Andrew James McDonald

Cultivar evolution through plant breeding is a cornerstone of contemporary food security, but the extent to which genetic adaptation to climatic variability and shocks contributes to yield gains is not well known. Here, we compile 48,797 cultivar-site-year observations from 2001 to 2020, covering the four prominent maize production regions in China with differing shifts in climatic conditions. The data shows that cultivar evolution underlies long-term yield gains, with productivity increasing by 0.3-2.8 Mg ha^-1 per decade. Yields in Northeast China (NEC) and North China (NC) are most vulnerable to heat stress during July and August, whereas high or insufficient precipitation during the growing season is a foremost constraint to yield gains in Southwest China (SWC) and Northwest China (NWC), respectively. Cultivar evolution has significant impacts on yield sensitivity to climate, with genotypic sensitivities to heat stress amplifying in NEC and diminishing over time in NC, respectively. In contrast, yield sensitivity to precipitation increases in SWC and NWC as a result of breeding. These results underscore the importance of breeding climate-resilient cultivars that account for contextualised in situ environmental constraints and climatic adversities in obtaining high yield.

Wheat ( Triticum aestivum L.) is a major winter season crop and a primary source of food and income for farmers in eastern India. Inadequate irrigation of wheat in this area limits its production potential. Our study evaluates the significance of one additional irrigation (supplemental) to wheat. The study presents large-scale multi-location agronomic field/on-farm trial data of supplemental irrigation conducted over six consecutive years in eastern India. The extensive agricultural experiment covered 1810 farmers’ fields spanning five agro-climatic zones. The objective was to validate the contribution of supplemental irrigation applied to wheat at the grain-filling stage in supporting wheat grain yield. The on-farm experiment was structured as a split-plot design, with the additional irrigation (Treatment) randomly assigned to sub-plots. All field locations were geo-tagged and grain yields were recorded using physical crop cuts. The data was carefully curated, anonymized, and released through a Dataverse portal making it reusable and interoperable. The dataset is invaluable in understanding sustainable wheat production in regions where wheat is inadequately irrigated.

Limiting nitrogen pollution from crop production is essential for mitigating greenhouse gas emissions and protecting aquatic ecosystems while maintaining food security. Precision nitrogen management (PNM) provides a conceptual framework for achieving yield goals while maintaining nitrogen pollution within planetary boundaries by matching fertilizer rates to specific production conditions. Nevertheless, PNM strategies for smallholder contexts like India, a global nitrogen pollution hotspot, have proven costly to implement and are often ineffective. By combining survey data of production practices from 8705 wheat fields with digital soil mapping, we develop a novel PNM strategy that ‘learns from landscapes’ to generate and evaluate novel decision logic for nitrogen management. With this approach, ex-ante simulations indicate that reductions of 9% in nitrogen use and 16% in N ₂ O emissions can be achieved without compromising yields, saving US$ 28 million per year in subsidies for the Indian state of Bihar alone. In contrast, conventional soil test-based recommendations may increase nitrogen use by 5% without corresponding yield gains. Our method that leverages large- n survey data and predictive modeling may provide a scalable pathway for PNM in similarly complex crop production environments where field and management heterogeneity is high.

Rice provides a foundation for global food security with emerging evidence suggesting that meeting future production demands will require increasing irrigation use in regions such as South Asia and Sub-Saharan Africa where untapped yield potential is high. Consequently, increasing water use in rice may be an unforeseen driver of higher global methane emissions. Here we use Eastern India as an exemplar region to estimate yield and greenhouse gas (GHG) implications of irrigation-led intensification with a model-based data assimilation and scenario approach. Results suggest that if intensification (ca. 2 t ha^-1) is achieved with continuous flood irrigation, current baseline emissions (4.1 t CO₂-eq ha^-1) are anticipated to double (8.8 t CO₂-eq ha^-1) with more moderate increases predicted with precision irrigation practices such as alternate wetting and drying (7.0 t CO₂-eq ha^-1). Robust strategies for achieving effective water management are needed to avoid the most damaging climate impacts of rice intensification.

Increasing nitrogen use efficiency (NUE) in agricultural production mitigates climate change, limits water pollution and reduces fertilizer subsidy costs. Nevertheless, strategies for increasing NUE without jeopardizing food security are uncertain in globally important cropping systems. Here we analyse a novel dataset of more than 31,000 farmer fields spanning the Terai of Nepal, Bangladesh’s floodplains and four major rice-producing regions of India. Results indicate that 55% of rice farmers overuse nitrogen fertilizer, and hence the region could save 18 kg of nitrogen per hectare without compromising rice yield. Disincentivizing this excess nitrogen application presents the most impactful pathway for increasing NUE. Addressing yield constraints unrelated to crop nutrition can also improve NUE, most promisingly through earlier transplanting and improving water management, and this secondary pathway was overlooked in the IPCC’s 2022 report on climate change mitigation. Combining nitrogen input reduction with changes to agronomic management could increase rice production in South Asia by 8% while reducing environmental pollution from nitrogen fertilizer, measured as nitrogen surplus, by 36%. Even so, opportunities to improve NUE vary within South Asia, which necessitates sub-regional strategies for sustainable nitrogen management.

Far removed from the agricultural fire “hotspots” of Northwestern India, rice residue burning is on the rise in Eastern India with implications for regional air quality and agricultural sustainability. The underlying drivers contributing to the increase in burning have been linked to the adoption of mechanized (combine) harvesting but, in general, are inadequately understood. We hypothesize that the adoption of burning as a management practice results from a set of socio-technical interactions rather than emerging from a single factor. Using a mixed methods approach, a household survey (n = 475) provided quantitative insights into landscape and farm-scale drivers of burning and was complemented by an in-depth qualitative survey (n = 36) to characterize decision processes and to verify causal inferences derived from the broader survey. For communities where the combine harvester is present, our results show that rice residue burning is not inevitable. The decision to burn appears to emerge from a cascading sequence of events, starting with the following: (1) decreasing household labor, leading to (2) decreasing household livestock holdings, resulting in (3) reduced demands for residue fodder, incentivizing (4) adoption of labor-efficient combine harvesting and subsequent burning of loose residues that are both difficult to collect and of lower feeding value than manually harvested straw. Local demand for crop residues for livestock feeding plays a central role mediating transitions to burning. Consequently, policy response options that only consider the role of the combine harvester are likely to be ineffective. Innovative strategies such as the creation of decentralized commercial models for dairy value chains may bolster local residue demand by addressing household-scale labor bottlenecks to maintaining livestock. Secondary issues, such as timely rice planting, merit consideration as part of holistic responses to “bend” agricultural burning trajectories in Eastern India towards more sustainable practices.

Yield gap analysis is used to characterize the untapped production potential of cropping systems. With emerging large-n agronomic datasets and data science methods, pathways for narrowing yield gaps can be identified that provide actionable insights into where and how cropping systems can be sustainably intensified. Here we characterize the contributing factors to rice yield gaps across seven Indian states, with a case study region used to assess the power of intervention targeting. Primary yield constraints in the case study region were nitrogen and irrigation, but scenario analysis suggests modest average yield gains with universal adoption of higher nitrogen rates. When nitrogen limited fields are targeted for practice change (47% of the sample), yield gains are predicted to double. When nitrogen and irrigation co-limitations are targeted (20% of the sample), yield gains more than tripled. Results suggest that analytics-led strategies for crop intensification can generate transformative advances in productivity, profitability, and environmental outcomes.