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【Objective】 Field evapotranspiration is the main consumption item of agricultural water. It is of great significance to clarify the evapotranspiration amount of crops during the growth period. 【Method】 Based on eddy correlation system, the evapotranspiration of maize field under typical border irrigation in northwest China was continuously observed at Shiyanghe Experimental Station, China Agricultural University from 2015 to 2018. Based on the partial correlation analysis and structural equation model, the relationship between evapotranspiration and meteorological factors during the growth period was analyzed. 【Result】 During the experiment period, the average evapotranspiration during the growing period of maize field under border irrigation was 524.3 mm, and the average daily evapotranspiration was 3.5 mm/d. The daily evapotranspiration showed a single-peak trend of rising to decreasing and reached its peak in July. Based on the analysis of the relationship between evapotranspiration and environmental factors during the experiment period, it can be seen that the correlation between net radiation and evapotranspiration is the largest, and the top three factors influencing the degree of environmental factors are net radiation, temperature and saturated vapor pressure difference. Based on the structural equation model (SEM), it was found that there was a positive correlation between leaf area index (LAI) as an intermediate variable and evapotranspiration, and net radiation, temperature and saturated vapor pressure difference were still the three environmental factors that had the greatest influence on evapotranspiration. 【Conclusion】 This study can provide a scientific basis for studying typical field evapotranspiration in Northwest China and understanding the impact of environmental change on crop evapotranspiration.

ObjectiveThe width of maize stalks is an important indicator affecting the lodging resistance of maize. The measurement of maize stalk width has many problems, such as cumbersome manual collection process and large errors in the accuracy of automatic equipment collection and recognition, and it is of great application value to study a method for in-situ detection and high-precision identification of maize stalk width.MethodsThe ZED2i binocular camera was used and fixed in the field to obtain real-time pictures from the left and right sides of maize stalks together. The picture acquisition system was based on the NVIDIA Jetson TX2 NX development board, which could achieve timed shooting of both sides view of the maize by setting up the program. A total of maize original images were collected and a dataset was established. In order to observe more features in the target area from the image and provide assistance to improve model training generalization ability, the original images were processed by five processing methods: image saturation, brightness, contrast, sharpness and horizontal flipping, and the dataset was expanded to 3500 images. YOLOv8 was used as the original model for identifying maize stalks from a complex background. The coordinate attention (CA) attention mechanism can bring huge gains to downstream tasks on the basis of lightweight networks, so that the attention block can capture long-distance relationships in one direction while retaining spatial information in the other direction, so that the position information can be saved in the generated attention map to focus on the area of interest and help the network locate the target better and more accurately. By adding the CA module multiple times, the CA module was fused with the C2f module in the original Backbone, and the Bottleneck in the original C2f module was replaced by the CA module, and the C2fCA network module was redesigned. Replacing the loss function Efficient IoU Loss(EIoU) splits the loss term of the aspect ratio into the difference between the predicted width and height and the width and height of the minimum outer frame, which accelerated the convergence of the prediction box, improved the regression accuracy of the prediction box, and further improved the recognition accuracy of maize stalks. The binocular camera was then calibrated so that the left and right cameras were on the same three-dimensional plane. Then the three-dimensional reconstruction of maize stalks, and the matching of left and right cameras recognition frames was realized through the algorithm, first determine whether the detection number of recognition frames in the two images was equal, if not, re-enter the binocular image. If they were equal, continue to judge the coordinate information of the left and right images, the width and height of the bounding box, and determine whether the difference was less than the given Ta. If greater than the given Ta, the image was re-imported; If it was less than the given Ta, the confidence level of the recognition frame of the image was determined whether it was less than the given Tb. If greater than the given Tb, the image is re-imported; If it is less than the given Tb, it indicates that the recognition frame is the same maize identified in the left and right images. If the above conditions were met, the corresponding point matching in the binocular image was completed. After the three-dimensional reconstruction of the binocular image, the three-dimensional coordinates (Ax, Ay, Az) and (Bx, By, Bz) in the upper left and upper right corners of the recognition box under the world coordinate system were obtained, and the distance between the two points was the width of the maize stalk. Finally, a comparative analysis was conducted among the improved YOLOv8 model, the original YOLOv8 model, faster region convolutional neural networks (Faster R-CNN), and single shot multiBox detector (SSD)to verify the recognition accuracy and recognition accuracy of the model.Results and DiscussionsThe precision rate (P)、recall rate (R)、average accuracy mAP0.5、average accuracy mAP0.5:0.95 of the improved YOLOv8 model reached 96.8%、94.1%、96.6% and 77.0%. Compared with YOLOv7, increased by 1.3%、1.3%、1.0% and 11.6%, compared with YOLOv5, increased by 1.8%、2.1%、1.2% and 15.8%, compared with Faster R-CNN, increased by 31.1%、40.3%、46.2%、and 37.6%, and compared with SSD, increased by 20.6%、23.8%、20.9% and 20.1%, respectively. Respectively, and the linear regression coefficient of determination R2, root mean square error RMSE and mean absolute error MAE were 0.373, 0.265 cm and 0.244 cm, respectively. The method proposed in the research can meet the requirements of actual production for the measurement accuracy of maize stalk width.ConclusionsIn this study, the in-situ recognition method of maize stalk width based on the improved YOLOv8 model can realize the accurate in-situ identification of maize stalks, which solves the problems of time-consuming and laborious manual measurement and poor machine vision recognition accuracy, and provides a theoretical basis for practical production applications.

【Objective】 Irrigation modulates water movement in soil and its bioavailability to crop roots. This study is to investigate the effect of different irrigation methods on water consumption and water use efficiency of soybean during its whole growth period, with an aim to help develop water-saving irrigation. 【Method】 The field experiment was conducted in 2021 in Wuwei, Gansu province. It compared four irrigation methods: drip irrigation under film mulching, drip irrigation without mulching, border irrigation with film mulching, and border irrigation without mulching. The Bowen ratio-energy balance system was used to continuously monitor the changes in microclimate, crop growth and yield, water consumption (ET) and water use efficiency (WUE). 【Result】 ①Film mulching increased net radiation (Rn) of the soybean by 6.58 W/m2, compared to no mulching; Soil temperature increased by 1.30 ℃ relative to drip irrigation. ②irrigation method and mulching affected physiological indexes of the soybean both at significant levels. Film mulching increased the leaf area index (LAI) significantly by 18.29%, compared to no mulching, while the border irrigation increased the LAI by 5.45%, compared to drip irrigation. ③Film mulching and drip irrigation reduced the ET significantly over the whole growth period, with the mulched drip irrigation having the least ET - 378.92 mm, compared to other treatments. ④Among all environmental and biological factors, Rn and LAI are correlated with ET positively and most significantly. ⑤The yield of soybean under mulched drip irrigation was 18.07%, 5.27% and 11.17% higher than that of non-mulched drip irrigation, film mulched border irrigation, and non-mulched border irrigation, respectively. Film mulching increased WUE by 25.93% compared to no mulching, and drip irrigation increased WUE by 34.62% compared to border irrigation. The mulched drip irrigation increased WUE by 31.15%, 42.86% and 70.21%, respectively, compared to non-film mulched drip irrigation, film-mulched border irrigation and non-film mulched border irrigation. 【Conclusion】 Compared to drip irrigation without film, border irrigation with film and border irrigation without film, the yield of drip irrigation under film increased by an average of 11.51%, ET decreased by 24.13%, and WUE increased by 48.07% and irrigation water use efficiency improved by 46.57%; Taking into account the water consumption, yield and water use efficiency of soybean, drip irrigation under film provided a more benefit growth environment for soybean growth and significantly reduced ET and increased yield.

Recent studies have suggested that drought projections using Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) may overestimate drought severity. This overestimation occurs because the potential evapotranspiration (PET) calculations fail to consider the interactive effects of vegetation responses such as increased leaf area index (LAI) and constrained stomatal conductance, which are influenced by elevated atmospheric CO2 concentrations ([CO2]). To address this issue, our study replaced the traditional Penman‐Monteith (PM) equation with a recently proposed PET equation that includes the effects of changing [CO2] and LAI to assess droughts at monthly scale in the Upper Yangtze River basin, which experiences the vegetation greening. The findings indicated a consistent increasing trend in drought conditions with minimal discrepancy between the two equations over the historical period (1986–2017). This consistency arises because the water‐saving effects of increased [CO2] and the greening effects of rising LAI largely counterbalance each other. However, for the future period (2018–2100), projections using PM equation predicted an intensification of drought conditions. In contrast, the improved SPEI indicated no significant drought variations, and the improved PDSI suggested a wetting trend. This divergence can be attributed to the water‐saving effects increasingly outweighing the greening effects, as PET shows a decreasing sensitivity to LAI with LAI increasing, but maintains a near‐constant sensitivity to elevated [CO2]. Consequently, the indices based on PM equation tend to overestimate future drought severity. Overall, this study demonstrates that the new PET estimation method is more capable of responding to the changing environment. Plain Language Summary: This study enhances drought projections by incorporating the effects of elevated atmospheric CO2 concentrations ([CO2]) and increased vegetation, as indicated by the leaf area index (LAI), on potential evapotranspiration (PET). Historically, the water‐saving effect of elevated [CO2] and the greening effect due to increased vegetation have counterbalanced each other, resulting in minimal discrepancies between traditional and newly developed methods. However, our innovative approach demonstrates that with rising LAI, the sensitivity of PET to LAI diminishes, while its sensitivity to CO2 remains relatively constant. This finding suggests that in future scenarios with concurrent increases in [CO2] and LAI, the water‐saving effect will potentially dominate the greening effect, leading traditional methods to overestimate future drought severity. Consequently, this new method offers a more accurate framework for predicting changes in future environmental conditions. Key Points: Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) are improved using a newly proposed potential evapotranspiration equation that considers the effects of atmospheric CO2 concentrations and leaf area index (LAI) changeThe improved PDSI and SPEI are more applicable in a changing environment and characterize the spatial differences of vegetation variationThe impacts of increased atmospheric CO2 concentrations and LAI must be factored into the assessment of meteorological drought trends [ABSTRACT FROM AUTHOR]

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules. [ABSTRACT FROM AUTHOR]

We propose a memory-enhanced multi-stage goal-driven network (ME-MGNet) for egocentric trajectory prediction in dynamic scenes. Our key idea is to build a scene layout memory inspired by human perception in order to transfer knowledge from prior experiences to the current scenario in a top-down manner. Specifically, given a test scene, we first perform scene-level matching based on our scene layout memory to retrieve trajectories from visually similar scenes in the training data. This is followed by trajectory-level matching and memory filtering to obtain a set of goal features. In addition, a multi-stage goal generator takes these goal features and uses a backward decoder to produce several stage goals. Finally, we integrate the above steps into a conditional autoencoder and a forward decoder to produce trajectory prediction results. Experiments on three public datasets, JAAD, PIE, and KITTI, and a new egocentric trajectory prediction dataset, Fuzhou DashCam (FZDC), validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Food security, a crucial issue for the development of humankind, is often severely constrained by water scarcity. As a globally recognized most advanced agricultural water-saving technology, drip irrigation under plastic mulch (DIPM) has played a significant role in grain production. However, a comprehensive review of the dual impacts of this practice in farmland remains lacking. This study has conducted an exhaustive review of DIPM research from 1999 to 2023 and employed CiteSpace software to perform a co-occurrence and clustering analysis of keywords in order to reveal research hotspots and trends. The results show that the attention to DIPM technology has increased annually and reached a peak in 2022. China leads in the number of publications in this field, reflecting its emphasis on agricultural water-saving technologies. This study critically discusses the dual impacts of DIPM on farmland. On the positive side, DIPM can improve soil temperature and moisture, enhance nutrient availability, promote water and nutrient absorption by roots, and increase the crop growth rate and yield while reducing evaporation and nitrogen loss, suppressing weed growth, decreasing herbicide usage, and lowering total greenhouse gas emissions. On the negative side, it will cause pollution from plastic mulch residues, damage the soil structure, have impacts on crop growth, and lead to increased clogging of drip irrigation systems, which will increase agricultural costs and energy consumption, hinder crop growth, hamper soil salinization management, and further reduce the groundwater level. The future development of DIPM technology requires optimization and advancement. Such strategies as mechanized residual-mulch recovery, biodegradable mulch substitution, aerated drip irrigation technology, and alternate irrigation are proposed to address existing issues in farmland triggered by DIPM. This review advocates for the active exploration of farming management practices superior to DIPM for future agricultural development. These practices could lead to higher yields, water–nitrogen efficiency, and lower environmental impact in agricultural development. [ABSTRACT FROM AUTHOR]

The substantial depletion of freshwater reserves in many pivotal agricultural regions, attributable to the dual pressures of global climate change and the excessive extraction of water resources, has sparked considerable apprehension regarding the sustainability of future food and water security. Drip irrigation, as an efficient and precise irrigation method, reduces water loss caused by deep percolation, soil evaporation, and runoff by controlling the irrigation dosage and frequency, thus improving the efficiency of water resource utilization. Studies have shown that compared with traditional irrigation methods, drip irrigation can significantly decrease water consumption, optimize the water–energy relationship by reducing soil evaporation, increase the leaf area index, and promote crop growth, thereby enhancing plant transpiration. Although more wet and dry soil cycles from drip irrigation may increase soil CO2 emissions, it also enhances crop photosynthesis and improves crop net ecosystem productivity (NEP) by creating more favorable soil moisture conditions, indicating greater carbon sequestration potential. The advantages of drip irrigation, such as a short irrigation cycle, moderate soil moisture, and obvious dry and wet interfaces, can improve a crop's leaf area index and biomass accumulation, improve root dynamics, promote the distribution of photosynthetic products to the aboveground parts, and thus enhance crop yields. This study highlights the potential for the application of drip irrigation in arid regions where resource optimization is sought, providing strong technical support for the achievement of sustainable agricultural development. Future research needs to consider specific agricultural practices, soil types, and environmental conditions to further optimize the implementation and effectiveness of drip irrigation. [ABSTRACT FROM AUTHOR]

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

Two years of field experiments were conducted at the National Field Observation Experiment Station for Efficient Agricultural Water Use in the Wuwei Oasis, Gansu Province. Based on the eddy correlation system, the evapotranspiration (ET) of the cabbage agroecosystem during the growth period was obtained and the main driving factors of ET changes were determined. The Root Zone Water Quality Model 2.0 version (RZWQM2 model) was used to simulate ET during the growth period. The results showed the following: (1) The ET of cabbage during the growth period was 260. 1 ± 24.2 mm, which was basically lower than other crops planted in this area. (2) Through partial correlation analysis and principal component analysis, it can be found that environmental and physiological factors jointly drive changes in ET. The main driving factors include gross primary productivity, net radiation, and water use efficiency. (3) The RZWQM2 model can simulate the ET of the cabbage agroecosystem well, especially in simulating the total ET value and its trend. The growth period ETs were 7.3% lower than the ETm. Cabbage is an important cash crop in Northwest China, and ET is an important component of the water cycle in the agroecosystem. Determining the main driving factors of ET is of great significance for the sustainable utilization of agricultural water resources in Northwest China. Our results can provide a scientific basis for the cultivation of cabbage as a cash crop and the development of water saving agriculture. [ABSTRACT FROM AUTHOR]

Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d -1 , unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d -1 , mean absolute errors (MAEs) of 0.81 and 0.73 mm d -1 , and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at 10.5281/zenodo.8047038 (Li et al., 2023b). [ABSTRACT FROM AUTHOR]