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Mapping grain crop sowing date in smallholder systems using optical imagery
No full textSowing date prediction using Earth observation data is challenging in smallholder systems due to small field sizes, heterogeneity in management practices, and a lack of reference data. This study aims to develop a generalizable algorithm that does not require any ground data for calibration to map sowing date using the Normalized Difference Vegetation Index (NDVI) from three optical datasets: MODIS, Harmonized Landsat and Sentinel (HLS), and Sentinel-2. We applied Savitzky-Golay (SG) and spline smoothing algorithms to each dataset and developed a derivative approach to identify the inflection point that represents the Start of Season (SoS), which was then converted to sowing date. We applied our methodology to map the sowing date of winter wheat in Bihar, India and spring-summer maize in the state of Mexico, Mexico. Overall, Sentinel-2 data led to the highest accuracies, but the performance of the smoothing algorithm differed across locations. In India, prediction models using SG achieved an R2 of 0.45 and a root mean square deviation (RMSD) of 11.44 days. In Mexico, prediction models using spline performed best, with an R2 of 0.19 and an RMSD of 4.24 weeks. The lower accuracy in Mexico was due to more complex cropping patterns as well as noise in the observed sowing date dataset. Our algorithm shows potential to identify SoS, and ultimately sowing date, at scale using Sentinel-2 imagery. However, challenges from low-quality validation datasets, small field sizes, cloud cover, and landscape complexity continue to pose challenges to predict sowing date using Earth observation data products
Improving access to new technologies: An experiment with Kenyan input sellers
No full textSmall and medium enterprises in low income countries are key actors in the introduction and diffusion of new technologies. However, demand uncertainty can discourage small retailers from stocking newer, less familiar products, limiting the availability of innovative technologies and leading to the persistence of outdated but well-established products. In this study, we provided Kenyan agrodealers with a 10% price discount on new, drought tolerant maize hybrids. The discount increased the likelihood that the agrodealer stocked the new hybrids and increased the share of the new hybrids in overall sales. Effects were strongest among risk-averse dealers. The discount encouraged dealers to gather more information about the seeds, resulting in better-informed recommendations to farmers. Although it did not lower retail prices, the discount increased sellers' advisory efforts. Our findings show that modest incentives can shift agrodealer behavior and spur technology diffusion in rural markets
Scaling farmer-centric innovation in Africa: regional TRICOT workshop connects science and practice
No full text6 page
Enhancing wheat resilience in subtropical agroecosystems through climate-resilient agriculture strategies
No full textWheat production in subtropical agroecosystems is increasingly challenged by climate-induced stresses such as lodging, terminal heat, and erratic rainfall patterns. This study was conducted during the 2023–2024 rabi season across eight locations, namely, the Borlaug Institute for South Asia (BISA) Research Station at Pusa and seven project hubs located in the districts of Munger, Nawada, Nalanda, Katihar, Purnea, Samastipur, and Vaishali in Bihar, India, and evaluated climate-resilient agronomic strategies to enhance wheat resilience and productivity. A randomized block design with 20 replications was used to assess the interactive effects of tillage practices [conventional tillage (CT), zero tillage (ZT), and permanent raised bed (PRB)], sowing times (early vs. timely), and wheat varieties (HD2967, DBW187, and DBW316) on crop performance. Results indicated that PRB and ZT strategies significantly (p < 0.05) reduced (80%–90%) risk of lodging and increased (15%–25%) wheat grain yield compared to CT. Furthermore, early sowing (first fortnight of November) and the use of lodging-resilient varieties of HD2967 and DBW187 reduced crop lodging, improved crop performance, and increased wheat grain yield compared to late sowing (second fortnight of November) and the DBW316 variety, respectively. Correlation and regression analysis studies exposed a weak positive correlation between yield and wind speed (r = 0.133) and a stronger positive correlation effect with rainfall (r = 0.342) during early-sown crops, with stepwise regression indicating yield gains of 0.32 t/ha and 1.15 t/ha under optimum wind speed and rainfall, respectively (r = 0.68). In contrast, late sowing exhibited negative correlations, with yield declining by 0.39 and 0.12 t/ha under aberrant wind and rainfall conditions, respectively (r = 0.52). The study emphasized the significance of adopting climate-resilient agronomic strategies, such as ZT, appropriate variety selection (HD2967 and DBW187), and early sowing, to enhance the sustainability and resilience of wheat production under adverse climatic conditions
Tailored framework for sustainable intensification of marginal and small farms using farm typology to strengthen farm income
No full textFarm typology studies assist in understanding how different farming components interact with each other and with the surroundings. These are often a prerequisite before devising adaptations to numerous agri-related challenges and in the development of sustainable agriculture policies. This holds importance for countries where the majority of the workforce is engaged in the agri-sector. The deliberated study investigates the determining factors that characterize the small and marginal farms spread across 16 states of India through typology to understand the limitations and challenges to formulate an alternative framework for leveraging their livelihood through enhanced income. The study clusters the surveyed farms under 06 farm types following a multivariate statistical approach. Further, it recommends alternate farming system models developed at research stations for different agro-climatic zones and the attributes of the respective farm type for the contemplated agroclimate zones. The study further scopes for the increment in annual farm income following the adoption of the recommended models. The results underscore the potential impacts of recommended models on the farmer's livelihoods
Agronomic levers to increase maize and soybean productivity across the Chinyanja Triangle, Southern Africa
No full textCONTEXTMaize is Southern Africa’s staple food crop, while soybean, a multi-purpose legume, is the fastest expanding crop in area and production in the region. Despite their importance, yields remain low, highlighting the need for context-specific strategies to sustainably increase productivity.OBJECTIVEThis study characterized maize and soybean production systems across the Chinyanja Triangle, estimated yield gaps, and identified agronomic levers for yield improvement.METHODSYields were measured using crop-cuts in farmers’ fields in Kasungu and Lilongwe (Malawi), Sinda and Katete (Zambia) and Angonia (Mozambique), alongside a diagnostic survey on crop management practices during the 2022-2023 season. A total of 485 maize and 509 soybean field observations were analyzed, supplemented with secondary climate and soil data, and water-limited yields simulated with the DSSAT crop model. A machine learning approach combining random forest and Shapley values was used to explain yield variability and identify yield constraints.RESULTS AND CONCLUSIONSActual maize yields across districts ranged between 2.2 and 2.6 t ha-1 on average and actual soybean yields between 0.4 and 1.6 t ha-1. Simulated water-limited yields were greater than 8.0 t ha-1 for maize and than 3.5 t ha-1 for soybean. Maize cropping systems were similar across districts, whereas an intensification pathway was found for soybean cropping systems in Malawi, an extensification pathway in Zambia and marginal production pathway in Mozambique. Yield constraints for maize included low plant population and fertilizer management and variety type, while soybean yield constraints hinged around soil fertility, sowing date and variety type.SIGNIFICANCEThe agronomic levers identified can be used to target technology development and prioritization of interventions to increase productivity sustainable in the region. These insights support strategic planning for sustainable intensification and food security across Southern Africa
Optimizing sowing dates to increase maize yield across the Huanghuaihai Plain in China
No full textWith the aim of adapting agricultural practices to climate warming, this study projected sowing dates for summer maize in the 2030s (2031–2040) across the Huanghuaihai Plain by analyzing key photo-thermal variables derived from field experiments and projected future climate data under Shared Socioeconomic Pathway 2–4.5 within a restricted planting season. Results showed that growing degree days (GDD) during the active dry matter accumulation period (AP), killing degree days (KDD) during AP, and GDD during the late dry matter accumulation period (LP) explained most yield variation and were used for determining suitable sowing windows. Thresholds of them were 571 °C d, 21 °C d and 411 °C d, respectively. In the 2030s, postponing sowing dates and shifting planting regions northward resulted in gradual declines in KDD during AP and GDD during LP. The proportion of regions limited by KDD and GDD changed from 66% to 0% and from 3% to 100% when sowing dates were postponed from June 1 to July 15. Suitable sowing dates for maize were determined as follows: June 25 to July 10 in regions south of 34°N, June 5 to June 30 between 34°N and 39°N, and before June 20 in regions north of 39°N