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Exploring the spatiotemporal dynamics, spatial mismatch, and complex influencing mechanism of grain production and cropland productivity in the black soil region of northeast China (BSRNC) is essential for the synergistic protection and utilization of black soil cropland and sustainable grain production. The BSRNC has realized cropland expansion and grain production increases in the past decades. This implied a substantial investment has been made in the region's agriculture. However, at present, knowledge on the spatial mismatch and influencing factors of grain production and cropland productivity is still unclear. This study analyzed the spatial–temporal mismatch characteristics of grain production and cropland net primary productivity (CNPP) using the gravity center model, spatial autocorrelation analysis, and spatial mismatch index (SMI), and identified the spatial heterogeneity and prediction–response relationships of influencing factors based on a geographically and temporally weighted regression (GTWR) model and boosted regression tree (BRT) machine learning algorithm. The findings indicated that grain production and CNPP have been increasing, but the overall spatial pattern of cold hotspots has not changed obviously in the BSRNC from 2000 to 2020. The SMI has shown a decreasing trend, indicating that the synergistic development of grain production and CNPP has been obvious, which plays an important role in sustainable food supply capacity. Agricultural production and the natural environment have always been critical factors influencing the spatial mismatch. Specifically, the marginal impact of fertilizer application has undergone a shift. This study may provide new clues for the formulation of regional strategies for sustainable food supply and black soil cropland system protection. [ABSTRACT FROM AUTHOR]

Excessive nitrogen (N) fertilization harms the diversity, structure, and function of the soil microbiome. Yet, whether such adverse effects can be repaired through reducing the subsequent N fertilization rate remains not completely clear so far. Here, using a long-term N-overfertilized wheat-maize cropping field, we assessed the effect of reducing various proportions of the subsequent N fertilization rate over six years on crop productivity, soil physicochemical and biochemical properties, and microbiome. Five treatments were employed in our field experiment: the farmers' conventional N fertilization rate (zero reduction, as a control) and the reduction in the farmers' N rate by 20%, 40%, 60%, and 100%. The results showed that moderate N reduction (20–40%) enhanced crop productivity and soil fertility but did not affect soil enzyme activity. Soil bacterial and fungal community diversity were insensitive to N fertilization reduction, whereas their community structures changed significantly, with more prominent alteration in the fungal community. Functional prediction indicated that average relative abundance of arbuscular mycorrhizal fungi increased with N fertilization reduction but that of ectomycorrhizal fungi decreased. Moderate N reduction (20–40%) enhanced species interactions and, thus, provided a more complex cross-kingdom microbial co-occurrence network. Both bacterial and fungal community assembly were governed by stochastic processes, and this was not altered by N fertilization reduction. Overall, the response of the soil microbiome to N fertilization reduction was greatly dependent on the reduced N proportion. The findings obtained here shed light on the importance of optimal N fertilization rate in the intensively cultivated, high-input grain production system. [ABSTRACT FROM AUTHOR]

One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model relies on computer vision techniques, including You Only Look Once (YOLO) and Detectron2, and adapts them to lightweight formats—TensorFlow Lite (TFLite) and Open Neural Network Exchange (ONNX)—for resource-constrained devices. Our method leverages two datasets: a comprehensive one and a smaller sample for comparison purposes. With this setup, the authors aimed at using these two datasets to evaluate the performance of the computer vision models and subsequently convert the best-performing models into TFLite and ONNX formats, facilitating their deployment on edge devices. The results are promising. Even in the worst-case scenario, where the ONNX model with the reduced dataset was compared to the YOLOv9-gelan model with the full dataset, the precision reached 87.3%, and the accuracy achieved was 95.0%. [ABSTRACT FROM AUTHOR]

Inner Mongolia Autonomous Region (IMAR) has become the sixth-largest grain-producing province of China, contributing 5.6% of the total national grain output in 2021. Grain production increased by around 821% from 1980 to 2019. The contributions of structure adjustment, sown area, and yield to the increase in grain production was analyzed for IMAR. For yield, the Cobb–Douglas production function was estimated, to analyze the contribution of agricultural inputs (fertilizer, irrigation, seed, pesticide, agriculture film, and machinery operation) and the impact of natural hazards on yield. Results from our study showed that the grain production and yield in IMAR increased by 81.40 × 104 t and 0.11 t·ha−1 annually from 1980 to 2019. The grain-crop sown area increased by 73.65 × 103 ha annually, and the sown area of maize constitutes 16.82% to 55.30% of the total sown area of the IMAR in 1980–2019. In IMAR, sown area, yield, and structure adjustment can explain 54.06%, 33.31%, and 12.63% of the increase in the total grain production, respectively, from 1980 to 2019. The input per unit area of fertilizer, irrigation, and other inputs (seed, pesticide, agricultural film, and mechanical operation inputs) increased by 362.16%, 259.66%, and 405.55%, respectively, from 1981 to 2019, which contributed 160.44%, 15.06%, and 53.13% for the main cereal grain (maize and wheat) yield, respectively, while the decrease in the comprehensive loss rate from agrometeorological hazards contributed 7.76% to the main cereal grain yield in IMAR. Technologies such as water-saving irrigation, high-efficiency fertilizer application, and agrometeorological-hazards mitigation measures should be adopted in the future to increase production, considering the stable sown area and environmental and resource constraints. [ABSTRACT FROM AUTHOR]

Increasing rice production has become one of the ultimate goals for South Asian countries. The yield and area under rice production are also facing threats due to the consequences of climate change such as erratic rainfall and seasonal variation. Thus, the main aim of this work was to find out the supply response of rice in Malaysia in relation to both price and non-price factors. To achieve this target, time series analysis was conducted on data from 1970 to 2014 using cointegration, unit root test, and the vector error correction model. The results showed that the planted area and rainfall have a significant effect on rice production; however, the magnitude of the impact of rainfall is less conspicuous for off-season (season 2) rice as compared to main-season rice (season 1). The speed of adjustment from short-run to long-run for season-1 rice production is almost two-and-a-half years (five production seasons), while for season-2 production, it is only about one-and-a-half year (three production seasons). Consequently, the study findings imply the supply of water to be enhanced through better water infrastructure for both seasons. Moreover, the area under season 2 is continuously declining to the point where the government has to make sure that farmers are able to cultivate the same area for rice production by providing uninterrupted supply of critical inputs, particularly water, seed and fertilizers.

Low forage quality of available perennial warm-season grasses during mid-summer through late summer affects the production of stocker cattle in the U.S. Southern Great Plains (SGP). Finger millet (Eleusine coracana Gaertn L.), which is a drought tolerant annual grass, could be a promising forage for the SGP. This field study assessed the adaptability and forage characteristics of 11 finger millet accessions originally sourced (1964-1981) from different parts of the world. Results of this study suggested that finger millet can generate forage yields ranging from 5.0 to 12.3 Mg ha-1 165 days after planting. Finger millet forage contained 105 to 156 g kg-1 crude protein, 598 to 734 g kg-1 neutral detergent fiber, 268 to 382 g kg-1 acid detergent fiber, 597 to 730 g kg-1 in vitro true digestibility, and 387 to 552 g kg-1 neutral detergent fiber digestibility. Ten of the 11 accessions flowered and produced grains with yields varying from 60 to 1636 kg ha-1. Overall, finger millet has the potential to serve as an alternative crop for the production of forage and possibly grain in the SGP. Further research needs to be focused on developing strategies for agronomic management and evaluating the capacity of finger millet under different grazing and hay production settings in the SGP. [ABSTRACT FROM AUTHOR]

Increasing rice production has become one of the ultimate goals for South Asian countries. The yield and area under rice production are also facing threats due to the consequences of climate change such as erratic rainfall and seasonal variation. Thus, the main aim of this work was to find out the supply response of rice in Malaysia in relation to both price and non-price factors. To achieve this target, time series analysis was conducted on data from 1970 to 2014 using cointegration, unit root test, and the vector error correction model. The results showed that the planted area and rainfall have a significant effect on rice production; however, the magnitude of the impact of rainfall is less conspicuous for off-season (season 2) rice as compared to main-season rice (season 1). The speed of adjustment from short-run to long-run for season-1 rice production is almost two-and-a-half years (five production seasons), while for season-2 production, it is only about one-and-a-half year (three production seasons). Consequently, the study findings imply the supply of water to be enhanced through better water infrastructure for both seasons. Moreover, the area under season 2 is continuously declining to the point where the government has to make sure that farmers are able to cultivate the same area for rice production by providing uninterrupted supply of critical inputs, particularly water, seed and fertilizers. [ABSTRACT FROM AUTHOR]