Publisher Correction: The transformation of agriculture towards a silicon improved sustainable and resilient crop production
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Climate change threatens crop diversity at low latitudes
Climate change alters the climatic suitability of croplands, likely shifting the spatial distribution and diversity of global food crop production. Analyses of future potential food crop diversity have been limited to a small number of crops. Here we project geographical shifts in the climatic niches of 30 major food crops under 1.5–4 °C global warming and assess their impact on current crop production and potential food crop diversity across global croplands. We found that in low-latitude regions, 10–31% of current production would shift outside the climatic niche even under 2 °C global warming, increasing to 20–48% under 3 °C warming. Concurrently, potential food crop diversity would decline on 52% (+2 °C) and 56% (+3 °C) of global cropland. However, potential diversity would increase in mid to high latitudes, offering opportunities for climate change adaptation. These results highlight substantial latitudinal differences in the adaptation potential and vulnerability of the global food system under global warming.
Advancing transnational assessments of biodiversity drivers in European agriculture with an updated hierarchical crop and agriculture taxonomy (HCAT)
By homogenizing landscapes and reducing natural habitats, modern agriculture plays a significant role in reducing natural species populations worldwide. Despite advances in research, quantifying the impacts of cropping systems on biodiversity remains challenging due to the lack of comprehensive agricultural data. Within the European Union’s (EU) common agricultural policy (CAP), farmers are required to declare cropping arrangements to receive subsidies. The resulting data is collected by each EU member state individually, leading to inconsistent crop taxonomies across the EU, which hinders transnational analyses of agriculture and related impacts. To overcome this barrier, we developed a hierarchical crop and agriculture taxonomy (HCAT), which harmonizes administrative and agricultural data from 16 EU member states. With the release of an upgraded second version of HCAT, we demonstrate, using the example of biodiversity drivers, how a harmonized CAP data set can aid in identifying indicators related to environmental impacts in agricultural landscapes at international scales.
Enhancing precision agriculture through cloud based transformative crop recommendation model
Modern agriculture relies more on technology to boost food production. It aims to improve both the quality and quantity of food. This paper introduces a novel TCRM (Transformative Crop Recommendation Model). It uses advanced machine learning and cloud platforms to give personalized crop recommendations. Unlike traditional methods, TCRM uses real-time data. It includes environmental and agronomic factors to optimize recommendations. The system has SMS alerts for remote farmers. It outperforms baseline algorithms like Logistic Regression, KNN(k-nearest neighbor), and AdaBoost. TCRM empowers farmers with actionable insights, reducing resource wastage while boosting yield. By offering region-specific recommendations, it enhances profitability and promotes sustainable agricultural practices. The model has 94% accuracy, 94.46% precision, and 94% recall. Its F1 score is 93.97%. The fivefold cross-validation score is 97.67%. These findings show that the model can improve precision farming. It can make agriculture more sustainable and efficient.
Crop-specific embodied greenhouse gas emissions inventory for 28 staple crops in China from 2007 to 2017
Rapid economic development and population growth have driven significant greenhouse gas (GHG) emissions from China’s crop farming. Understanding specific features of these emissions is crucial for developing effective mitigation strategies. While existing studies primarily focused on accounting for GHG emissions at the entire crop farming system level, a critical gap exists in systematic measurements at individual crop level. This study addresses this gap by constructing a high-resolution China’s provincial crop-specific embodied GHG emission inventory for years 2007, 2010, 2012, 2015, and 2017. The inventory quantifies embodied GHG emissions per unit yield and per unit area for 28 staple crops across 30 Chinese provinces, providing insights into status and structure of emissions across diverse crops and regions. The results demonstrate significant disparities in crop-specific embodied GHG emissions, with grain crops exhibiting higher emissions than cash crops—1.51 times greater per unit area and 0.86 times greater per unit yield on average. This dataset offers information for formulating effective emission mitigation strategies for crop farming in China.
Legacy effects of crop diversity on weed-crop competition in maize production
The legacy effects of crop diversity on maize (Zea mays L.) tissue nutrient composition, weed community structure, and intensity of weed-crop competition were assessed through a field experiment at two sites in the northeastern United States. Fields were conditioned with crop diversity gradients from summer 2016 to spring 2019. The crop diversity gradients ranged from a single cultivar to sixteen intercropped cultivars (four species, four cultivars per species) and were established in organic annual and perennial cropping systems. Following the three-year conditioning phase, maize was planted across the entire experiment, and each conditioning-phase diversity treatment was split into weed-free, ambient-weed, moderate-weed, and heavy-weed treatments. Within each cropping system, the effect of crop diversity legacy on weed-crop competition was negligible. In contrast, weed-crop competition varied between the maize grown in soil conditioned by the annual and perennial cropping systems.
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