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Tailoring interventions through a combination of statistical typology and frontier analysis: a study of mixed crop-livestock farms in semi-arid Zimbabwe
No full textAn innovative methodological approach combining statistical typologies and stochastic frontier analysis was applied to data collected from 1840 mixed crop-livestock farms in six districts of Zimbabwe, representative of semi-arid areas of the country. The average annual cereal production was 362 kg farm–1, and the average annual livestock offtake was 0.64 ± 1.32 Tropical Livestock Units (TLU) farm–1. Our results demonstrate there is scope to increase cereal and livestock production by 90.7% and 111.9% relative to current production levels, respectively, with more efficient use of existing resources and technologies. Rainfall was found to have a strong effect on cereal production, highlighting the need for climate-smart practices. Livestock mortality (0.59 ± 1.62 TLU farm–1) was found to be in the same order of magnitude as livestock offtake (0.64 ± 1.32 TLU farm–1). Cereal production was supported by livestock, demonstrating the importance of crop-livestock interactions in these mixed farming systems. Three farm types were identified in our analysis. Crop-oriented mixed farms (31%) are likely to be the ones most responsive to crop-specific interventions e.g., crop rotation and integrated pest management. Livestock-oriented mixed farms (34%) are likely to benefit the most from livestock-specific interventions, e.g., home feed. Mixed farms dependent on off-farm activities (36% of the sample) may require nutrition-sensitive and labour-saving sustainable intensification technologies to benefit from their limited resources. Reducing cattle mortality is a priority for all three farm types. The method proposed here could be adapted to other contexts characterized by heterogeneous farming populations to target interventions
Enhancing winter wheat prediction with genomics, phenomics and environmental data
No full textIn the realm of multi-environment prediction, when the goal is to predict a complete environment using the others as a training set, the efficiency of genomic selection (GS) falls short of expectations. Genotype by environment interaction poses a challenge in achieving high prediction accuracies. Consequently, current efforts are focused on enhancing efficiency by integrating various types of inputs, such as phenomics data, environmental information, and other omics data. In this study, we sought to evaluate the impact of incorporating environmental information into the modeling process, in addition to genomic and phenomics information. Our evaluation encompassed five data sets of soft white winter wheat, and the results revealed a significant improvement in prediction accuracy, as measured by the normalized root mean square error (NRMSE), through the integration of environmental information. Notably, there was an average gain in prediction accuracy of 49.19% in terms of NRMSE across the data sets. Moreover, the observed prediction accuracy ranged from 5.68% (data set 3) to 60.36% (data set 4), underscoring the substantial effect of integrating environmental information. By including genomic, phenomic, and environmental data in prediction models, plant breeding programs can improve selection efficiency across locations
Enhancing breeding infrastructures for the Ethiopian chickpea, sorghum, and millet breeding program
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Rebalancing global nitrogen management to address the food-fertilizer-climate crisis
No full text25 slide
Understanding sustained adoption of conservation agriculture among smallholder farmers: insights from a sentinel site in Malawi
No full textSmallholder farming systems need climate-proofing and sustainable intensification practices such as conservation agriculture (CA), are promising options. However, there is a general perception that the adoption of CA systems in southern Africa is low. Sentinel sites, where CA has been promoted for a long time, offer forward-looking new insights. This paper, thus, takes a deep dive at Nkhotakota district of Malawi to understand what could have led to the success of CA promotion and subsequent perceived high adoption. We use survey data from 620 farmers, with 298 farmers sampled from treatment areas – known to have had contact with host farmers and 320 from a control group. Overall, 31% of the farmers in both groups adopted full CA over at least a 2-year period. We also find that about 57% of farmers in the treatment area adopted full CA and only 7% of farmers in the control areas. This highlights that longer-term CA promotion with dedicated extension support can enhance the uptake of CA practices. In essence, this paper offers a different perspective to the current narrative that CA systems are too complex and knowledge intensive to be adopted despite its long-term promotion and significant investments. However, there are some nuances: sustained adoption even in sentinel sites is neither 100% nor persistent over the long term. We find an appreciable adoption decay, showing large declines from highs of 57 and 7% in adoption for at least 2 years for treatment and control, respectively, to 12% in the treatment group and practically zero in the control when we condition full CA adoption to at least 7 years. This means that fewer farmers adopted CA for a longer period and suggests some dis-adoption over time even in sentinel sites. The key adoption enablers in the sentinel sites include the availability of training, dedicated longer-term extension support coupled with farmer experiential learning through demonstration plots managed by host farmers. Based on our findings, there is need to consistently promote CA using farmer-centric approaches that include peer-to-peer learning over long periods. This allows farmers time to experiment with different CA options, enable behavioral and lasting change. At policy level, there is need to build and strengthen farmer groups to facilitate easier access to inputs like leguminous crop seeds for farmers practicing CA and to offer market-smart incentives to induce initial adoption in the short term to facilitate sustained adoption
Agricultura Sustentable: Opciones para la implementación de prácticas sustentables
No full textPresented at Expoceres, Agroinnovación, from March 14 to 16, 2024.31 slide
Closing the gaps in experimental and observational crop response estimates: a Bayesian approach
No full textA stylized fact of African agriculture is that crop responses to inorganic fertilizer application derived from experimental studies are often substantially greater than those from observational studies (e.g., surveys and administrative data). Recent debates on relative costs and benefits of expensive farm input subsidy programs in Africa, have raised the importance of reconciling these estimates. Beyond mean response differences, this paper argues for including parameter uncertainty and heterogeneity arising from variations in soil types, environmental conditions, and management practices. We use a Bayesian approach that combines information from experimental and observational data to model uncertainty and heterogeneity in crop yield responses. Using nationally representative experimental, survey, and administrative datasets from Malawi, we find that: (1) crop responses are low in observational data, (2) there are large spatial heterogeneities, and (3) based on sensitivity analysis, ignoring parameter uncertainty and spatial heterogeneity in crop responses can lead to questionable policy prescriptions
Double-digest restriction-associated DNA sequencing-based genotyping and its applications in sesame germplasm management
No full textSesame (Sesamum indicum L.) is an ancient oilseed crop belonging to the family Pedaliaceae and a globally cultivated crop for its use as oil and food. In this study, 2496 sesame accessions, being conserved at the National Genebank of ICAR-National Bureau of Plant Genetic Resources (NBPGR), were genotyped using genomics-assisted double-digest restriction-associated DNA sequencing (ddRAD-seq) approach. A total of 64,910 filtered single-nucleotide polymorphisms (SNPs) were utilized to assess the genome-scale diversity. Applications of this genome-scale information (reduced representation using restriction enzymes) are demonstrated through the development of a molecular core collection (CC) representing maximal SNP diversity. This information is also applied in developing a mid-density panel (MDP) comprising 2515 hyper-variable SNPs, representing almost equally the genic and non-genic regions. The sesame CC comprising 384 accessions, a representative set of accessions with maximal diversity, was identified using multiple criteria such as k-mer (subsequence of length "k" in a sequence read) diversity, observed heterozygosity, CoreHunter3, GenoCore, and genetic differentiation. The coreset constituted around 15% of the total accessions studied, and this small subset had captured >60% SNP diversity of the entire population. In the coreset, the admixture analysis shows reduced genetic complexity, increased nucleotide diversity (pi), and is geographically distributed without any repetitiveness in the CC germplasm. Within the CC, India-originated accessions exhibit higher diversity (as expected based on the center of diversity concept), than those accessions that were procured from various other countries. The identified CC set and the MDP will be a valuable resource for genomics-assisted accelerated sesame improvement program
Identifying resource-conscious and low-carbon agricultural development pathways through land use modelling
No full textIncreasing agricultural production with current resources and technology may lead to increased GHG emissions. Additionally, large population countries like India face substantial challenges in terms of food demand, agroecological heterogeneity, carbon footprint and depleting natural resources, thus increasing the decision complexities for policymakers and planners. We aim to examine the potential of producing more food from available agricultural land with low-carbon (reduced GHG emissions) and resource-conscious (optimal resource use) options. The current study develops multiple calorie production and emission-centric land use using a land use optimization model wherein the calorie production and emission objective, resource and emissions constraints, and food production targets interact across multiple spatial levels. The capabilities of the developed model are demonstrated with a case study in India targeting ten crops (grown over two seasons) covering three food groups (cereals, legumes, and oilseeds). Three hypothetical scenarios for each objective of maximizing calories production (Calories-nation, Calories-group, Calories-crop) and minimizing GHG emissions (Emissions-nation, Emissions-group, Emissions-crop) are developed concerning targets of national crop production (Calories-nation, Emissions-nation), state food groups production (Calories-group, Emissions-group), and state crop production(Calories-crop, Emissions-crop), with different spatial levels of constraints. A maximum growth of 11% in calorie production is observed in Calories-nation while mitigating 2.5% emissions. Besides, the highest emission reduction of around 30% is observed in Emissions-group but with no change in calorie production. Emission scenarios can spare up to 14.8% land and 18.2% water, while calorie production-maximization scenarios can spare a maximum of 4.7% land and 6.5% water. The optimization-based methodology identifies the regions of altered land use by proposing appropriate crop substitution strategies, such as increasing oilseeds in Rajasthan and soybean in east Maharashtra. Many states show conservative production growth and emission reduction with state-level crop production targets (Calories-crop), suggesting crop redistribution within the state alone will not be sufficient unless improved technologies are introduced. The maximum growth and mitigation potential estimated in this study may be affected by climate shocks; therefore, introducing the improved technologies needs to be coupled with a crop redistribution mechanism to design climate-resilient and futuristic land use systems. The proposed land use model can be modified to incorporate climate change effects through consideration of scenarios of changed crop yields or through direct/indirect coupling with dynamic crop simulation models
Gendered impacts of training and factors influencing Good Agricultural Machinery Manufacturing Practices Adoption in Bangladesh
No full textThis study examined the gendered impact of training and the factors influencing the adoption of Good Agricultural Machinery Manufacturing Practices (GAMMP) in Bangladesh, with a focus on understanding adoption levels, barriers, and enabling factors among trained and untrained men and women. The research aimed to provide evidence-based insights to enhance adoption rates and address gender disparities in agricultural machinery manufacturing in the Agricultural-Based Light Engineering (ABLE) sector.15 page