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Determining the amount of nitrogen (N) and phosphorus (P) discharged into lakes and assessing the effectiveness of reduction measures in a basin that involve the interaction of multiple factors are still daunting challenges. In this study, the random forest (RF) model was employed to simulate the impact of controlling measures on the amount of N and P discharged in 2025 under seven specific reduction scenarios. Discharged N and P decreased in the basin by 23.38% and 31.69% from 2011 to 2020, respectively. The N and P nutrient discharge intensities were significantly higher in the western part of the basin (13.31 kg·ha−1·a−1 and 1.34 kg·ha−1·a−1) than those in the eastern region (10.24 kg·ha−1·a−1 and 0.74 kg·ha−1·a−1). Fertilizer runoff (N: 34.72%, 5934.49 t; P: 13.60%, 199.76 t), domestic sewage (N: 29.14%, 4009.27 t; P: 34.84%, 496.59 t), and livestock farming (N: 15.11%, 2657.50 t; P: 40.05%, 616.05 t) were the key sources of N and P. The RF model shows that (R2 > 0.994, p < 0.01) the multi-factor reduction effect is the best, and under this discharge reduction effect, the amount of N and P discharged in 2025 are expected to decrease by 13.79% and 19.42%, respectively, compared with those in 2020. In addition, different key sources in sub-basins might lead to regional differences in the discharge reduction effects of various measures. Ultimately, we recommend that the synergistic treatment of point and non-point sources, using treatments with multiple measures, should be implemented in different regions to reduce the amount of N and P discharged in the Chaohu Lake Basin. [ABSTRACT FROM AUTHOR]

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios. [ABSTRACT FROM AUTHOR]

As global warming continues, heat stress events are expected to increase and negatively affect rice production. Spatiotemporal changes in single-season rice exposure to heat stress were explored along the middle and lower reaches of the Yangtze River (MLRYR) in China during 1971–2020 based on created heat thresholds in different phenological stages, derived from comparison of historical heat records for single-season rice and agro-meteorological data. The feature importance (IF) provided by the Random Forest model was used to modulate the relationship between threshold accumulated temperature and yield reduction rate caused by heat stress. In addition, critical temperature thresholds at different phenological stages were determined by combining Overall Accuracy and the Receiver Operating Characteristic (ROC) curve. According to historical disaster records, the heat stress occurred before the reproductive phase (i.e., the tillering–jointing stage) and ended in the filling stage. Critical temperature thresholds of Tmax at tillering–jointing, booting, flowering and filling were quantified as 36, 35, 35 and 38 °C, with higher IF values of 13.14, 10.93, 17.15 and 13.15, respectively. The respective values of Overall Accuracy and the areas under the ROC curve were greater than 0.85 and 0.930, implying that each threshold performed excellently in identifying heat occurrence. Based on the determined critical thresholds, accumulated harmful temperature (Tcum), number of heat days (HD), first heat date (FHD) and last heat date (LHD) were presented to characterize heat exposure. It was clear that Tcum and HD exhibited a north-to-south increasing trend from 1971 to 2020, with the obvious increasing occurrence in most parts of the study region through the period of 2010 to 2020. FHD occurred earlier in most stations except the northeast parts, while LHD ended later in southern MLRYR. Exploring heat critical thresholds at different phenological stages highlighted in this study can help decision-makers monitor and evaluate heat exposure to single-season rice in MLRYR and further develop mitigation strategies to ensure rice production security. [ABSTRACT FROM AUTHOR]

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant's continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah. [ABSTRACT FROM AUTHOR]

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large amount of high spatiotemporal resolution remote sensing data, different data sources are expected to increase the frequency of data acquisition, which can provide more information in the early season. To explore the potential of integrating different data sources, a Dual-1DCNN algorithm was built based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtain the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2) attained in 2019. To verify this method, the classical methods support vector machine (SVM), random forest (RF), and Mono-1DCNN were implemented. The input for SVM and RF was S1A and S2 data, and the input for Mono-1DCNN was S2 data. The results demonstrated the following: (1) Dual-1DCNN achieved an overall accuracy above 85% at the earliest time.; (2) all four types of models achieved high accuracy (F1s were greater than 90%) on summer maize after sowing one month later; (3) for cotton and common yam rhizomes, Dual-1DCNN performed best, with its F1 reaching 85% within 2 months after cotton sowing, 15 days, 20 days, and 45 days ahead of Mono-1DCNN, SVM, and RF, respectively, and its extraction of the common yam rhizome was achieved 1–2 months earlier than other methods within the acceptable accuracy. These results confirmed that Dual-1DCNN offered significant potential in the in-season detection of crop types. [ABSTRACT FROM AUTHOR]

Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies. [ABSTRACT FROM AUTHOR]

Selenium (Se) is a beneficial microelement for humans, and its varying abundances and shortages have attracted widespread concern. The accumulation process of soil Se is quite complicated, being controlled by multiple factors. However, the influence mechanism of soil properties, climate, and topographic conditions on Se distribution is still unclear in Se-deficient areas. For this study, we collected 2804 samples from cropland soil to assess the levels of Se and the factors that influence those levels in Se-deficient areas of southwestern China. The Se content in this area (0.17 mg/kg) was less than the mean value of China as a whole (0.29 mg/kg). Moran's I index and a random forest (RF) model showed that higher Se levels were mostly observed in the southern and northern sections of the area we studied. The RF model had excellent performance in predicting soil Se content, with an accuracy of 64%. The use of Shapley additive explanations indicated that soil organic matter (SOM) and mean annual precipitation (MAP) were the critical factors determining Se distribution. The areas with high SOM and MAP showed high Se levels. The information obtained from this work can provide guidance for agricultural planning in Se-deficient areas. [ABSTRACT FROM AUTHOR]

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study, winter wheat in the Guanzhong Plain area of the Shaanxi Province, China, was selected as the research subject to explore the feasibility of canopy spectral transformation (CST) combined with a machine learning method to estimate CCC. A hyperspectral canopy ground dataset in situ was measured to construct CCC prediction models for winter wheat over three growth seasons from 2014 to 2017. Sensitive-band reflectance (SR) and narrow-band spectral index (NSI) were established based on the original spectrum (OS) and CSTs, including the first derivative spectrum (FDS) and continuum removal spectrum (CRS). Winter wheat CCC estimation models were constructed using univariate regression, partial least squares (PLS) regression, and random forest (RF) regression based on SR and NSI. The results demonstrated the reliability of CST combined with the machine learning method to estimate winter wheat CCC. First, compared with OS-SR (683 nm), FDS-SR (630 nm) and CRS-SR (699 nm) had a larger correlation coefficient between canopy reflectance and CCC; secondly, among the parametric regression methods, the univariate regression method with CRS-NDSI as the independent variable achieved satisfactory results in estimating the CCC of winter wheat; thirdly, as a machine learning regression method, RF regression combined with multiple independent variables had the best winter wheat CCC estimation accuracy (the determination coefficient of the validation set (Rv2) was 0.88, the RMSE of the validation set (RMSEv) was 3.35 and relative prediction deviation (RPD) was 2.88). Thus, this modeling method could be used as a basic method to predict the CCC of winter wheat in the Guanzhong Plain area. [ABSTRACT FROM AUTHOR]

Wheat can be cultivated by hole sowing, but its border effect has not yet been studied. Therefore, we carried out a field experiment from 2021 to 2022 at the Doukou Crop Experimental Demonstration Station (108°52′ E, 34°37′ N) of Northwest A&F University in Jingyang County, Xianyang City, Shaanxi Province, China. The response of dry matter, photosynthetic characteristics, and yield components of wheat to the border effects under the hole sowing method was studied. The results showed specific border effects on each index of five wheat varieties (XN136, XN175, XN527, XN536, and XN765), among which the border effects of XN175 and XN765 were the most significant, with the highest yield. Subsequent correlation analysis revealed that only grain per spike and intercellular carbon dioxide concentration responded negatively to the border effects, and the rest were positively correlated. Finally, we conducted a random forest model analysis of different indicators of wheat varieties with significant border effects. We found that net photosynthetic rate and aboveground dry matter per plant had the most significant impact and contribution to the border effects. In contrast, grain per spike had the most negligible impact on the border effects. Our results fill a gap in the study of the border effects of wheat under hole sowing cultivation for future researchers. [ABSTRACT FROM AUTHOR]

Soil cation exchange property (SCEP) is important in soil development and environmental buffering. However, the variations in SCEP and its correlation with soil-forming factors in subsurface soil are not fully understood. In this study, we quantified the surface and subsurface SCEP variations as a function of parent material, vegetation, hillslope position, and soil type. Fifty upland soil profiles from mid-subtropical China were selected. The cation exchange capacity (CEC) and effective CEC (ECEC) of subsurface soil were significantly higher in soils derived from slate, Quaternary red clay (QRC), and limestone than in soils derived from granite and sandstone. The subsurface soils derived from limestone had the highest base saturation (BS) and the sum of base cations, and the lowest aluminum (Al) saturation. The SCEP in surface soil significantly varied with vegetation and hillslope position. The surface soil CEC was the highest in mixed-forest vegetation, whereas the ECEC and exchangeable acidity (EA) were the highest in arable vegetation. Exchangeable potassium (K+) was lowest and the EA was highest in soil orders at the strong development phase. Exchangeable calcium (Ca2+), magnesium (Mg2+), CEC, and BS were the highest in soil orders at the intermediate development phase. The prediction accuracy of soil CEC using the random forest model was higher than that obtained using multiple stepwise regression, with the best results (R2 = 0.92) obtained in the surface soil. Our study indicated that the SCEP in surface and subsurface soils was controlled by different soil-forming factors and could be effectively predicted by soil properties in subtropical China. [ABSTRACT FROM AUTHOR]

Suitability evaluation of tea cultivation is very important for improving the yield and quality of tea, which can avoid blind expansion and achieve sustainable development; however, to date, relevant research at town and village scales is lacking. This study selected Xinming Township in Huangshan City, Anhui Province, as the study area, which is the main production area of Taiping Houkui Tea—one of the ten most famous teas in China. We proposed a machine learning-based tea cultivation suitability evaluation model by comparing logistic regression (LR), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), random forest (RF), Gaussian Naïve Bayes (GNB), and multilayer perceptron (MLP) to calculate the weight accuracy of the evaluation factors. We then selected 12 factors, including climate, soil, terrain, and ecological economy factors, using the RF with the highest accuracy to calculate the evaluation factor weights and obtained the suitability evaluation results. The results show that the highly suitable area, moderately suitable area, generally suitable area, and unsuitable area land categories for tea cultivation were 14.13%, 27.25%, 32.46%, and 26.16%, respectively. Combined with field research, the highly suitable areas were mainly distributed in northwest Xinming Town, which is in line with the distribution of tea cultivation at the Xinming township level. The results provide a scientific reference to support land allocation decisions for tea cultivation and sustainable green agricultural development at the town and village scales. [ABSTRACT FROM AUTHOR]

Accurate and timely information on crop planting patterns is crucial for research on sustainable agriculture, regional resources, and food security. However, existing spatial datasets have few high-precision and wide-range planting pattern maps. The production may be limited by the unbalanced spatiotemporal resolution, insufficient massive field sample data, low local computer processing speed, and other factors. To overcome these limitations, we proposed semi-automatic expansion and spatiotemporal migration strategies for sample points and performed a pixel-and-phenology-based random forest algorithm on the Google Earth Engine platform to generate crop planting pattern maps at high spatiotemporal resolution by integrating Landsat-8 and Sentinel-2 time series image data. In this study, we report planting pattern maps for 2017–2021 at a 10-m spatial resolution of the Jianghan Plain, including six crops and nine planting patterns, with an overall accuracy of 84–94% and a kappa coefficient of 0.80–0.93. The spatiotemporal distribution is driven by multiple factors, such as subjectivity and social economy. This research indicates that the proposed approach is effective for mapping large-scale planting patterns and can be readily applied to other regions. [ABSTRACT FROM AUTHOR]