Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed.

Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties and using their resistance genes in breeding is an effective and economical control measure. Accurate severity estimation of this disease is crucial for breeding resistant cotton varieties. However, current methods fall short, slowing the breeding process. To address these challenges, this paper introduces CVW-Etr, a high-precision method for estimating the severity of cotton verticillium wilt. CVW-Etr classifies severity into six levels (L0 to L5) based on the proportion of segmented diseased leaves to lesions. Upon integrating YOLOv8-Seg with MobileSAM, CVW-Etr demonstrates excellent performance and efficiency with limited samples in complex field conditions. It incorporates the RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, and GSegment modules to handle blurry transitions between healthy and diseased regions and variations in angle and distance during image collection, and to optimize the model’s parameter size and computational complexity. Our experimental results show that CVW-Etr effectively segments diseased leaves and lesions, achieving a mean average precision (mAP) of 92.90% and an average severity estimation accuracy of 92.92% with only 2.6M parameters and 10.1G FLOPS. Through experiments, CVW-Etr proves robust in estimating cotton verticillium wilt severity, offering valuable insights for disease-resistant cotton breeding applications.

Agricultural weather index insurance overcomes the technology and management problems existing in traditional agricultural insurance, and can effectively transfer agricultural meteorological disasters risk, providing strong guarantee for farmers with small production scale and scattered land. Although weather index insurance has many advantages, governments around the world have made a lot of efforts to expand the coverage, but farmers' demand is still not high. One of the reasons is basis risk. How to reduce it, and improve the operational efficiency of weather index insurance is an important issue that needs to be solved urgently. This paper has summarized researches on the theories and practices of weather index insurance at home and abroad, and systematically comments on the basis risk’s definition, formation mechanism, quantitative methods and management measures. In order to provide useful ideas and references for the development of China's weather index insurance and the future research on basis risk.

A new method, namely, the scenario-based method, is developed and further applied in assessing economic risk of paddy damage caused by floods in the Huai River sub-basin of China following these four steps: (1) generation of scenarios, (2) simulation of scenarios, (3) consequence of scenarios, and (4) comprehension of scenarios. In the first step, a rainfall Intensity-Duration-Frequency (IDF) model is established to generate a set of storm scenarios in the sub-basin by selecting seven return periods. In the second step, the flooding simulation for each scenario is carried out based on the distributed Rainfall-Runoff-Inundation (RRI) model to obtain inundation depth (meter) and time (day) over the sub-basin. In the third step, a grid-based Flood Economic Loss Estimation (FELE) model of paddies is employed to estimate economic losses of paddies in Chinese Yuan (CNY), where inundation depth and duration are treated as parameters for each scenario. In the last step, the Conditioned Expected Losses (CELs) in CNY for each county is derived as a measurement of risk by fitting the risk curve with scenario samples. Based on CELs, an economic risk zoning map of paddy damage caused by floods in the Huai River sub-basin was created as a final result.

Applying county-level crop yield models to individual pixels introduces scale mismatches and aggregation bias. We examined these effects across the winter wheat regions of Henan and Shandong, China, using MODIS gross primary productivity (GPP), daytime and nighttime land surface temperature (LST) time series, and geographic coordinates for 2001–2025. XGBoost and a hybrid convolutional neural network (Hybrid-CNN) were trained using county-level yield statistics for 2001–2020 and independently evaluated on 728 county-year observations for 2021–2024. Three strategies were compared for yield mapping at 500 m resolution: unadjusted pixel predictions, an area-weighted county-level constraint, and quantile mapping (QM) followed by the same constraint. Evaluation covered county-level predictive performance, aggregation bias, within-county variability, and internal consistency with GPP. Validation R² values were 0.749 for XGBoost and 0.770 for Hybrid-CNN but declined to 0.508 and 0.399, respectively, in independent temporal testing, with both models underestimating yield. Aggregating unadjusted pixel predictions to counties produced biases of −1.744 and −0.360 t ha⁻¹, respectively, indicating that county-level predictive accuracy did not directly characterize performance following pixel-level application. Both constrained strategies reduced county-level aggregation residuals to approximately zero. At the same county-level reference yield, adding QM increased the median within-county coefficient of variation (CV) from 2.32% to 10.31% and the median interquartile range (IQR) from 0.187 to 0.826 t ha⁻¹ for XGBoost, representing increases of approximately 344% and 342%, respectively. For Hybrid-CNN, CV increased from 8.95% to 10.66% and IQR from 0.672 to 0.819 t ha⁻¹, corresponding to increases of approximately 19% and 22%. Changes in correlations with GPP remained within ±0.004 for both models. QM thus primarily altered the magnitude of within-county variability, while independent field evidence is still required to determine whether it improves spatial accuracy. The county-constrained strategy provides a baseline for historical yield mapping and regional risk analysis, with QM followed by the county-level constraint serving as a sensitivity comparison. Absolute pixel-level yields, interannual anomalies, and extreme yield-loss signals require further independent validation.