With high and increasing expectations for research to have social and environmental impact, there is a corresponding need for appropriate methods to demonstrate (for accountability) and analyze (for learning) whether and how research projects contribute to change processes. Evaluation is especially challenging for problem-oriented research that employs inter- and transdisciplinary approaches and intervenes in complex systems, where experimental and statistical approaches to causal inference are inappropriate. Instead, theory-based evaluation can be applied to identify and test causal processes. This paper presents a detailed explanation of the Outcome Evaluation approach applied in Belcher et al. (2019b). It draws on concepts and approaches used in theory-based program evaluation and the more limited experience of theory-based research evaluation, providing a brief overview of conceptual strengths and limitations of other methods. The paper offers step-by-step guidance on application of the Outcome Evaluation approach, detailing how to: document a theory of change; determine data needs and sources; collect data; manage and analyze data; and present findings. This approach provides a clear conceptual and analytical framework in addition to actor-specific and impact pathway analyses for more precision in the assessment of outcomes. Specifically, the Outcome Evaluation approach: •Conceptualizes research within a complex system and explicitly recognizes the role of other actors, context, and external processes;•Utilizes a detailed actor-centred theory of change (ToC) as the analytical framework; and•Explicitly tests a set of hypotheses about the relationship between the research process/outputs and outcomes.

With high and increasing expectations for research to have social and environmental impact, there is a corresponding need for appropriate methods to demonstrate (for accountability) and analyze (for learning) whether and how research projects contribute to change processes. Evaluation is especially challenging for problem-oriented research that employs inter- and transdisciplinary approaches and intervenes in complex systems, where experimental and statistical approaches to causal inference are inappropriate. Instead, theory-based evaluation can be applied to identify and test causal processes. This paper presents a detailed explanation of the Outcome Evaluation approach applied in Belcher et al. (2019). It draws on concepts and approaches used in theory-based program evaluation and the more limited experience of theory-based research evaluation, providing a brief overview of conceptual strengths and limitations of other methods. The paper offers step-by-step guidance on application of the Outcome Evaluation approach, detailing how to: document a theory of change; determine data needs and sources; collect data; manage and analyze data; and present findings. This approach provides a clear conceptual and analytical framework in addition to actor-specific and impact pathway analyses for more precision in the assessment of outcomes. Specifically, the Outcome Evaluation approach: •Conceptualizes research within a complex system and explicitly recognizes the role of other actors, context, and external processes;•Utilizes a detailed actor-centred theory of change (ToC) as the analytical framework; and•Explicitly tests a set of hypotheses about the relationship between the research process/outputs and outcomes.

The research and development needed to achieve sustainability of African smallholder agricultural and natural systems has led to a wide array of theoretical frameworks for conceptualising socioecological processes and functions. However, there are few analytical tools for spatio-temporal empirical approaches to implement use cases, which is a prerequisite to understand the performance of smallholder farms in the real world. This study builds a multi-agent system (MAS) to operationalise the Sustainable Agricultural Intensification (SAI) theoretical framework (MASSAI). This is an essential tool for spatio-temporal simulation of farm productivity to evaluate sustainability trends into the future at fine scale of a managed plot. MASSAI evaluates dynamic nutrient transfer using smallholder nutrient monitoring functions which have been calibrated with parameters from Malawi and the region. It integrates two modules: the Environmental (EM) and Behavioural (BM) ones. • The EM assess dynamic natural nutrient inputs (sedimentation and atmospheric deposition) and outputs (leaching, erosion and gaseous loses) as a product of bioclimatic factors and land use activities. • An integrated BM assess the impact of farmer decisions which influence farm-level inputs (fertilizer, manure, biological N fixation) and outputs (crop yields and associated grain). • A use case of input subsidies, common in Africa, markedly influence fertilizer access and the impact of different policy scenarios on decision-making, crop productivity, and nutrient balance are simulated. This is of use for empirical analysis smallholder's sustainability trajectories given the pro-poor development policy support.

The research and development needed to achieve sustainability of African smallholder agricultural and natural systems has led to a wide array of theoretical frameworks for conceptualising socioecological processes and functions. However, there are few analytical tools for spatio-temporal empirical approaches to implement use cases, which is a prerequisite to understand the performance of smallholder farms in the real world. This study builds a multi-agent system (MAS) to operationalise the Sustainable Agricultural Intensification (SAI) theoretical framework (MASSAI). This is an essential tool for spatio-temporal simulation of farm productivity to evaluate sustainability trends into the future at fine scale of a managed plot. MASSAI evaluates dynamic nutrient transfer using smallholder nutrient monitoring functions which have been calibrated with parameters from Malawi and the region. It integrates two modules: the Environmental (EM) and Behavioural (BM) ones. • The EM assess dynamic natural nutrient inputs (sedimentation and atmospheric deposition) and outputs (leaching, erosion and gaseous loses) as a product of bioclimatic factors and land use activities. • An integrated BM assess the impact of farmer decisions which influence farm-level inputs (fertilizer, manure, biological N fixation) and outputs (crop yields and associated grain). • A use case of input subsidies, common in Africa, markedly influence fertilizer access and the impact of different policy scenarios on decision-making, crop productivity, and nutrient balance are simulated. This is of use for empirical analysis smallholder's sustainability trajectories given the pro-poor development policy support.

The research and development needed to achieve sustainability of African smallholder agricultural and natural systems has led to a wide array of theoretical frameworks for conceptualising socioecological processes and functions. However, there are few analytical tools for spatio-temporal empirical approaches to implement use cases, which is a prerequisite to understand the performance of smallholder farms in the real world. This study builds a multi-agent system (MAS) to operationalise the Sustainable Agricultural Intensification (SAI) theoretical framework (MASSAI). This is an essential tool for spatio-temporal simulation of farm productivity to evaluate sustainability trends into the future at fine scale of a managed plot. MASSAI evaluates dynamic nutrient transfer using smallholder nutrient monitoring functions which have been calibrated with parameters from Malawi and the region. It integrates two modules: the Environmental (EM) and Behavioural (BM) ones. • The EM assess dynamic natural nutrient inputs (sedimentation and atmospheric deposition) and outputs (leaching, erosion and gaseous loses) as a product of bioclimatic factors and land use activities. • An integrated BM assess the impact of farmer decisions which influence farm-level inputs (fertilizer, manure, biological N fixation) and outputs (crop yields and associated grain). • A use case of input subsidies, common in Africa, markedly influence fertilizer access and the impact of different policy scenarios on decision-making, crop productivity, and nutrient balance are simulated. This is of use for empirical analysis smallholder's sustainability trajectories given the pro-poor development policy support.

Excess agricultural nitrogen, mainly from manure and chemical fertilizers, is a primary source of nutrient pollution and presents serious environmental threats to natural ecosystems and human health. Improvements in nitrogen-use efficiency in crop production are critical for addressing the triple challenge of food insecurity, environmental degradation, and climate change. Approaches such as sustainable intensification that stress technological innovations have received the most attention. But science-based cropland use planning, a promising complementary approach, has so far been largely overlooked. Here we develop a spatially integrated economic-ecological modeling method to assess this previously unexplored potential for improving the economic-environmental performance of crop production by examining the seasonal and spatial implications of cropland and fertilizer use in Bangladesh. In doing so, we aim to make the modeling method accessible to researchers and practitioners interested in achieving the dual goal of food production and environmental sustainability for countries that are characterized by seasonal and spatial variations in crop mix and cropping practices.

Excess agricultural nitrogen, mainly from manure and chemical fertilizers, is a primary source of nutrient pollution and presents serious environmental threats to natural ecosystems and human health. Improvements in nitrogen-use efficiency in crop production are critical for addressing the triple challenge of food insecurity, environmental degradation, and climate change. Approaches such as sustainable intensification that stress technological innovations have received the most attention. But science-based cropland use planning, a promising complementary approach, has so far been largely overlooked. Here we develop a spatially integrated economic-ecological modeling method to assess this previously unexplored potential for improving the economic-environmental performance of crop production by examining the seasonal and spatial implications of cropland and fertilizer use in Bangladesh. In doing so, we aim to make the modeling method accessible to researchers and practitioners interested in achieving the dual goal of food production and environmental sustainability for countries that are characterized by seasonal and spatial variations in crop mix and cropping practices.

Excess agricultural nitrogen, mainly from manure and chemical fertilizers, is a primary source of nutrient pollution and presents serious environmental threats to natural ecosystems and human health. Improvements in nitrogen-use efficiency in crop production are critical for addressing the triple challenge of food insecurity, environmental degradation, and climate change. Approaches such as sustainable intensification that stress technological innovations have received the most attention. But science-based cropland use planning, a promising complementary approach, has so far been largely overlooked. Here we develop a spatially integrated economic-ecological modeling method to assess this previously unexplored potential for improving the economic-environmental performance of crop production by examining the seasonal and spatial implications of cropland and fertilizer use in Bangladesh. In doing so, we aim to make the modeling method accessible to researchers and practitioners interested in achieving the dual goal of food production and environmental sustainability for countries that are characterized by seasonal and spatial variations in crop mix and cropping practices. | PR | IFPRI3; 1 Fostering Climate-Resilient and Sustainable Food Supply; CRP7 | EPTD | CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)

Our group works on the detection and characterization of cassava viruses, supporting projects that involve large scale pathogen surveillance activities and resistance screening assays in multiple and remote locations. In order to comply with these applications, nucleic acid isolation protocols need to be cost effective, adjusted for samples that will stand long distance transport and harsh storage conditions, while maximizing the yield and quality of the nucleic acid extracts obtained. The method we describe here has been widely used and validated using different downstream tests (including, but not limited to, Rolling Circle Amplification and Illumina and Nanopore sequencing), but is currently unpublished. The protocol begins with milligram amounts of dry leaf samples stored in silica gel, does not require liquid Nitrogen nor phenol extraction and produces an average of 2.11 µg of nucleic acids per mg of dry tissue.

Our group works on the detection and characterization of cassava viruses, supporting projects that involve large scale pathogen surveillance activities and resistance screening assays in multiple and remote locations. In order to comply with these applications, nucleic acid isolation protocols need to be cost effective, adjusted for samples that will stand long distance transport and harsh storage conditions, while maximizing the yield and quality of the nucleic acid extracts obtained. The method we describe here has been widely used and validated using different downstream tests (including, but not limited to, Rolling Circle Amplification and Illumina and Nanopore sequencing), but is currently unpublished. The protocol begins with milligram amounts of dry leaf samples stored in silica gel, does not require liquid Nitrogen nor phenol extraction and produces an average of 2.11 µg of nucleic acids per mg of dry tissue.•DNA purity estimations reveal OD260/280 ratios above 2.0 and OD260/230 ratios above 1.7, even for samples stored in silica gel for several months.•The high quality of the extracts is suitable for detection of DNA and RNA viruses, with high efficiency.•We suggest this method could be used as part of a gold standard kit for virus detection in cassava.

An optimized high-quality DNA isolation protocol was developed using body segment tissue from the Fall Armyworm (Spodoptera frugiperda), that will allow documenting genetic variability based on biotypes, facilitating studies on the appearance, distribution and population dynamics of the fall armyworm at the molecular level. The resulting protocol is an easy-to-use, timesaving method that can rapidly achieve high quality, high-yielding total genomic DNA, using chemicals and everyday consumables available in a molecular laboratory. This new method of DNA extraction avoids the contamination of polysaccharides, salts, phenols, proteins and other cellular by-products that can interfere with subsequent reactions. DNA purity estimates reveal A260: A280 ratios greater than 1.9, which were evidenced by quality test on agarose gel, observing complete integrity and high purity of the resulting samples, and yielded 30–99 µg/g of total DNA. Therefore, the quality of the DNA produced from this extraction is suitable for subsequent molecular applications: (i) next generation whole genome sequencing, (ii) conventional polymerase chain reaction for genotyping, (iii) barcodes and (iv) gene cloning.

An optimized high-quality DNA isolation protocol was developed using body segment tissue from the Fall Armyworm (Spodoptera frugiperda), that will allow documenting genetic variability based on biotypes, facilitating studies on the appearance, distribution and population dynamics of the fall armyworm at the molecular level. The resulting protocol is an easy-to-use, timesaving method that can rapidly achieve high quality, high-yielding total genomic DNA, using chemicals and everyday consumables available in a molecular laboratory. This new method of DNA extraction avoids the contamination of polysaccharides, salts, phenols, proteins and other cellular by-products that can interfere with subsequent reactions. DNA purity estimates reveal A260: A280 ratios greater than 1.9, which were evidenced by quality test on agarose gel, observing complete integrity and high purity of the resulting samples, and yielded 30–99 µg/g of total DNA. Therefore, the quality of the DNA produced from this extraction is suitable for subsequent molecular applications: (i) next generation whole genome sequencing, (ii) conventional polymerase chain reaction for genotyping, (iii) barcodes and (iv) gene cloning. In addition, to become an anticipating diagnostic tool for invasive lepidopteran larval stages:•The resulting protocol is an easy-to-use time-saving method.•This new extraction method prevents contamination from polysaccharides, salts, phenols, proteins, and other cellular sub-products.•DNA purity estimations reveal A260:A280 ratios above 1.9.

Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g., those of farms, research managers, donors, or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social, and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.

Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.

Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.

Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.

An optimized method, based on the coupling of two commercial kits, is described for the extraction of soil nucleic acids, with simultaneous extraction and purification of DNA and RNA following a cascade scheme and avoiding the use of harmful solvents. The protocol canmonitor the variations in the recovery yield of DNA and RNA from soils of various types.The quantitative version of the protocol was obtained by testing the starting soil quantity, the grinding parameters and the final elution volumes, in order to avoid saturation of both kits. o A first soil-crushing step in liquid nitrogen could be added for the assessment of fungal parameters. o The protocol was efficienton different tropical soils, including Andosol, while their high contents of clays, including poorly crystalline clays, and Fe and Al oxides usually make the nucleic acid extraction more difficult. o The RNA recovery yield from the previous tropical soils appeared to correlate better to soil respiration than DNA, which is positively influenced by soil clay content. (Résumé d'auteur)

An optimized method, based on the coupling of two commercial kits, is described for the extraction of soil nucleic acids, with simultaneous extraction and purification of DNA and RNA following a cascade scheme and avoiding the use of harmful solvents. The protocol canmonitor the variations in the recovery yield of DNA and RNA from soils of various types.The quantitative version of the protocol was obtained by testing the starting soil quantity, the grinding parameters and the final elution volumes, in order to avoid saturation of both kits.•A first soil-crushing step in liquid nitrogen could be added for the assessment of fungal parameters.•The protocol was efficienton different tropical soils, including Andosol, while their high contents of clays, including poorly crystalline clays, and Fe and Al oxides usually make the nucleic acid extraction more difficult.•The RNA recovery yield from the previous tropical soils appeared to correlate better to soil respiration than DNA, which is positively influenced by soil clay content.

An optimized method, based on the coupling of two commercial kits, is described for the extraction of soil nucleicacids, with simultaneous extraction and purification of DNA and RNA following a cascade scheme and avoidingthe use of harmful solvents. The protocol can monitor the variations in the recovery yield of DNA and RNA fromsoils of various types.- The quantitative version of the protocol was obtained by testing the starting soil quantity, the grindingparameters and the final elution volumes, in order to avoid saturation of both kits.- A first soil-crushing step in liquid nitrogen could be added for the assessment of fungal parameters.- The protocol was efficient on different tropical soils, including andosol, while their high contents of clays,including poorly crystalline clays, and Fe and Al oxides usually make the nucleic acid extraction more difficult.- The RNA recovery yield from the previous tropical soils appeared to correlate better to soil respiration thanDNA, which is positively influenced by soil clay content.

An optimized method, based on the coupling of two commercial kits, is described for the extraction of soil nucleicacids, with simultaneous extraction and purification of DNA and RNA following a cascade scheme and avoidingthe use of harmful solvents. The protocol can monitor the variations in the recovery yield of DNA and RNA fromsoils of various types.- The quantitative version of the protocol was obtained by testing the starting soil quantity, the grindingparameters and the final elution volumes, in order to avoid saturation of both kits.- A first soil-crushing step in liquid nitrogen could be added for the assessment of fungal parameters.- The protocol was efficient on different tropical soils, including andosol, while their high contents of clays,including poorly crystalline clays, and Fe and Al oxides usually make the nucleic acid extraction more difficult.- The RNA recovery yield from the previous tropical soils appeared to correlate better to soil respiration thanDNA, which is positively influenced by soil clay content.