Soil nitrogen (N) is the main limiting nutrient for plant growth, which is sensitive to variations in the soil oxygen environment.  To provide insights into plant N accumulation and yield under aerated and drip irrigation, a greenhouse tomato experiment was conducted with six treatments, including three fertilization types: inorganic fertilizer (NPK); organic fertilizer (OM); chemical (75% of applied N)+organic fertilizer (25%) (NPK+OM) under drip irrigation (DI) and aerated irrigation (AI) methods.  Under AI, total soil carbon mineralization (C_min) was significantly higher (by 5.7–7.0%) than under DI irrigation.  C_min in the fertilizer treatments followed the order NPK+OM>OM>NPK under both AI and DI.  Potentially mineralizable C (C₀) and N (N₀) was greater under AI than under DI.  Gross N mineralization, gross nitrification, and NH₄⁺ immobilization rates were significantly higher under the AINPK treatment than the DINPK treatment by 2.58–3.27-, 1.25–1.44-, and 1–1.26-fold, respectively.  These findings demonstrated that AI and the addition of organic fertilizer accelerated the turnover of soil organic matter and N transformation processes, thereby enhancing N availability.  Moreover, the combination of AI and organic fertilizer application was found to promote root growth (8.4–10.6%), increase the duration of the period of rapid N accumulation (ΔT), and increase the maximum N accumulation rate (V_max), subsequently encouraging aboveground dry matter accumulation.  Consequently, the AI treatment yield was significantly greater (by 6.3–12.4%) than under the DI treatment.  Further, N partial factor productivity (NPFP) and N harvest index (NHI) were greater under AI than under DI, by 6.3 to 12.4%, and 4.6 to 8.1%, respectively.  The rankings of yield and NPFP remained consistent, with NPK+OM>OM>NPK under both AI and DI treatments.  These results highlighted the positive impacts of AI and organic fertilizer application on soil N availability, N uptake, and overall crop yield in tomato.  The optimal management measure was identified as the AINPK+OM treatment, which led to more efficient N management, better crop growth, higher yield, and more sustainable agricultural practices.

Phosphorus (P) is a finite natural resource and is increasingly considered to be a challenge for global sustainability. Agriculture in China plays a key role in global sustainable P management. Rhizosphere and soil-based P management are necessary for improving P-use efficiency and crop productivity in intensive agriculture in China. A previous study has shown that the future demand for phosphate fertilizer by China estimated by the LePA model (legacy phosphorus assessment model) can be greatly reduced by soil-based P management (the building-up and maintenance approach). The present study used the LePA model to predict the phosphate demand by China through combined rhizosphere and soil-based P management at county scale under four P fertilizer scenarios: (1) same P application rate as in 2012; (2) rate maintained same as 2012 in low-P counties or no P fertilizer applied in high-P counties until targeted soil Olsen-P (TPOlsen) level is reached, and then rate was the same as P-removed at harvest; (3) rate in each county decreased to 1–7 kg ha^–1 yr^–1 after TPOlsen is reached in low-P counties, then increased by 0.1–9 kg ha^–1 yr^–1 until equal to P-removal; (4) rate maintained same as 2012 in low-P counties until TPOlsen is reached and then equaled to P-removal, while the rate in high-P counties is decreased to 1–7 kg ha^–1 yr^–1 until TPOlsen is reached and then increased by 0.1–9 kg ha^–1 yr^–1 until equal to P-removal. Our predictions showed that the total demand for P fertilizer by whole China was 693 Mt P2O5 and according to scenario 4, P fertilizer could be reduced by 57.5% compared with farmer current practice, during the period 2013–2080. The model showed that rhizosphere P management led to a further 8.0% decrease in P fertilizer use compared with soil-based P management. The average soil Olsen-P level in China only needs to be maintained at 17 mg kg^–1 to achieve high crop yields. Our results provide a firm basis for government to issue-relevant policies for sustainable P management in China.

Information about disease management in winter wheat (Triticum aestiva) in eight European countries was collated and analysed by scientists and extension workers within the European Network for the Durable Exploitation of Crop Protection Strategies (ENDURE). This included information about specific disease thresholds, decision support systems, host varieties, disease prevalence and pathogen virulence. Major differences in disease prevalence and economic importance were observed. Septoria tritici blotch (Mycosphaerella graminicola) was recognized as the most yield reducing disease in countries with intensive wheat production, but also rust diseases (Puccinia striiformis and Puccinia triticina), powdery mildew (Blumeria graminis) and Fusarium head blight (Fusarium spp.) were seen as serious disease problems. Examples of current integrated pest management (IPM) strategies in different countries have been reported. Disease management and fungicide use patterns showed major differences, with an average input equivalent to 2.3 full dose rates (TFI) in the UK and a TFI of 0.6 in Denmark. These differences are most likely due to a combination of different cropping systems, climatic differences, disease prevalence, and socio-economic factors. The web based information platform www.eurowheat.org was used for dissemination of information and results including information on control thresholds, cultural practices which can influence disease attack, fungicide efficacy, fungicide resistance, and pathogen virulence, which are all elements supporting IPM for disease control in wheat. The platform is open to all users. The target groups of EuroWheat information are researchers, advisors, breeders, and similar partners dealing with disease management in wheat.

According to the contemporary ecosystem approach, the linkages of human actions with their environment have to be assessed in an integrative manner. The Driver-Pressure-State-Impact-Response (DPSIR) model is applied to identify and describe processes and interactions in human-environmental systems. An example application from a research project dealing with the development of sustainable management strategies for the agriculture in Jiangsu, China, illustrates the potentials and limitations of its sustainable development. The concept and indicators of ecological integrity are used to assess the indicators in the dimensions of DPSIR between 2003 and 2006. The main drivers included population growth which caused increasing demand for food, growing environmental demands, and rapidly decreasing of land and other natural resources. The main environmental problem was water pollution. The results show that in the dimension of driver, total grain output and agricultural land productivity both increased. Labor intensive agriculture has been promoted to increase agricultural land productivity. In the dimension of pressure, on the positive side, infrastructure got greatly improved, the input level such as total power of machinery, and level of fertilizer use increased, and level of pesticides use decreased, but on the negative side, cultivated land per capita and irrigation rate decreased, natural resources keep decreased. Environmental pollution indicators such as industrial wastewater discharge and acid rain rate increased in Jiangsu Province. In the aspect of state, ecosystem state was improved, plant coverage index increased, biological abundance index increased, fertilizer productivity increased, eco-environmental quality index increased, but land degradation index also increased. In the aspect of impact, output level increased, output efficiency enhanced, farmer’s social economic benefit improved. In the aspect of response, social support was greatly improved, input for environmental governance increased. To assess the effects of environmental governance, Jiangsu government was successful to increase compliance rate of sulfur dioxide emissions, but not so efficient in compliance rate of industrial wastewater discharge.

Computer vision is widely recognized as an influential technology in the field of precision management of animals. Emerging studies have demonstrated the potential to improve pig health and welfare through animal surveillance systems and computer vision (CV) algorithms. However, the lack of benchmark datasets and robust fundamental algorithms restrict CV applications for the commercial use. This study aims to bridge the gap between technology development and commercial applications in pig farming scenarios by introducing a general-purpose dataset (PigLife), comparing benchmark performances of foundational CV algorithms and model development workflows. The PigLife dataset contains video clips and images (38 short video clips, 2K image frames, 22K pig instances) across most pig production phases in a typical commercial pig farm: Breeding and Gestation, Farrow to Wean, Weaning & Nursery, and Growth to Finish. Three detection algorithms (Faster R-CNN, RetinaNet, TridentNet) and three segmentation algorithms (Mask R-CNN, MViTv2, Point-Rend) were trained on the PigLife dataset from scratch. Fine-tuning of pre-trained models (YOLO8-m, Faster-RCNN-r50) and no-training from zero-shot models (CLIP-SAM, Grouddino-HQSAM) were also evaluated to suggest faster CV development workflows for commercial applications in pig farming. This study emphasizes the necessity of a benchmark dataset for evaluating the robustness of algorithms and identifying the remaining difficulties and challenges across various algorithms. Furthermore, developing CV models from pre-trained algorithms or zero-shot models showed better performance and a faster process, which could reduce barriers when developing high-performance CV products in pig production industry

Nitrogen use efficiency in rice is lower than in upland crops, likely due to differences in soil nitrogen dynamics and crop nitrogen preferences. However, the specific nitrogen dynamics in paddy and upland systems and their impact on crop nitrogen uptake remain poorly understood. The N dynamics and impact on crop N uptake determine the downstream environmental pollution from nitrogen fertilizer. To address this poor understanding, we analyzed 2,044 observations of gross nitrogen transformation rates in soils from 136 studies to examine nitrogen dynamics in both systems and their effects on nitrogen uptake in rice and upland crops. Our findings revealed that nitrogen mineralization and autotrophic nitrification rates are lower in paddies than in upland soil, while dissimilatory nitrate reduction to ammonium is higher in paddies, these differences being driven by flooding and lower total nitrogen content in paddies. Rice exhibited higher ammonium uptake, while upland crops had over twice the nitrate uptake. Autotrophic nitrification stimulated by pH reduced rice nitrogen uptake, while heterotrophic nitrification enhanced nitrogen uptake of upland crops. Autotrophic nitrification played a key role in regulating the ammonium-to-nitrate ratio in soils, which further affected the balance of plant nitrogen uptake. These results highlight the need to align soil nitrogen dynamics with crop nitrogen preferences to maximize plant maximize productivity and reduce reactive nitrogen pollution.