ArticleNature food2024
Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production.
Article in Nature food, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Optimizing water allocation in irrigation districts for enhanced sustainability and climate resilience.Journal of advanced research · 2026Article
- Climate-adaptive and low-carbon multi-objective water resources optimization in arid oasis cities.iScience · 2026Article
- Artificial Intelligence based on behavioral recognition and optimization for low carbon fertilization in agriculture.Scientific reports · 2026Article
- Microplastics and heavy metals dynamics: a potential threat to soil health and food safety.Plant cell reports · 2026Review
- Machine learning-optimized composting strategies can enhance nutrient recycling and transform food system waste into a net carbon sink.Nature food · 2026Article
- Underestimation of Future Agricultural Soil NGlobal change biology · 2026Article
- An Increasing Misalignment Between Crop Distribution and Environmental Resources Under Climate Change in China.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A machine learning-coupled APSIM model pipeline for projected oil palm yield in Surat Thani, Thailand.PloS one · 2026Article
- Application of crop growth models in crop yield assessment.Frontiers in plant science · 2026Review
- Assessing the Alignment Between Naturally Adaptive Grain Crop Planting Patterns and Staple Food Security in China.Foods (Basel, Switzerland) · 2025Article
- Optimization allocation strategy of agricultural production resources based on SSA-BP algorithm.Scientific reports · 2025Article
- Global Crop-Specific Fertilization Dataset from 1961-2019.Scientific data · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
Co-optimization of multiple management practices may facilitate climate-smart agriculture, but is challenged by complex climate-crop-soil management interconnections across space and over time. Here we develop a hybrid approach combining agricultural system modelling, machine learning and life cycle assessment to spatiotemporally co-optimize fertilizer application, irrigation and residue management to achieve yield potential of wheat and maize and minimize greenhouse gas emissions in the North China Plain. We found that the optimal fertilizer application rate and irrigation for the historical period (1995-2014) are lower than local farmers' practices as well as trial-derived recommendations. With the optimized practices, the projected annual requirement of fertilizer, irrigation water and residue inputs across the North China Plain in the period 2051-2070 is reduced by 16% (14-21%) (mean with 95% confidence interval), 19% (7-32%) and 20% (16-26%), respectively, compared with the current supposed optimal management in the historical reference period, with substantial greenhouse gas emission reductions. We demonstrate the potential of spatiotemporal co-optimization of multiple management practices and present digital mapping of management practices as a benchmark for site-specific management across the region.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.