Sorghum plays an important role in the mixed crop–livestock system of tribal farming communities in Adilabad District, a high climate risk-prone region in India. Currently, the local seed system is limited to landraces and hybrids that are primarily used for domestic grain and fodder purposes. This study aimed to understand the farmers' needs and context, and use this knowledge to deliver relevant, adoptable climate-smart sorghum crop technologies through farmer-participatory approaches (FPAs). We conducted an ex-ante survey with 103 farmer households to understand their preferences and constraints concerning sorghum, their staple food-crop. Farmers expressed taste as the most important characteristic, followed by stover yield, grain yield, drought adaptation, and pest resistance. They identified fodder deficit, loss of seed purity in landraces, and lack of diverse sorghum seed options as critical constraints. Therefore, we chose dual-purpose, open-pollinated sorghum varieties suitable for postrainy/rabi cultivation as the study site's entry point. Accordingly, sixteen popular rabi sorghum varieties were tested at ICRISAT station (2017–18 and 2018–19) for agronomic performance in field conditions under a range of treatments (irrigation and fertilization). The standing crop was also scored by farmer representatives. Additionally, the detailed lysifield study elucidated the plant functions underlying the crop agronomic performance under water stress (plant water use and stay-green score) and an important trait of farmer's interest (relation between stay-green score and in-vitro stover digestibility and relation between grain fat and protein content) The selected varieties– Phule Chitra, CSV22, M35-1 and preferred landrace (Sevata jonna)–were further tested with 21 farmers at Adilabad (2018–20). Participating farmers from both the trials and focus group discussions voiced their preference and willingness to adopt Phule Chitra and CSV22. This article summarizes how system-relevant crop options were selected for subsistence farmers of Adilabad and deployed using participatory approaches. While varieties are developed for wider adoption, farmers adopt only those suitable for their farm, household, and accessible market. Therefore, we strongly advocate FPA for developing and delivering farmer relevant crop technologies as a vehicle to systematically break crop adoption barriers and create a positive impact on household diets, well-being, and livelihoods, especially for smallholder subsistence farmers.

Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.