or old breeds)* • Bees on farm**  100 Source: Grenz et al (2018, p. 66) * Assign livestock breeds a maximal weighting of 90 points ** We can arbitrarily assign bees of 10 points, can be changed  Input: Species Diversity of Plants Scoring:  (# plant species) / (total agricultural area, ha) * 100 X pts from formula Source: Grenz et al (2018, p. 68) ***Farms with > 10 ha of agricultural area earn 100 points if they have 10 or more species. 117 • Area weighted land-use scoring:  Source: Grenz et al (2018, p. 67) Scoring Cont’d: 118 Input: Diversity Indices (Livestock, Plants, Land Use Output: Scoring (out of 100) • Average of diversity scores of livestock species, plant species, and land use, as applicable.  0-100  Analysis:  Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y It lumps together a bunch of different ways of measuring farm biodiversity.  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos:  • Regionalization of bee scoring (ex. Are bees even beneficial in a given locale) • Regionalization of specific scores assigned for livestock, plant, and land-use diversity to account for local differences • Sensitivity to economic circumstances Indicator I: Quality of Life  The quality of life indicator measures farm personnel satisfaction with various aspects of their lives through a series of survey questions (Grenz et al., 2018). It is relevant to the long-term sustainability of the system as these factors may impact worker retention as well as dedication.  119 Parameter I1: Occupation and training  Source: RISE Description: This parameter quantifies the level of satisfaction of farm personnel with their occupation and any training of which they are the recipient (Grenz et al., 2018).  Satisfaction with occupation should be reflective of overall satisfaction, taking into account types of work (on-farm, sideline activities, household work etc) as well as working hours, workload, relationships with other personnel and management as well as customers  (Grenz et al., 2018) Satisfaction with training as it relates to duration, type, and level of training as well as satisfaction with any ongoing training options (Grenz et al., 2018).    Input: It is implemented through interviews with personnel in which they are asked to self-evaluate their satisfaction levels on a scale from 0 to 100.   Data Collection Methods: Once a year the farmer or extension officer would ask all employees to self-evaluate their personal satisfaction in their day-to-day work and training on a scale from 0 to 100. An average of scores would be put into the app or all scores would be entered and the app would calculate an average.  Scoring:  Input: Average level of satisfaction Scoring: Not at all satisfied with current occupation and training 0 Moderately satisfied with current occupation and training 50 Very satisfied with current occupation and training 100   Analysis: Criteria Number:  Pass? Notes: 1. Sustainability Relevance Y Social 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change N Depending on the regularity of interviews (annual?) 5. Quantification Y  6. Specificity for Interpretability N Covers a large range of variables in one parameter 7. High Precision and Statistical Power N Highly  variable sense of “satisfaction” 8. Ease of Communication/Understanding Y  120 9. Ease of Use N  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Regional/cultural benchmarking for average satisfaction with job  Parameter I2: Financial Situation Source: RISE Description: This parameter quantifies the level of satisfaction of farm personnel with their financial situation (Grenz et al., 2018). Satisfaction with financial situations should be reflective of personnels access to resources, participation and involvement in economic life and autonomy in life and work unrestricted by financial restrictions (Grenz et al., 2018).   Input: It is implemented through interviews with personnel in which they are asked to self-evaluate their satisfaction levels on a scale from 0 to 100.   Data Collection Methods: Once a year the farmer or extension officer would ask all employees to self-evaluate their personal satisfaction in their financial situation on a scale from 0 to 100. An average of scores would be put into the app or all scores would be entered and the app would take an average.  Scoring:  Level of Satisfaction Scoring: Not at all satisfied with financial situation 0 - 50 Very satisfied with financial situation 100   Analysis: Criteria Number:  Pass? Notes: 1. Sustainability Relevance Y Social 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change N Depending on the regularity of interviews (annual?) 5. Quantification Y  121 6. Specificity for Interpretability Y  7. High Precision and Statistical Power N Highly  variable sense of “satisfaction” 8. Ease of Communication/Understanding Y  9. Ease of Use N  10. Broad Acceptance N The connection between workers satisfaction and sustainability may  take some explanation.  11. Affordable Measurement Y  Implementation Todos: • Regional/cultural benchmarking for average satisfaction with financial situations, what is “normal” for workers on a farm?   Parameter I3: Social Relations  Source: RISE Description: This parameter quantifies the level of satisfaction of farm personnel with their social relations (Grenz et al., 2018). Satisfaction with social relations should be reflective of satisfaction with one’s family situation as well as their social environment (Grenz et al., 2018).   Input: It is implemented through interviews with personnel in which they are asked to self-evaluate their satisfaction levels on a scale from 0 to 100.   Data Collection Methods: Once a year the farmer or extension officer would ask all employees to self-evaluate their personal satisfaction in their relationships both personal and in the workplace on a scale from 0 to 100. An average of scores would be put into the app or all scores would be entered and the app would take an average.  Scoring:  Input: Level of Satisfaction Scoring: Not at all satisfied with social relations 0 Moderately satisfied with social relations 50 Very satisfied with social relations 100   Analysis: Criteria Number:  Pass? Notes: 1. Sustainability Relevance Y Social 122 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change N  5. Quantification Y  6. Specificity for Interpretability N  7. High Precision and Statistical Power N Highly variable sense of “satisfaction” 8. Ease of Communication/Understanding N  9. Ease of Use N  10. Broad Acceptance Y/N The connection between workers satisfaction and sustainability may  take some explanation.  11. Affordable Measurement Y  Implementation Todos: • Regional/cultural benchmarking for average satisfaction with social relations, what is “normal” for workers on a farm?  • Machismo in this category would need to be addressed - not LiteFarm’s Place   Parameter I4: Personal Freedom and Values Source: RISE Description: This parameter quantifies the level of satisfaction of farm personnel with their personal freedoms and values (Grenz et al., 2018). Satisfaction with personal freedom and values should be reflective of satisfaction with one’s ability to live as they wish in regards to politics, economics, as well as activities, religious, and cultural practice (Grenz et al., 2018).   Input: It is implemented through interviews with personnel in which they are asked to self-evaluate their satisfaction levels on a scale from 0 to 100.   Data Collection Methods: Once a year the farmer or extension officer would ask all employees to self-evaluate their personal satisfaction in their perceived freedom in areas including religion, expression, and politics on a scale from 0 to 100. An average of scores would be put into the app or all scores would be entered and the app would take an average.  Scoring:  Input: Level of satisfaction Scoring: 123 Not at all satisfied with personal freedoms and values 0 Moderately satisfied with personal freedoms and values 50 Very satisfied with personal freedoms and values  100  Source: Grenz et al (2018)  Analysis: Criteria Number:  Pass? Notes: 1. Sustainability Relevance Y Social 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change N  5. Quantification Y  6. Specificity for Interpretability N  7. High Precision and Statistical Power N Highly variable sense of “satisfaction” 8. Ease of Communication/Understanding N  9. Ease of Use N  10. Broad Acceptance Y/N The connection between workers satisfaction and sustainability may  take some explanation.  11. Affordable Measurement Y  Implementation Todos: • Regional/cultural benchmarking for average satisfaction with personal freedoms and values, what is “normal” for workers on a farm?  • Gender, Ethnic, racial, and national identity needed for this to give a full picture?   Parameter I5: Health Source: RISE Description: This parameter quantifies the level of satisfaction of farm personnel with their health (Grenz et al., 2018). Satisfaction with health should be reflective of satisfaction with one’s physical and mental health as well as time management (Grenz et al., 2018).   124 Input: Implemented through interviews with personnel in which they are asked to self-evaluate their satisfaction levels on a scale from 0 to 100.   Data Collection Methods: Once a year the farmer or extension officer would ask all employees to self-evaluate their personal satisfaction in regards to their health on a scale from 0 to 100. An average of scores would be put into the app or all scores would be entered and the app would take an average.  Scoring:  Input: Level of satisfaction Scoring: Not at all satisfied with personal health 0 Moderately satisfied with personal health 50 Very satisfied with personal health 100  Source: Grenz et al (2018)  Analysis: Criteria Number: I5 Pass? Notes: 1. Sustainability Relevance Y Social 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power N Highly variable sense of “satisfaction” 8. Ease of Communication/Understanding Y  9. Ease of Use N  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • Regional/cultural benchmarking for average satisfaction with health, what is “normal” for workers on a farm?   125 Parameter I6: Other Areas of Life  Source: RISE Description: This parameter quantifies the level of satisfaction of farm personnel within other areas of life(Grenz et al., 2018). Satisfaction with other areas of life  should be reflective of satisfaction with one’s physical and mental health as well as time management (Grenz et al., 2018).   Input: Implemented through interviews with personnel in which they are asked to self-evaluate their satisfaction levels on a scale from 0 to 100.   Data Collection Methods: Once a year the farmer or extension officer would ask all employees to self-evaluate their personal satisfaction on a scale from 0 to 100. An average of scores would be put into the app or all scores would be entered and the app would take an average.  Scoring:  Input: Level of satisfaction Scoring: Not at all satisfied with other areas of life 0 Moderately satisfied with other areas of life 50 Very satisfied with other areas of life 100  Source: Grenz et al (2018)  Analysis: Criteria Number: I6 Pass? Notes: 1. Sustainability Relevance Y Social 2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability N  7. High Precision and Statistical Power N Highly variable sense of “satisfaction” 8. Ease of Communication/Understanding N  9. Ease of Use N  10. Broad Acceptance N  11. Affordable Measurement Y  126 Implementation Todos: • Regional/cultural benchmarking for average satisfaction with other areas of life, what is “normal” for workers on a farm?   Alternative questions that could be asked (as appropriate for a given region?): • Gender distribution of all personnel on the farm (%) • Gender, Ethnic, racial and National Identity of Personnel  • Immigration status of employees  • Marital Status o If married, does their spouse live in the same house as them the majority of the year?  (Y/N) • Does the household have multiple incomes? (Y/N) • How many dependents do they have? (#)  Indicator J: Health & Nutrition  The health and nutrition indicator measures 2 key aspects of health, pesticide exposure and diet (FAO, 2019).  Parameter J1: Exposure to pesticides  Source: TAPE Description: This parameter measures pesticide exposure as a function of the quantity, area, toxicity, and risk mitigation practices for pesticides used (FAO, 2019).  Pesticide usage impacts the sustainability of a system as overuse can have impacts both on the  land as well as human health (FAO, 2019).   Input:  • Farmers would input type, amount, and regularity with which pesticides are used. • Level of toxicity is measured using the highly/moderately/slightly distinction defined by Damalas and Koutroubas (2016)  Data Collection Methods: Once a year the farmer would input the following data: 1. Type of pesticides used (organic, chemical, or a mix of both) 2. Amount of pesticides used:  • An estimation of the quantity of synthetic and quantity of organic pesticides used 3. Toxicity of pesticides used (Class I, II, or III) 4. Mitigation strategies used when applying pesticides, farmers would check off any that apply: a. Mask b. Body protection (glasses, gloves, etc.) c. Special protection for women and children (no elaboration is given on this point in TAPE) d. Visible signs of danger after spraying are recorded e. Community is informed of the danger f. Secure disposal of the empty containers after use  Scoring:  127 Input: Pesticide usage patterns Scoring: Producers use highly hazardous pesticides (Class I) and/or illegal pesticides OR Producers use pesticides of class II or III (Moderately toxic and slightly or relatively non-toxic) with less than 4 of the listed mitigation techniques OR Producers use chemical pesticides of any class and no organic pesticides and no other integrated techniques are used 0 Quantity of synthetic pesticides used > quantity of organic pesticides used, and producers do not use pesticides of class I (Highly toxic), and at least 4 of the listed mitigation techniques are used when applying chemicals, and organic pesticides and/or other integrated techniques are also used 50 Quantity of organic pesticides used ≥ Quantity of synthetic pesticides used, and pesticides of class I and II (highly and moderately toxic) are not used, and at least 4 of the listed mitigation techniques are used when applying chemical pesticides; OR Chemical are not used; organic pesticides and/or other integrated techniques for pest management are used  100  Source: FAO, 2019  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) N  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  128 Implementation Todos:  Parameter J2: Dietary Diversity  Source: TAPE Description: This parameter measures the minimum dietary diversity (MDD) for women, with women acting as a proxy for general population dietary diversity (FAO, 2019). Dietary diversity is a function of the number of 10 food groups consumed over a 24 hour period. The overall health of the people working on the farm is greatly impacted by diet, and therefore the longevity of the farm is dependent on  healthy habits (FAO, 2019).   Input: Farmers would input number of food group consumed by checking off of a list 10 food groups considered: a. Grains, white roots and tubers, and plantains b. Pulses (beans, peas, and lentils) c. Nuts and seeds d. Dairy e. Meat, poultry, fish f. Eggs g. Dark green leafy vegetables h. Other vitamin A-rich fruits and vegetables i. Other vegetables j. Other fruits  Data Collection Methods: Once a year, the farmer would survey all personnel. Each employee would check off which of the following 10 food groups they consumed foods from in the previous week: 1. Grains, white roots and tubers, and plantains 2. Pulses (beans, peas, and lentils) 3. Nuts and seeds 4. Dairy 5. Meat, poultry, fish 6. Eggs 7. Dark green leafy vegetables 8. Other vitamin A-rich fruits and vegetables 9. Other vegetables 10. Other fruits  Scoring:  Input: Number of food groups consumed Scoring: <5  0 5≥ and <7 50 ≥7 100 Source: FAO, 2019  129 Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability Y  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use Y  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos:  Indicator K: Society & Culture  Description: The society and culture indicator measures the sustainability of the societal and cultural conditions within which the farm and its personnel operate (FAO, 2019). Parameter K1: Women’s Empowerment  Source: TAPE Description: This parameter measures women’s empowerment based on survey data which covers 5 domains of women’s empowerment: Input in productive decisions, access to and the decision-making power about productive resources, control over use of income, leadership in the community, and time use (FAO, 2019).  Input:  • Farmers would input their answers to the survey.   Data Collection Methods:  The LiteFarm app should provide a score based on survey answers that all domains are weighted equally to provide a score out of 100%. Suggested survey-based scoring/weighting provided below:  130 Domains Areas of Assessment Answer Score Weight Productive Decisions About crops production, animal production, other economic activities Myself or both of us 1 0.25 My husband or someone else 0 About major and minor household expenditures Myself or both of us 1 0.25 My husband or someone else 0 Perception of decision making about crops production, animal production, other economic activities No decision 0 0.25 Just little decisions 0.33 Some decisions 0.66 In great part/totally 1 Perception of possibility of decision making about major and minor household expenditures No decision 0 0.25 Just little decisions 0.33 Some decisions 0.66 131 In great part/totally 1 Access to and decision making power about productive resources Secure land tenure for men and women (Results pulled from earlier Indicator)  Green for women 1 0.25 Yellow for women, yellow or red for men 0.75 Yellow for women, green for men 0.5 Red for women, red for men 0.25 Red for women, yellow for men 0.1 Red for women, green for men 0 Access to credit Possible for women in secured channels 1 0.25 Possible for women in non-official channels only, possible for men in non-official channels only 0.75 Possible for women in non-official channels only, possible for men in official channels 0.5 Not possible for women, not possible for men 0.25 Not possible for women, possible in non-official channels for men 0.1 132 Not possible for women, possible in official channels for men 0 Ownership of crops, seeds, animals, and other productive assets Myself or both of us 1 0.25 My husband or someone else 0 Ownership of major and minor household assets Myself or both of us 1 0.25 My husband or someone else 0 Control over use of income Decision about the use of the revenue generated by crop production, animal production, and other economic activities I did not contribute or I contribute in few decisions 0 1 I contributed in some decisions 0.5 I contribute in almost all the decisions 1 Leadership in the community If women's associations/organizations/groups exist in your community, how often do you participate in their activities and meetings? Never/almost never 0 0.5 Sometimes 0.33 Most of the times 0.66 Always 1 133 Cooperatives for rural production, social movements, unions of rural workers, political groups, religious groups, training for capacity development, other Never/almost never 0 0.5 Sometimes 0.33 Most of the times 0.66 Always 1 Time Use  More than 10.5 hours spent working per day Women no 1 0.5 Women yes, men yes 0.5 Women yes, men no 0 Time spent in agricultural activities + food preparation & domestic works + other gainful activities Women's time > men's 0 0.5 Women’s time < = men’s 1 Source: FAO, 2019  Scoring:  Input: Scoring on Survey Scoring: <60% 0 ≥60% and <80% 50 ≥80 100  134 Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability N Covers multiple domains 7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use N -Requires investment in surveying staff  10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • A comprehensive yearly survey with every question that would need to be asked of farm personnel rather than just the farm manager in order to simplify this process for them  Parameter K2: Youth Employment Opportunities  Source: TAPE Description: This parameter measures youth employment opportunities based off of both the current employment activities within the system, as well as the desire to emigrate or remain in the current agricultural lifestyle (FAO, 2019)  Input:  • Farmers would input their answers to the survey.   Data Collection Methods: The LiteFarm app should provide a score based on survey answers that all domains are weighted equally to provide a score out of 100%. Suggested survey scores as listed below:  Domain Indicators Score Weight Employment/ activity % of young people working in the agricultural production of the farm 1 0.5 % of young people in education or training 1 135 % of young. People working outside but currently living in the system assessed 0.5 % of young people not in education, nor working in agricultural nor in other activities 0 % of young people who already left the community for lack of opportunities 0 Emigration % of young people who want to continue the agricultural activity of their parents 1 0.5 % of young people who would emigrate, if they had the chance 0.5 % of young people who already left the community for lack of opportunities 0 Source: FAO, 2019  Scoring:  Scoring on Survey Scoring <50% 0 ≥50% and <70% 50 ≥70% 100  Analysis: Criteria Number: Pass? Notes: 1. Sustainability Relevance Y  2. Clearly Defined & Standardized Y  3. Performance-Based (over Practices) Y  4. Sensitivity to Change Y  5. Quantification Y  6. Specificity for Interpretability N Covers multiple domains  7. High Precision and Statistical Power Y  8. Ease of Communication/Understanding Y  9. Ease of Use N -Requires investment in surveying staff  136 10. Broad Acceptance Y  11. Affordable Measurement Y  Implementation Todos: • A comprehensive yearly survey with every question that would need to be asked of farm personnel rather than just the farm manager in order to simplify this process for them   Appendix B TAPE Comparison and Analysis ENVR400-TAPE FRAMEWORK COMPARISON The Tool for Agroecology Performance and Evaluation (TAPE) is an agricultural sustainability framework created by the UN using sustainability indicators pulled from various different frameworks, similar to the ENVR400 framework. TAPE sets out 10 core criteria that must be met by a framework’s indicators to accurately and holistically assess sustainability of a system. In this comparison, ENVR400 indicators which meet the core criteria are noted, while gaps in the framework are filled by pulling indicators directly from TAPE. However, the scoring of TAPE indicators is based on a three tiered (Unsustainable, Acceptable, and Desirable) system, while the ENVR400 Framework utilizes scores out of 100 for each indicator. For the purposes of integrating TAPE indicators into LiteFarm, the following scores should be assigned to each outcome.  TAPE Outcome ENVR400 Framework Scoring Unsustainable 0 Acceptable 50 Desirable 100  It is also important to note the difference in intended audiences and uses for each framework.   Economy  TAPE Parameters: • Productivity: A measure of value produced per hectare of land. This parameter is addressed by parameter E2: Crop Productivity. • Income: A measure of net income produced for the operator of the farm. This is very similar to parameter C1: Available Income. However, I think that TAPE is a more useful and easy to follow measure. Quantification: Net Income = Gross product + subsidies - cost of inputs and taxes - cost of hired labor - loans, interest, and cost of renting land - depreciation of machinery and equipment Scoring 137 Net Income Outcome National poverty line (defined by world bank) > Net income  Unsustainable Median income of similar operations in the region > Net Income > National poverty line (defined by world bank) Acceptable Net Income > Median income of similar operations in the region Desirable   • Added Value: This parameter is a measure of wealth produced by the farm beyond simply the value of crops. It takes into account factors like jobs created. Quantification: Gross added value = Net income - subsidies and income from rented land + cost of hired labour + loans interests and cost of renting land Scoring:   Outcome  < 0.8 Unsustainable  0.8 - 1.2 Acceptable  > 1.2 Desirable  TAPE Parameter ENVR 400 Parameter Productivity Parameter E2: Crop Productivity Income Parameter C1: Available Income, TAPE may be a better fit Added Value Not addressed   Health & Nutrition   TAPE Parameters  • Exposure to pesticides: A measure of pesticide exposure as a function of the quantity, area, toxicity, and risk mitigation practices for pesticides used.  o Quantification:  § Level of toxicity is measured using the highly/moderately/slightly distinction defined by Damalas and Koutroubas 2016 § Presence of mitigation techniques § Use of organic pesticides  § Use of ecological management strategies which decrease the necessity of chemicals  • Scoring Pesticides Usage Patterns Outcome 138 Producers use highly hazardous pesticides (Class I) and/or illegal pesticides OR Producers use pesticides of class II or III (Moderately toxic and slightly or relatively non-toxic) with less than 4 of the listed mitigation techniques OR Producers use chemical pesticides of any class  AND no organic pesticides and no other integrated techniques are used Unsustainable Quantity of synthetic pesticides used > quantity of organic pesticides used AND producers do not use pesticides of class I (Highly toxic) AND at least 4 of the listed mitigation techniques are used when applying chemicals AND organic pesticides and/or other integrated techniques are also used Acceptable Quantity of organic pesticides used ≥ Quantity of synthetic pesticides used  AND pesticides of class I and II (highly and moderately toxic) are not used AND at  least 4 of the listed mitigation techniques are used when applying chemical pesticides; OR Chemical pesticides are not used AND organic pesticides AND/OR other integrated techniques for pest management are used  Desirable   • Dietary Diversity: A measure of the minimum dietary diversity (MDD) for women, with women acting as a proxy for general population dietary diversity. Dietary diversity is a function of the number of 10 food groups consumed over a 24 hour period.  o Quantification:  § 10 food groups considered: 1. Grains, white roots and tubers, and plantains 2. Pulses (beans, peas, and lentils) 3. Nuts and seeds 4. Dairy 5. Meat, poultry, fish 6. Eggs 7. Dark green leafy vegetables 8. Other vitamin A-rich fruits and vegetables 9. Other vegetables 10. Other fruits • Scoring Number of food groups consumed Outcome <5  Unsustainable 5≥ and <7 Acceptable ≥7 Desirable 139 Society & Culture  TAPE Parameters: • Women’s Empowerment: A measure of women’s empowerment based off of survey data which covers 5 domains of women’s empowerment: Input in productive decisions, access to and the decision-making power about productive resources, control over use of income, leadership in the community, and time use  o Quantification:   Domains Areas of Assessment Answer Score Weight Productive Decisions About crops production, animal production, other economic activities Myself or both of us 1 0.25 My husband or someone else 0 About major and minor household expenditures Myself or both of us 1 0.25 My husband or someone else 0 Perception of decision making about crops production, animal production, other economic activities No decision 0 0.25 Just little decisions 0.33 Some decisions 0.66 In great part/totally 1 140 Perception of possibility of decision making about major and minor household expenditures No decision 0 0.25 Just little decisions 0.33 Some decisions 0.66 In great part/totally 1 Access to and decision making power about productive resources Secure land tenure for men and women (Results pulled from earlier indicator)  Green for women 1 0.25 Yellow for women, yellow or red for men 0.75 Yellow for women, green for men 0.5 Red for women, red for men 0.25 Red for women, yellow for men 0.1 Red for women, green for men 0 Access to credit Possible for women in secured channels 1 0.25 Possible for women in non-official channels only, 0.75 141 possible for men in non-official channels only Possible for women in non-official channels only, possible for men in official channels 0.5 Not possible for women, not possible for men 0.25 Not possible for women, possible in non-official channels for men 0.1 Not possible for women, possible in official channels for men 0 Ownership of crops, seeds, animals, and other productive assets Myself or both of us 1 0.25 My husband or someone else 0 Ownership of major and minor household assets Myself or both of us 1 0.25 My husband or someone else 0 Control over use of income Decision about the use of the revenue generated by crop production, animal production, and other economic activities I did not contribute or I contribute in few decisions 0 1 I contributed in some decisions 0.5 142 I contribute in almost all the decisions 1 Leadership in the community If women's associations /organizations/groups exist in your community, how often do you participate in their activities and meetings? Never/almost never 0 0.5 Sometimes 0.33 Most of the times 0.66 Always 1 Cooperatives for rural production, social movements, unions of rural workers, political groups, religious groups, training for capacity development, other Never/almost never 0 0.5 Sometimes 0.33 Most of the times 0.66 Always 1 Time Use  More than 10.5 hours spent working per day Women no 1 0.5 Women yes, men yes 0.5 Women yes, men no 0 143 Time spent in agricultural activities + food preparation & domestic works + other gainful activities Women's time > men's 0 0.5 Women’s time < = men’s 1  Each domain is weighted equally, with weighting of each question and associated scoring noted above.   • Scoring Scoring on Survey Outcome <60% Unsustainable ≥60% and <80% Acceptable ≥80% Desirable    •  • Youth Employment Opportunities: A measure of youth employment opportunities based off of both the current employment activities within the system, as well as the desire to emigrate or remain in the current agricultural lifestyle o Quantification Domain Indicators Score Weight Employment/activity % of young people working in the agricultural production of the farm 1 0.5 % of young people in education or training 1 % of young. People working outside but currently living in the system assessed 0.5 % of young people not in education, nor working in agricultural nor in other activities 0 % of young people who already left the community for lack of opportunities 0 Emigration % of young people who want to continue the agricultural activity of their parents 1 0.5 % of young people who would emigrate, if they had the chance 0.5 % of young people who already left the community for lack of opportunities 0    • Scoring  144 Scoring on Survey Outcome <50% Unsustainable ≥50% and <70% Acceptable ≥70% Desirable  Agricultural Biodiversity Agricultural biodiversity is similarly defined with TAPE as with the rest of our indicators from RISE/IDEA. They believe that agricultural biodiversity is an important way that can help create more “sustainable and equitable production systems” (FAO, 2019, p. 42). They explicitly outline that areas of the world with higher agricultural diversity produce more nutrients (FAO, 2019, p. 42). TAPE places extra emphasis on the presence of trees (rather than RISE’s more general interpretation of “areas of ecological importance”) as a proxy for biodiversity (FAO, 2019, p. 42).   TAPE uses a more scientific approach to calculate agricultural biodiversity than the simple species counts featured in RISE/IDEA. They pool plant/animal species counts area-based species occupation to calculate a Gini-Simpson index of diversity (FAO, 2019, p. 43). For LiteFarm this may impose a higher than expected data upkeep cost as farmers would need to do transect-based data collection (similar to techniques in field ecology) to be able to estimate a Gini-Simpson index (FAO, 2019, p. 43).  TAPE describes advanced methods (FAO, 2019, p. 44) such as using the “Index of Agrobiodiversity” (IDA) to help calculate a more fine-grained estimate of biodiversity. In comparison to RISE and IDEA, the basic measures of biodiversity for TAPE go beyond the upkeep required for RISE or IDEA already. Going one step further appears to require too much upkeep for the farmer to recommend at this time. IDA is composed of four “sub-indices'': food for humans, food for animals, improvements for soil properties, and complementary species (FAO, 2019, p. 45).   TAPE points to the “Livestock Environmental Assessment and Performance” (LEAP) partnership as a source of methods to assess biodiversity (FAO, 2019, p. 45).  • Parameters: o Animal Biodiversity  § Source: FAO, 2019, p. 43 § Quantification: Species counts (Gini-Simpson index), Livestock unit equivalencies Formula: 1 - D = 1 - Σpi2 pi is the abundance (proportion of species p vs total number of individuals) i is the proportion of individuals (in livestock units) found in the i-th species.  • Scoring: Score (Gini-Simpson %): Interpretation: ≥ 70% Good ≥ 50% and <70% Acceptable 145 < 50% Unsustainable • Implementation Notes: o May be hard for farmers to do transects/get farmers to do transects o Need to educate farmers on what a “Gini Simpson Index” is.  • Plant Biodiversity o Source: FAO, 2019, p. 43 o Quantification: Quantification: Species counts (Gini-Simpson index), Livestock unit equivalencies Formula: 1 - D = 1 - Σpi2 pi is the abundance (proportion of area covered by a given crop i) i is the proportion of individuals (in livestock units) found in the i-th species.  • Scoring:  Score (Gini-Simpson %): Interpretation: ≥ 70% Good ≥ 50% and <70% Acceptable < 50% Unsustainable   • Implementation Notes: o May be hard for farmers to do transects/get farmers to do transects o Need to educate farmers on what a “Gini Simpson Index” is.  • Natural Vegetation, Trees and Pollinators • Source: FAO, 2019, p. 44 • Quantification: Average of linear scale. Linear scales for beekeeping, productive areas covered by natural/diverse vegetation, presence of pollinators and beneficial animals.  Sub-Parameter: Relative Value Value Beekeeping No 0 Yes, wild 0.5 Yes, Raised 1 Productive area covered by natural/diverse vegetation Absent  Small 0 Medium 0.25 146 Significant 0.5 Abundant 1 Presence of pollinators and beneficial animals Absent 0 Little 0.33 Significant 0.66 Abundant 1  Adapted From: FAO, 2019, p. 44 • Scoring: Score (Average of Criteria %): Interpretation: ≥ 70% Good ≥ 50% and <70% Acceptable < 50% Unsustainable   • Implementation Notes: o Need to regionalize “presence of pollinators/beneficial animals” to reflect regional differences in the presence/availability of pollinators/beneficial animals and clarify what levels each label (e.g. little vs. significant) would mean.  o Need to define what “small”, “medium”, “significant”, etc. mean, it is not as clear as RISE’s 17% (Grenz et al, 2018).  Soil Health As with RISE or IDEA in the existing framework developed for LiteFarm, TAPE recognizes the importance of soil in an agricultural operation (FAO, 2019, p. 45). They hope to measure the “stabilization of oil structure, the maintenance of soil life and biodiversity, retention and release of plant nutrients, and maintenance of [the soil’s] water-holding capacity” (FAO, 2019, p. 45).   TAPE suggests that farmers can improve soil health by “minimiz[ing] mechanical soil disturbance, organic fertilization from animal manure or compost, permanent soil cover (organic matter supply through the preservation of crop residues and cover crops) crop rotation for biocontrol and efficient use of the soil profile, rotation grazing management, and minimal soil compaction” (FAO, 2019, p. 45).  The measurements that TAPE suggests are tied to the ones done for agricultural biodiversity and are based on the Latin American Society for Agroecology’s methodologies on “field measurements and [the] assessment of [the] agroecosystem properties that reflect soil quality and plant health” (FAO, 2019, p. 147 45).  TAPE suggests farmers find a patch of soil that represents the average status of the soil on all productive lands of the farm (FAO, 2019, p. 87).   Similar to RISE, they also suggest measuring soil organic matter (SOM) as an important characteristic of the soil to help support sustainable agriculture (FAO, 2019, p. 46-47). In our current analysis, we find it hard to suggest without further information on the availability of local lab facilities and their prices (i.e. can farmers get access and afford to measure SOM?).   As a brief analysis of the provided measurements, we believe TAPE’s measure of soil compaction could be very useful to implement in LiteFarm (FAO, 2019, p. 87). Their soil flagging approach (e.g. sticking a flag into the soil) is simple and easy to perform.  • Soil Structure 5 3 1   • Source: FAO, 2019, p. 46, 87 • Quantification: Characteristic: Score: 1 Loose, powdery soil without visible aggregates 3 Few aggregates that break with little pressure 5 Well-formed aggregates – difficult to break   • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable Source: FAO, 2019, p. 87 • Implementation Notes: • Degree of Compaction o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Compacted soil, flag bends readily 148 3 Thin compacted layer, some restrictions to a penetrating wire 5 No compaction, flag can penetrate all the way into the soil Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • May need to define what “easily” may mean • Soil Depth o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Exposed subsoil 3 Thin superficial soil 5 Superficial soil (> 10 cm)   • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • Status of Residues o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Slowly decomposing organic residues 149 3 Presence of last year’s decomposing residues 5 Residues in various stages of decomposition, most residues well-decomposed Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • Colour, Odor, and Organic matter o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Pale, chemical odor, and no presence of humus 3 Light brown, odorless, and some presence of humus 5 Dark brown, fresh odor, and abundant humus Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • Water Retention  o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Dry soil, does not hold water 3 Limited moisture level available for short time 150 5 Reasonable moisture level for a reasonable period of time Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • Soil Cover o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Bare soil 3 Less than 50% soil covered by residues or live cover 5 More than 50% soil covered by residues or live cover Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • Signs of Soil Erosion  o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Severe erosion, presence of small gullies 3 Evident, but low erosion signs 151 5 No visible signs of erosion Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: • Provide visual representation of gullies/erosional signs for reference • Presence of Invertebrates • Source: FAO, 2019, p. 46, 87 • Quantification: Characteristic: Score: 1 No signs of invertebrate presence or activity 3 A few earthworms and arthropods present 5 Abundant presence of invertebrate organisms Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: o Provide explanatory background on what invertebrates can do for soil • Microbiological Activity o Source: FAO, 2019, p. 46, 87 o Quantification: Characteristic: Score: 1 Very little effervescence after application of water peroxide 3 Light to medium effervescence 152 5 Abundant effervescence Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable   • Implementation Notes: o Determine exact strength of water peroxide required o Define and educate farmers what “effervescence” means Governance Governance primarily covers the land holding/secure land access documents that define where a farmer can conduct their operations on. TAPE hopes that farmers will be able to become “autonomous and self-sufficient, and to define their own models of development” (FAO, 2019, p. 27) - i.e. become food sovereign.   We believe that this is not the most relevant indicator for LiteFarm as LiteFarm focuses on producing indicators that are able to appraise a farm’s sustainability from an operational standpoint (rather than on a governance standpoint). LiteFarm may choose to add this to their set of indicators if they feel their mission would benefit from this kind of data measurement.    • Parameters: o Secure Land Tenure/Mobility (for Pastoralists) § Source: FAO, 2019, p. 27 § Quantification: Characteristic: Score: 1 No document possessedAND perception of insecure access to landAND/OR no right to sell/bequeath/inherit the 3 Has a formal document with the name of the holder on it AND perception of insecure access to land AND/OR no right to sell/bequeath/inherit the landORHas a formal document even if the name of the holder is not on itOR has no document but has perception of secure land AND has at least one right to sell/bequeath/inherit the land; 153 5 Has a formal document with the name of the holder on itAND has perception of secure access to landAND has at least one right to sell/bequeath/inherit any of the parcel of the holding Source: FAO, 2019, p. 87 • Scoring: Score: Interpretation: 5 Good 3 Acceptable 1 Unsustainable  Appendix C External Farm Sustainability Tool Survey: Farm Tool Survey Analysis and Summary: Overall, most of the tools are often focused on the fundamental business operations for a farm such as inventory, yields, accounting, and human resource allocation. Many tools also offer integrations with heavy farm machinery (ex. John Deere tractors for Agrivi or Conservis) that may not be directly applicable to an average subsistence farmer. For our project, a basic thematic analysis against our goals (physical science sustainability indicators for farms) from our list of tools finds some useful applications for researching the use of remote sensing data (Normalized Difference Vegetation Index, NDVI), weather analysis, environmental data integrations, and finally modelling. Remote sensing data metrics, such as the Normalized Difference Vegetation Index (NDVI), play a role in many tools such as with ISAGRI’s, Granular Ag, Cropio, and Agrivi seem to be highly useful indicators for farmers to use. This data is often available for free from governmental sources (ex. US NASA’s MODIS program, NASA, 2020) and the issue may lie in getting that data ready for use by farmers in the field who also need to be taught the value of these indicators for their daily work. OneSoil is another tool of interest as it provides satellite imagery analysis of lands for free and provides analytical insights based on machine-learning analysis. Many tools, such as Agrivi, Farmer’s Edge, ISAGRI, or FarmLogs include some kind of weather-impact analysis that could be useful. Finding literature data or general research on the correlation between weather events and estimated losses per acre (or the like) may be a way to generate actionable insights for a farmer. In terms of environmental data integrations, such as with soil data (Land-PKS, Cropio), greenhouse gas estimates (COMET-Farm), biodiversity (Cool Farm Tool) can come a long way in assessing the sustainability of a farm. The challenge may be to directly link these physical indicators with the success of a given farm. Fieldprint comes closest to a holistic evaluation of sustainability and can be an excellent starting point for examining the role of sustainability indicators in use for farms. 154 Modelling out different scenarios or providing our reference simulations can help farmers visualize what they can do, especially if they may not have had the exposure to different farming practices. Tools such as Haller’s Farm App or Farmer’s Edge can help educate farmers on new practices and their potential outcomes, acting as a virtual extension officer. Thus, across our themes of remote sensing, weather impact analysis, environmental data monitoring, and modelling/simulations, we have made a shortlist (below) outlining our next steps for tools research for this project. Table of Potential Tools for Further Research: Remote Sensing: Weather Impact Analysis: Environmental Data Integration: Modelling/Simulations: OneSoil Agrivi FieldPrint Haller’s Farm App Granular Ag Farmer’s Edge Land-PKS Farmer’s Edge Cropio FarmLogs Cropio   Agrivi ISAGRI (French Language) COMET-Farm   ISAGRI (French Language)   Cool Farm Tool     Land PKS (Land Potential Knowledge System) Platform: Mobile app (iOS/Android)/online data portal Open source: Yes (data US Public Domain, no GitHub) Link: https://landpotential.org/ Requirements: Some lab results (or alternative), some geographical restrictions on data (habitats) Description of Capabilities: An open-source tool to interpret soil, vegetation cover, and land management data to improve soil health and productivity. Meant to support traditional, regenerative, organic, and holistic land management. 155 Modules: land Information, land management (record keeping), soil health (no lab required, soil pH, SOM, EC requires specialized tests), land cover, habitats (US only) Comments: This could be a potentially useful resource to connect with and use for LiteFarm as it is an open-source tool and it focuses on key physical science indicators that can help drive farm yields (ex. soil pH and organic matter content). Seed Linked Platform: iOS/Android Open source: No Link: https://www.seedlinked.com/ Requirements: Join their community and participate in their seed trials Description of Capabilities: This tool is primarily meant to crowdsource the testing of seeds produced by breeders by connecting willing growers to seed breeders, providing a platform to run studies, monitor progress and results, analyze data, and share it with users within the network. Comments: This tool seems like it could provide auxiliary services for the LiteFarm app, but it may be of marginal use for the average farmer. The average farmer may not be interested in being a part of a controlled study to test out new breeds of seeds. Regen Network Platform: iOS/Android Open source: Yes (Apache 2.0) Link: https://www.regen.network/ Requirements: Some soil sampling (farm size-dependent, 25-75 for an example farm/round), join the network Description of Capabilities: Allows farmers to earn digital credits (blockchain technology) for their work to reduce carbon emissions. One credit equals 1 ton of CO2e removed from the atmosphere. This project is a work in progress with some 3rd party verification of carbon reductions. Buyers of credits can then pay for these ecological services provided by the farmers. Some integration with other platforms (ex. FarmOS). Comments: This tool may provide a helpful linkage between farmers and carbon markets. While not necessarily a physical Indicator, it can bring an alternate source of income for farmers. However, the costs in verification by third parties may prove prohibitive for the poorest of farmers. FarmOS & Farm OS Fieldkit 156 Platform: Web-based application/Android/iOS Open source: Yes (GNU General Public License) Link: https://farmos.org/ Requirements: Webserver to host app (Raspberry Pi?) Description of Capabilities: This tool helps farms plan, manage, and record data during the regular operations of a farm. Some features include mapping areas of the farm, todo lists, asset lists, logs, people, inventory tracking and integrations with other platforms. Limited commercialization/monetary tracking is available. A dedicated app on mobile (Farm OS Fieldkit) allows farmers to connect to FarmOS from a mobile device with more limited functionality. Comments: This set of tools seems to be a direct competitor against the functionalities of LiteFarm, especially as they offer features that deal with the direct management of farms, assets, and the day-to-day operations of a producer. While the base version of FarmOS does not provide direct indicators/support for measuring sustainability indicators, the integrations that it has with many other tools as part of OpenTEAM allows FarmOS users significant flexibility in using the FarmOS platform. The requirement for a web server may prove difficult for the poorest of subsistence farmers. Haller Farmers Web App Platform: Web-based application/Android/iOS Open source: No Link: https://haller.org.uk/impact/the-app/ Requirements: Description of Capabilities: This tool is meant to provide smallholder farmers with open access to farming techniques and agricultural information. Some of the information provided includes step-by-step instructions, farming tips, youth content, conservation information, human/animal conflict management, contact with Haller (a UK-based charity), indigenous/medicinal crop planting techniques, model farm visualization and English/Swahili language support. Comments: This tool seems custom-designed for poorer farmers in developing countries. It is limited in its management capabilities as it appears to be mostly an information-sharing platform. Some of the information within can be used to guide development for sustainability indicators for LiteFarm. Cool Farm Tool Platform: Web-based application 157 Open source: No Link: https://coolfarmtool.org/coolfarmtool/ Requirements: Some farm data (ex. electricity/fuel bills, pesticide use, areas) Description of Capabilities: This tool is meant to support analyses of nutrient, energy and land use for greenhouse gas emissions analysis, biodiversity scoring of farm management practices, and farm water requirements (crop irrigation and water footprints) using an online web app. It allows farmers to help manage their farms as part of a larger supply chain of resources from farm to table and to communicate to others their environmental practices. This tool is free for farmers and is designed to require a minimum amount of data. Larger institutions will have to pay a fee. Comments: This tool looks very promising in that it provides usable sustainability metrics from data that farmers already have (ex. fuel bills/pesticide use). This is a very promising tool for integration/to draw inspiration from for LiteFarm. OurSci – SurveyStack Platform: Web-based application/Android/iOS Open source: Yes? (Cannot find Github) Link: http://blog.our-sci.net/2020/06/04/introducing-surveystack-the-new-data-collection-platform-from-the-our-sci-team/ Requirements: Description of Capabilities: This tool is meant to organize and maintain records of scientific data collected on one or more farms. It can integrate with FarmOS and create surveys for use in collecting observational data for farm experiments. Eventually, it will integrate with hardware tools for data collection. Comments: This tool seems like it would be of limited value to LiteFarm as it is a survey platform for field data collection. This does not seem to estimate sustainability metrics or provide actionable insights. Tend Platform: Web-based application/Android/iOS Open source: No Link: https://www.tend.com/ Requirements: 158 Description of Capabilities: Tend is meant to be a one-stop-shop for farm data, allowing farmers to plan plantings, keep records and notes, generate reports, and see farm analytics. Crop planning can be treated in the context of project management software (ex. Monday.com). Taskings can help farmers out as a to-do list with some costing of labour to help track the financials of work. It also allows farmers to sell to consumers with product listings and payments. Through relationships with restaurants, Tend can help inform farmers of orders and support their record-keeping of tasks. Farm analytics can help breakdown costs and crop yields. This is a commercial app with costs for farmers to use. Comments: This is another direct competitor to LiteFarm, but it may prove cost-prohibitive for many types of farmers. While there are no direct sustainability-relevant measures provided by this website, inspiration can be drawn for how different metrics can integrate in a highly-polished commercial application. Field to Market – Fieldprint Platform: Web-based application Open source: No (cannot find Github/code) Link: https://fieldtomarket.org/our-programs/ Requirements: Data on-farm operations (ex. windbreakers, pesticide usage, irrigation) Description of Capabilities: FieldPrint is a free application that can perform analysis to benchmark farm sustainability across biodiversity, energy use, greenhouse gas emissions, water use, land use, soil carbon, soil conservation, and water quality with cross-references to external datasets (ex. USDA Natural Resources Conservation data) to provide a holistic view of farm sustainability. Comments: Being a free application with linkages to other software, perhaps partnering or exploring similar functionalities with the simple interface of sustainability indicators (i.e. spider webs) can help inform similar functionalities with LiteFarm. COMET-Farm Platform: Web-based application Open source: Yes Link: https://comet-farm.com/ Requirements: Data on-farm operations (ex. livestock, cropland, energy use) Description of Capabilities: 159 COMET-Farm estimates greenhouse gas emissions across all of the operations for a farm. Data can be provided for the system, along with how different farm-management approaches can compare to current baseline emissions. Reports can then be generated to provide actionable insights. Comments: As COMET-Farm is a free platform for estimating the carbon emissions of a farm, it can be useful for general interest scenarios, or for changing farming practices in support of producing emissions-reduction credits for trade. This can mean that LiteFarm could link into the platform to support farmer decision-making. Fieldin Platform: Web-based platform/iOS/Android Open source: No Link: https://www.fieldin.com/ Requirements: Sensors, subscription to services, tracking equipment Description of Capabilities: Fieldin provides a paid platform for farmers to manage pesticide applications, harvest operations, and other fieldwork by connecting with on-the-ground machinery (ex. tractors) and sensors to produce actionable insights for producers. Actionable insights may include pesticide spray tracking (to prevent double-sprays), harvest analysis (ex. shaking times for trees), and peer-farmer analysis of methods to benchmark an individual farmer’s outputs. Comments: The insights and data that Fieldin generates may be best suited to farmers of sufficient means to connect all of their machinery with the Fieldin system. Fieldin uses information from machinery and sensors to produce insights that subsistence farmers may find out-of-reach or cost-prohibitive to use. Perhaps a refinement of methods highlighted by Fieldin may be helpful for LiteFarm to provide similar insights for lower-income farmers. Agrivi Platform: Web-based platform/iOS/Android Open source: No Link: https://www.agrivi.com/en/ Requirements: Subscription to service Description of Capabilities: 160 Agrivi is a farm management software that helps farmers plan, monitor, and analyze farm tasks online. It helps track work such as tilling, planting, crop protection, fertilization, irrigation, and harvesting. It also includes a knowledge base for over 100 crops, along with weather monitoring and pest detection algorithms. Financially, it offers tools to keep records and documents together for sales, expenses, and investments. Support for tracking employees and assets is provided to help farmers keep track of essential resources. These data points can then be aggregated to produce analytics and results for the farm. Special support for wineries, satellite imagery (biomass and disease detection, Normalized Difference Vegetation Index, NDVI), and traceability are available. Comments: This product seems targeted towards producers in developed economies and is a paid product. Subsistence farmers may struggle to find the money to use this product, but LiteFarm may be able to provide similar functionality for cheaper. In terms of sustainability, NASA MODIS provides open data on biomasses and related vegetation data at a very fine scale (500m), this can be used to generate imagery/analytics for farmers using existing methods and open tools. Granular Ag Platform: Web-based platform/iOS/Android Open source: No Link: https://ca.granular.ag/ Requirements: Subscription to service Description of Capabilities: Granular Ag provides a paid suite of tools for farmers, farm managers, and agronomists. Granular Insights provides remote sensing tools that can help farmers scout fields to detect problems. The satellite imagery can provide data (ex. Wide Dynamic Range Vegetation Index) at a very fine scale (3m). Granular Business provides farm management software for the financial planning of a farm. Granular Insights provides agronomists tools to help manage field sampling, recommend fertility/seeding prescriptions to clients, and simulate changes to assess best paths forward. Comments: Granular Ag is very similar to Agrivi, but with more targeting and segmentation of products. This product seems targeted towards producers in developed economies and is a paid product. Subsistence farmers may struggle to find the money to use this product, but LiteFarm may be able to provide similar functionality for cheaper. 161 In terms of sustainability, NASA MODIS provides open data on biomasses and related vegetation data at a very fine scale (250m), this can be used to generate imagery/analytics for farmers using existing methods and open tools (NASA, 2020). Conservis Platform: Web-based platform/iOS/Android Open source: No Link: https://conservis.ag/ Requirements: Subscription to service, Compatible John Deere Tractors/FieldView (some features) Description of Capabilities: Conservis is meant to act as a paid data hub to bring multiple streams of data together and provides flexibility for multiple classes of users. It is meant to help track field activities, manage inventories, analyze yields and finances. This data can also help farmers access financing through Rabo AgriFinance in the United States. Conservis provides integrations with John Deere tractors and John Deere’s Climate FieldView software to help estimate yields from farms and estimate the efficiency of fields. Comments: While this is commercial software that is probably not suitable for the average subsistence farmer of limited means, the yield estimates and field efficiency metrics that it mentions may help support some of the physical science sustainability indicators in linking indicators to general outcomes. FarmLogs Platform: Web-based platform/iOS/Android Open source: No Link: https://farmlogs.com/ Requirements: Subscription to service Description of Capabilities: FarmLogs provides a paid, tiered approach to its software offering to help farmers plan, manage, monitor, and market crops. Some features include field mapping, rainfall tracking, rain/heat history, field scouting, soil maps, crop markets, satellite imagery, local pricing, work orders, and financial analytics. Satellite imagery includes true colour, infrared and NDVI layers at two-week intervals for a 10m resolution. Comments: 162 While not specifically a sustainability Indicator, the local pricing of crops might prove to be very helpful and actionable for farmers to know how crop prices might change in a given area. Open satellite imagery and weather data are available and could potentially provide a substitute for this paid service. Agworld Platform: Web-based platform/iOS Open source: No Link: https://www.agworld.com/ca/products/ Requirements: Subscription to service, (Optional) Data compatible equipment (ex. John Deere tractors) Description of Capabilities: Agworld is a paid software platform that helps farmers keep track of key metrics on farms. In its record-keeping, it helps to log observations, machinery, operations, pests, and provide map annotations. It can help farmers plan for future seasons with templated reports and financial analytics. Some calculation support is available for mixing chemicals. Compatible machines can feed data into Agworld. Agworld provides agronomists with a way to work closely with farmers with access to their data. Agworld provides lab integrations with several laboratory-service companies. Comments: While not specifically a sustainability indicator, a mixing calculator/recipe generator might be helpful for a farmer’s workflow in applying chemicals to the field in planning out tasks. An interface for labs to enter in data may help support LiteFarm sustainability indicator operations. ISAGRI Platform: Web-based platform/iOS Open source: No Link: https://www.agworld.com/ca/products/ Requirements: (Optional) Data compatible equipment (ex. John Deere tractors) Description of Capabilities: * Website is in French * ISAGRI offers a variety of software and hardware solutions for farms. Their Geofolia offering helps keep track of activities done on plots (ex. fertilizing fields), remote sensing integration, analytics on tillage, treatments (Treatment Frequency Index), and on weather-based impacts. They also have software specifically for wineries.  They have software that handles farm accounting management, payroll, and point-of-sale activities. Comments: 163 An interesting avenue to explore might be the interplay between weather and remote sensing analytics and farm yields. There is an abundance of remote sensing and weather data from open governmental sources that could benefit farmers in terms of actionable metrics. Cropio Platform: Web-based platform/iOS/Android Open source: No Link: https://about.cropio.com/ Requirements: Subscription to service Description of Capabilities: Cropio is meant to act as a paid service to help farmers manage their fields and vegetation with features such as vegetation mapping, weather data, soil moisture content, nitrogen deficits, harvest forecasting, farm telemetrics/tracking (ex. tracking of harvesting activity). Comments: The physical science analytics prove a very interesting avenue of analysis, particularly through Cropio’s attention to soil science (nitrogen content, soil moisture, and soil lab tests), weather monitoring, and remote sensing integrations. With some appropriate research, these could prove to be valuable metrics for LiteFarm. Cropio Platform: Web-based platform/iOS/Android Open source: No Link: https://about.cropio.com/ Requirements: Subscription to service Description of Capabilities:  Cropio is meant to act as a paid service to help farmers manage their fields and vegetation with features such as vegetation mapping, weather data, soil moisture content, nitrogen deficits, harvest forecasting, farm telemetrics/tracking (ex. tracking of harvesting activity). Comments: The physical science analytics prove a very interesting avenue of analysis, particularly through Cropio’s attention to soil science (nitrogen content, soil moisture, and soil lab tests), weather monitoring, and remote sensing integrations. With some appropriate research, these could prove to be valuable metrics for LiteFarm. 164 Farm@Hand Platform: Web-based platform/iOS/Android Open source: No Link: https://www.farmathand.com/ Requirements: Subscription to service Description of Capabilities: Farm@Hand helps farmers keep track of their operations. They have field, inventory and task management tools, planning tools that integrate yield and financial data, and collaboration tools for agronomists. These agronomy tools help agronomists access farmer data, make recommendations, and communicate with stakeholders. It can also interface with John Deere's digital service to help share data between platforms for data.   Comments: This tool may be of limited value in guiding Indicator generation for our project as it focuses more on the operation, financial, agronomy activities of a farm. Farmer’s Edge Platform: Web-based platform/iOS/Android Open source: No Link: https://www.farmersedge.ca/ Requirements: Subscription to service Description of Capabilities: Farmer’s Edge is a company that focuses on developing software tools for the agri-food industry. Farm Command comes with several features, such as severe weather monitoring, satellite imagery (ex. plant health, cloud/shadow identification), scouting fields, in-cab support for tractors, fleet monitoring, predictive modelling, benchmarking, and analytics/reporting. Comments: The tool’s physical science integrations (weather and satellite imagery), along with its predictive modelling suite may prove to be of interest to farmers. It can act to help farmers learn new insights into their farms to grow more sustainably and efficiently. OneSoil Platform: Web-based platform/iOS/Android 165 Open source: No Link: https://onesoil.ai/en/applications#onesoil-fertilizers Requirements: Description of Capabilities: Using machine learning algorithms, OneSoil aims to provide decision-making support for farmers via remote sensing of fields. It can detect changes in plant vegetation (Normalized Difference Vegetation Index) every 3-5 days, assist in field scouting note-taking, segregate out fields by NDVI into three categories for different types of fertilizer application, and use weather data to predict field-level (2km) outcomes. Comments: The remote sensing work done by this platform seems to be highly innovative and a great avenue to explore for future work in driving sustainability indicators for LiteFarm.   References: NASA. (2020). MODIS Web. Retrieved 17 October 2020, from https://modis.gsfc.nasa.gov/about/      

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