Using Sentinel-2 to Track Field-Level Tillage Practices at Regional Scales in Smallholder Systems

Zero tillage is an important pathway to sustainable intensification and low-emission agriculture. However, quantifying the extent of zero tillage adoption at the field scale has been challenging, especially in smallholder systems where field sizes are small and there is limited ground data on zero tillage adoption. Remote sensing offers the ability to map tillage practices at large spatio-temporal scales, yet to date no studies have used satellite data to map zero tillage adoption in smallholder agricultural systems. In this study, we use Sentinel-2 satellite data, random forest classifiers, and Google Earth Engine to map tillage practices across India’s main grain producing region, the Indo-Gangetic Plains. We find that tillage practices can be classified with moderate accuracy (an overall accuracy of 75%), particularly in regions with relatively large field sizes and homogenous crop management practices. We find that models that use satellite data from only the first half of the growing season perform as well as models that use data throughout the growing season, allowing for the creation of within-season tillage maps. Finally, we find that our model can generalize well through time in the western IGP, with reductions in accuracy of only 5–10%. Our results highlight the ability of Sentinel-2 satellite data to map tillage practices at scale, even in smallholder systems where field sizes are small and cropping practices are heterogeneous.

Spatial parameters associated with the risk of banana bunchy top disease in smallholder systems

The Banana Bunchy Top Disease (BBTD), caused by the Banana Bunchy Top Virus (BBTV) is the most important and devastating in many tropical countries. BBTD epidemiology has been little studied, mixed landscape smallholder systems. The relative risks associated with this disease vary between geographical areas and landscapes. This work analyzed the management and vegetation conditions in smallholder gardens to assess the factors linked to landscape-level BBTV transmission and management. Mapping was done in this study area which is in a BBTD-endemic region, involving farmers actively managing the disease, but with household-level decision making. A spatial scanning statistic was used to detect and identify spatial groups at the 5% significance threshold, and a Poisson regression model was used to explore propagation vectors and the effect of surrounding vegetation and crop diversity. Spatial groups with high relative risk were identified in three communities, Dangbo, Houéyogbé, and Adjarra. Significant associations emerged between the BBTD prevalence and some crop diversity, seed systems, and BBTD management linked factors. The identified factors form important candidate management options for the detailed assessment of landscape-scale BBTD management in smallholder communities.

Wild gardening as a sustainable intensification strategy in northwest Cambodian smallholder systems

Within the last decade, Sustainable Intensification (SI) has emerged as a strategy to respond to future food security challenges. It incorporates increased food production without the cultivation of more land while incurring no net environmental cost. Frameworks attempting to measure SI often focus heavily on production indicators while overlooking other important indicators, such as impacts on economic, social, or human conditions. In this study, we evaluate the purposeful assemblage and management of neglected and underutilized species (NUS) in fringe areas around rural homesteads as a potential SI strategy. We use a recent SI assessment framework developed by the Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) that incorporates five measurable domains (productivity, economic, environmental, human condition, and social). We present findings from a qualitative case study in northwest Cambodia to assess local conceptu­alization of wild gardening, current uses of NUS, perceived benefits and challenges to their use and management, and the potential of wild gardening as a SI strategy. The qualitative methods employed 65 key informant interviews and four focus group discussions with both men and women partici­pants. Our results indicate that wild gardening is an important component of rural livelihoods in northeast Cambodia. However, a general lack of knowledge of strategic benefits, such as nutrition poten­tial, inhibits its use for maximum benefits. Wild gardening addresses multiple SI domains simultaneously and demonstrates the potential to be a promising SI strategy for improving rural livelihoods in Cambodia.

Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms

Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms (<600 m2). This was likely because its higher spatial resolution (3 m) could better account for field-level variations in smallholder systems. We also examined the impact of image timing on the classification accuracy, and we found that early-season images did little to improve our models. Overall, we found that readily available Sentinel-1, Sentinel-2, and Planet imagery were able to map crop types at the field-scale with high accuracy in Indian smallholder systems. The findings from this study have important implications for the identification of the most effective ways to map crop types in smallholder systems.

Constraints and options to sustainably intensifying smallholder maize farming systems in southern Africa

&lt;p&gt;In southern Africa, sustainable intensification (SI) of low input farming is promoted as a key strategy to improve the livelihoods and food security of smallholder farmers. It has been argued, however, that due to the severity and frequency of droughts, irrigation is a prerequisite for sustainable yield improvement and stability, and less crop failures. Restricted access to water for such farmers in the study region necessitates the investigation of alternative adaptive management options suited to smallholder systems. Using the Limpopo province South Africa as a case study, we use a combination of survey data (140 households) and detailed quantitative agronomic measurements and observations (116 georeferenced on-farm plots) to understand yield limitations in maize-based smallholder systems. Data was collected from five villages in the Mopani district representing a distinct climate gradient. Agronomic measurements included soil characteristics such as CN ratio, texture, rooting depth and management aspects such as weed type and soil cover, as well as maize planting density, biomass and yield. Combined insights from the interviews and detailed on-farm observations were used to benchmark the agro-ecosystem model APSIM, which was then setup for different technology levels. These were defined through combinations of advanced crop and soil management practices plus the status quo as observed through the ground-truthing campaign with no irrigation, zero to low fertilisation, little weeding, no pest management, and low planting density. Advanced practices involved higher input levels including irrigation and fertiliser, as well as management aspects such as increased planting density and intense weeding.&lt;/p&gt;&lt;p&gt;Survey results showed that farmers adjusted sowing time and planting density according to rainfall availability and perceived risk. Overall, input intensity levels were low (fertiliser and density) and all villages expressed similar challenges to adapt to climate variability. It appeared most farmers lacked knowledge about drought avoidance measures, and only very few had access to water for crop irrigation.&lt;/p&gt;&lt;p&gt;Our simulation results showed that irrigation could increase maize grain yields by around two tons ha&lt;sup&gt;-1&lt;/sup&gt; over a three-year average for a moderately wet site under current management practices. For the driest site, this led to an increase of just over one ton ha&lt;sup&gt;-1&lt;/sup&gt;. If irrigation is applied it necessitates an increase in biotic stress management, as failing to do so can compromise potential yield gains. Higher labour input, increased input costs and possibly associated increased economic risks make such intensification strategies unattractive for some farmers depending on their age and household economic security.&lt;/p&gt;&lt;p&gt;For this case study, we outlined and implemented a novel method of linking survey and agro-ecosystem modelling data to assess ex-ante potential impacts of SI in smallholder cropping systems vulnerable to climate-induced risk.&lt;/p&gt;

Pig breeding.

This chapter presents a brief background on the global pig sector and pig-meat value chains. Topics focused on the breeding objectives, pig breeds and lines, genetic improvement strategies for pigs (for both large-scale and smallholder systems) and use of pig reproductive technologies. The final section of this chapter gives some practical guidelines for selection.

Spatial modelling improves genetic evaluation in smallholder breeding programs

Background Breeders and geneticists use statistical models to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. We hypothesised that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Furthermore, geographically referenced environmental covariates are increasingly available and could model underlying sources of spatial relationships. The objective of this study was therefore, to evaluate the potential of spatial modelling to improve genetic evaluation in dairy cattle smallholder systems. Methods We performed simulations and real dairy cattle data analysis to test our hypothesis. We modelled environmental variation by estimating herd and spatial effects. Herd effects were considered independent, whereas spatial effects had distance-based covariance between herds. We compared these models using pedigree or genomic data. Results The results show that in smallholder systems (i) standard models do not separate genetic and environmental effects accurately, (ii) spatial modelling increases the accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve the accuracy of genetic evaluation beyond simple distance-based relationships between herds, (iv) the benefit of spatial modelling was largest when separating the genetic and environmental effects was challenging, and (v) spatial modelling was beneficial when using either pedigree or genomic data. Conclusions We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds, which enhances separation of genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have a major impact in studies of human and wild populations.

Business Models and Innovation in the Indonesian Smallholder Beef Value Chain

This paper proposes a framework for identification of business models in smallholder cattle production and marketing that represents innovation behaviour. Cattle are vital to Indonesia’s smallholders’ livelihoods, and smallholder systems are key to serving the country’s growing demand for beef. Business incentives currently select against the development and sustainability of breeding systems which would support domestic production, and so new models are needed which utilize innovation. The three primary components of a business model are presented: its value proposition, its value architecture, and its financing mechanism. A research approach is provided, by way of mapping data needs to the business models, and proposing relationships between observed innovation practices and the business models within the value chain. For implementation, the paper provides guidance on facilitation needs and the role of stakeholders in the case of the Indonesian cattle and beef value chain.

Value-Ag: An integrated model for rapid ex-ante impact evaluation of agricultural innovations in smallholder systems

Evaluation of agricultural Research, Development, Extension and Management requires knowledge of farming systems economics and risk as well as broader adoption drivers. But until now, these factors have not been effectively combined when determining the success of agricultural research projects. To fill this gap, we developed Value-Ag, an integrated modelling platform using whole-farm economic analysis and prediction of the scaling potential in the context of production risk and household dynamics to provide an ex-ante estimate of the benefits of adopting an innovation. In this paper, we use a hypothetical case study to illustrate Value-Ag’s potential to evaluate agricultural innovations in a rigorous, systematic and participatory manner across a range of scenarios, thereby stimulating thinking and learning opportunities with the relevant stakeholders, and increasing the scrutiny of projects so that they deliver greater value for money while fostering a more results-focused culture in developing countries.