ClimMob: Software to support experimental citizen science in agriculture.

Experimental citizen science offers new ways to organize on-farm testing of crop varieties and other agronomic options. Its implementation at scale requires software that streamlines the process of experimental design, data collection and analysis, so that different organizations can support trials. This article considers ClimMob software developed to facilitate implementing experimental citizen science in agriculture. We describe the software design process, including our initial design choices, the architecture and functionality of ClimMob, and the methodology used for incorporating user feedback. Initial design choices were guided by the need to shape a workflow that is feasible for farmers and relevant for farmers, breeders and other decision-makers. Workflow and software concepts were developed concurrently. The resulting approach supported by ClimMob is triadic comparisons of technology options (tricot), which allows farmers to make simple comparisons between crop varieties or other agricultural technologies tested on farms. The software was built using Component-Based Software Engineering (CBSE), to allow for a flexible, modular design of software that is easy to maintain. Source is open-source and built on existing components that generally have a broad user community, to ensure their continuity in the future. Key components include Open Data Kit, ODK Tools, PyUtilib Component Architecture. The design of experiments and data analysis is done through R packages, which are all available on CRAN. Constant user feedback and short communication lines between the development teams and users was crucial in the development process. Development will continue to further improve user experience, expand data collection methods and media channels, ensure integration with other systems, and to further improve the support for data-driven decision-making.

gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation.

To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data, a type of data that is generated in on-farm experimentation and data synthesis exercises, among others. To address this need, we developed the R package gosset, which provides functionality for rank-based data and models. The gosset package facilitates data preparation, modeling and results presentation stages. It introduces novel functions not available in existing R packages for analyzing ranking data. This paper demonstrates the package functionality using the case study of a decentralized on-farm trial of common bean (Phaseolus vulgaris L.) varieties in Nicaragua.

Assessing Farmers' Diverse Preferences and Expectations for Tailoring Food and Nutrition Security Interventions in Southeastern Madagascar.

Background: Severe food and nutrition insecurity persists in Madagascar. The Atsimo Atsinanana region is among the most affected areas due to elevated poverty rates and low levels of resilience to frequent shocks. Implementing food and nutrition security (FNS) interventions could help to improve this situation, but to be effective and sustainable, intervention packages must fit the local context. Objectives: To identify locally suitable options, this study assessed the perceptions of local communities in rural Atsimo Atsinanana toward a range of FNS intervention options. Methods: We held 12 gender-disaggregated workshops with 80 prospective beneficiaries of an FNS project, from inland and coastal parts of the region. Preferences were elicited for 14 potential FNS interventions. Next, through participatory ex ante impact assessment, participants ranked 8 impact criteria and individually estimated expected impacts of all intervention options on these criteria. Results: Overall, participants preferred interventions targeting on-farm crop, vegetable, and livestock production. Income and food self-sufficiency were ranked as the highest intervention priorities. However, intervention preferences differed by gender and geographic location. Whereas preferences for interventions targeting dietary habits were weak across genders, women had relatively stronger preferences for these interventions than men. This shows that collecting gender-disaggregated preferences can enable more gender-sensitive choice of interventions. Preferences also reflected local livelihoods, as more market-oriented coastal sites showed stronger interest in income generation than more subsistence-oriented inland sites. The ex ante impact assessments highlight positive and negative expectations for most interventions, with increased labor burden being the most prominent negative impact overall. Conclusions: The findings suggest that participatory, multidimensional impact assessments before project implementation can support development stakeholders in tailoring intervention packages, considering 1) local and gendered preferences and 2) trade-offs among development objectives.

Effect of passive solar drying on food security in rural Mozambique.

Achieving food security in Mozambique is critical, since 80% of the population cannot afford an adequate diet. While increasing agricultural production is a necessary effort to address this challenge, inadequate post-harvest treatment leads to storage losses and quality degradation, with repercussions for food security. The use of solar drying is promoted as a solution to provide efficient and reliable access to food preservation that improves the food security situation in rural communities. However, there is a lack of clear evidence on how the use or access to solar drying affects food security. This study identifies the determinants of farmers' choice to use solar drying and evaluates the effect of a passive solar dryer on food security using survey data from 634 households. We allocated solar dryers to selected communities and all interested individuals belonging to these communities were eligible to use it. Propensity score matching and endogenous switching poisson regression are used to estimate the average effect. The use of solar drying with associated training significantly increases the food security status of participants by increasing household food availability, women's dietary diversity, and months of adequate household food provision and by decreasing the household food insecurity access scale.

Understanding the drivers of food security among agriculture-based households in Gurué District, Central Mozambique.

BACKGROUND: The prevalence of food insecurity in Mozambique is alarming, despite progress made during the 2010s. Several studies apply different proxy indicators of food security (FS) to assess the FS situation. However, these studies overlook the factors affecting FS, using only a single data point that results in an incomplete picture of FS. Food security is expected to fluctuate, being better and worse than what studies suggest. Using a sample of 296 households to assess FS, key drivers conditioning households' capacity to achieve FS in Gurué District, Central Mozambique, are identified. Data were collected in the pre-harvest period and during the harvest period to capture relevant interseasonal variation of FS. Household FS is assessed using three standard indicators: Household Dietary Diversity Score (HDDS), Household Food Consumption Score (HFCS), and Months of Adequate Household Food Provisioning (MAHFP). RESULTS: Each household was classified into a specific FS status depending on the indicator applied. Generally, most households were classified as being severely or moderately food insecure during the pre-harvest season, while during the harvest season, medium and high levels of FS predominated. Nevertheless, varying outcomes were found depending on the indicator used to assess FS. MAHFP and HDDS are more related to the consumption of farm-sourced food, while HFCS responds more strongly to purchased food. Gender and age of the household head, geographic location, size and quality of land, staples production (especially cassava), livestock and crop diversity, as well as cash crops had a statistically significant effect on FS indicators. CONCLUSIONS: The study concludes that the decision whether farmers should rely on staple foods production for increasing their FS status or specialize on cash crops production to generate income and buy food depends on the indicator used to assess FS, since each indicator captures a specific domain of food security. Thus, one central recommendation derived from our results is that policy makers should promote a balance between market-oriented agriculture and subsistence production to achieve FS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40066-021-00344-3.

Assessing impacts of COVID-19 and their responses among smallholder farmers in Brazil, Madagascar and Tanzania.

This study investigates the impacts of the first wave of the COVID-19 pandemic on smallholder farmers and their coping strategies in three contrasting Low- and Middle-Income Countries. The case studies include Brazil (South region), Madagascar (Atsimo Atsinanana region), and Tanzania (Morogoro/Eastern Tanzania). These countries were chosen because i) the economies are strongly influenced by the agricultural sector; ii) their national food security is strongly affected by smallholder production, and, iii) they represent a set of contrasting government responses to COVID-19 including the denial of the pandemic. Data were collected through semi-structured household interviews in all three countries in rural areas. COVID-19 induced effects were found in all three countries, including in Brazil and Tanzania where both national governments initially neglected the existence of COVID-19 and introduced few containment measures only. Here, mobility and trade restrictions of other countries impact also on agricultural trade and production in countries in which governments took less action to COVID-19 and also people remained home and practiced social distancing even if no official government policy was issued. The findings in all three countries suggest that the COVID-19 crisis had negatively affected smallholders' agricultural production, leading to a vicious cycle of low production, low incomes, and higher food insecurity. Results of this study raise the thorny issue of how best to balance containment of pandemic and future shocks against the well-being of the vulnerable rural population in lower- and middle-income countries; especially considering also the degree of global interconnected and the potential of polices to effect people beyond the national scale.

The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries.

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity. These variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on- and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households.

Crop variety management for climate adaptation supported by citizen science.

Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean (Phaseolus vulgaris L.) in Nicaragua, durum wheat (Triticum durum Desf.) in Ethiopia, and bread wheat (Triticum aestivum L.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations.

Prioritizing options for multi-objective agricultural development through the Positive Deviance approach.

Agricultural development must integrate multiple objectives at the same time, including food security, income, and environmental sustainability. To help achieve these objectives, development practitioners need to prioritize concrete livelihood practices to promote to rural households. But trade-offs between objectives can lead to dilemmas in selecting practices. In addition, heterogeneity among farming households requires targeting different strategies to different types of households. Existing diversity of household resources and activities, however, may also bear solutions. We explored a new, empirical research method that identifies promising options for multi-objective development by focusing on existing cases of strong multi-dimensional household performance. The "Positive Deviance" approach signifies identifying locally viable livelihood practices from diverse households that achieve stronger performance than comparable households in the same area. These practices are promising for other local households in comparable resource contexts. The approach has been used in other domains, such as child nutrition, but has not yet been fully implemented for agricultural development with a focus on the simultaneous achievement of multiple objectives. To test our adapted version of the Positive Deviance approach, we used a quantitative survey of over 500 rural households in South-Eastern Tanzania. We identified 54 households with outstanding relative performance regarding five key development dimensions (food security, income, nutrition, environmental sustainability, and social equity). We found that, compared to other households with similar resource levels, these "positive deviants" performed strongest for food security, but only slightly better for social equity. We then re-visited a diverse sub-sample for qualitative interviews, and identified 14 uncommon, "deviant" practices that plausibly contributed to the households' superior outcomes. We illustrate how these practices can inform specific recommendations of practices for other local households in comparable resource contexts. The study demonstrates how, with the Positive Deviance approach, empirical observations of individual, outstanding households can inform discussions about locally viable agricultural development solutions in diverse household context.

What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers.

As the sustainability of agricultural citizen science projects depends on volunteer farmers who contribute their time, energy and skills, understanding their motivation is important to attract and retain participants in citizen science projects. The objectives of this study were to assess 1) farmers' motivations to participate as citizen scientists and 2) farmers' mobile telephone usage. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers' characteristics. The questionnaire was applied in three communities of farmers, in countries from different continents, participating as citizen scientists. We used statistical tests to compare motivational factors within and among the three countries. In addition, the relations between motivational factors and farmers characteristics were assessed. Lastly, Principal Component Analysis (PCA) was used to group farmers based on their motivations. Although there was an overlap between the types of motivations, for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. While fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued 'passing free time' the lowest. Two major groups of farmers were distinguished: one motivated by sharing information (egoistic intrinsic), helping (altruism) and contribute to scientific research (collectivistic) and one motivated by egoistic extrinsic factors (expectation, expert interaction and community interaction). Country and education level were the two most important farmers' characteristics that explain around 20% of the variation in farmers motivations. For educated farmers, contributing to scientific research was a more important motivation to participate as citizen scientists compared to less educated farmers. We conclude that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. Citizen science does have high potential, but easy to use mechanisms are needed. Moreover, gamification may increase the egoistic intrinsic motivation of farmers.