Winter Wheat Yield Research Articles

Delayed application of water and fertilizer increased wheat yield but did not improve quality parameters

Context or problemWheat is an important food crop for mankind, simultaneous improvement of wheat yield and quality is human most important goal, but they often cannot increase at the same time. Objective or research questionThe effects of irrigation mode, nitrogen application rate and topdressing time on yield and quality of two wheat varieties were studied, and the possibility of synergistic improvement of yield and quality was also explored. MethodsTwo irrigation treatments (conventional border irrigation and micro-sprinkling irrigation) were set up, and three nitrogen application rate treatments (120, 210 and 300kgN ha−1) were set up under each irrigation treatment. The yield formation and quality parameters of wheat were investigated. ResultsThe grain yield, key enzyme activities of protein synthesis, protein content and dough processing quality of the two cultivars increased with the increase of nitrogen application rate. However, when the nitrogen level was higher than 210kgN ha−1, the grain yield and processing quality of winter wheat did not increase further. Under the same nitrogen application rate, compared with conventional border irrigation (CBI), micro-sprinkling irrigation (MSI) significantly increased the grain yield and total protein content, but did not improve the ratio of glutenin to gliadin and dough processing quality parameters. ConclusionsUnder the experimental conditions, the synergistic improvement of wheat yield and quality parameters could be achieved by optimizing nitrogen application rate, and the delayed application of water and fertilizer could further increase wheat grain yield, but did not improve grain quality parameters. Implications or significanceThis study provided theoretical guidance for further improving the grain yield and quality of winter wheat in the North China Plain.

Simulation of wheat water footprint using AquaCrop model under the climate change, case study in Qazvin plain

Simulating crop water consumption has been introduced as a valuable decision tool in food security. Such a tool is typically used to support a better understanding of how to increase water-use efficiency to satisfy optimal water management and sustainability. However, climate change is one of the most important and influential factors that restrain sustainable development, agriculture, and food security. Wheat is one of the most important and strategic products in the world and Iran. Therefore, in this study, the impacts of future climate changes on winter wheat yield, water requirement (WR), evapotranspiration (ET), and water footprint (WF) were evaluated in Qazvin Plain, Iran. As such, the outputs from five general circulation models (EC-EARTH, GFDL-CM3, MPI-ESM-MR, MIROC5, and HADGEM2-ES) were fed into the LARS-WG model to get finer spatial climate data for four future periods (P1:2021–2040, P2:2041–2060, P3:2061–2080, P4:2081–2100) considering three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). Thereafter, the projected climate change data were used in the FAO AquaCrop model to simulate the variability of wheat characteristics. The results proved the superiority of LARS-WG to model the maximum and minimum temperatures and precipitation (P) of the baseline scenario (1986–2015). Moreover, results revealed that the wheat WF will decrease in future periods. The modeling results showed that the average wheat yield and biomass will increase in future periods by 7.67 and 15.98 tons/ha, respectively, as compared to the baseline. The highest increase was recorded by the HadGEM2-ES model with RCP8.5 during 2081–2100. The average WR in the baseline was 127.14 mm, which was projected to decrease in future periods. The results show that ET will potentially increase in the period 2021–2040. As a consequence, the adapted methodology produced significantly superior outcomes and can aid in decision-making for both water managers and development planners.

DUS testing characteristics relation with agro-morphological and quality traits of winter wheat

In a field trial, a database of qualitative traits associated with agromorphological and quality traits of winter wheat (Triticum aestivum L.) has been collected. The present study aimed to determine the relationships between the winter wheat traits of productivity and quality with traits of Distinctness, Uniformity and Stability (DUS) testing. In addition, according to grain yield and significantly correlated traits, investigated genotypes will be clustered. Grain yield was significantly positively correlated with the number of days to heading, time of ear emergence, and plant growth habit. Based on the grouping of the winter wheat genotypes using the Euclidean distance algorithm, wheat genotypes were separated into five main clusters according to the similarity of those four traits. The grouping pattern was very origin homogeneous in each group, and the grouping pattern of genotypes appeared to be associated. The highest contributor to a grouping of genotypes was grain yield. The established result pertain to the potential for increasing the grain yield of winter wheat.

Adopting plastic film mulching system in the food-energy-water-carbon nexus to the sustainable dryland agriculture

Agricultural production development and ecological environmental protection are the main challenges facing dryland agriculture in the Loess Plateau. Over the past four decades, this region has transitioned from a state of food shortage and ecological deterioration to a new phase with plentiful food supply and improved ecological environment. However, it remains unclear how this system navigates resource and environmental constraints, balancing economic growth and ecological preservation. Here we conducted integrated and systematic analysis by combining process-based biogeochemical model, statistical yearbook data, and rural social surveys with the life cycle assessment (LCA) methodology. The yield, economic benefits, carbon footprint (CF), and energy balance of maize, winter wheat, and potato within the plastic film mulching (PFM) cropping system surpassed those of conventional tillage, while the water footprint (WF) was lower. Among them, soil N2O was the primary source of direct greenhouse gas (GHG), while mineral fertilizer (40–71 % and 52−73 %) and agricultural diesel (13−18 % and 17−18 %) were the main contributors to indirect GHG and energy inputs. Moreover, the food-energy-water-carbon (FEWC) nexus of maize being harmonized during 1980–2019 (0.50–1.00), and those of winter wheat and potato being harmonized after 2000 (0.51–0.98 and 0.54–0.97, respectively). The maize also was more profitable than winter wheat and potato. The changing rates of yield, agricultural net profit (ANP), WF, and CF of major crops in the Loess Plateau exhibited consistent increase over time, albeit with regional differentiation characteristics. These results highlight that PFM system achieved high economic benefits and low environmental costs, and it contributed to establishing resource-efficient, production-effective, and eco-friendly dryland agriculture in China and the world.

Biological Effectiveness of Tank Mixtures of Pesticides and Micronutrients in the Protection of Winter Wheat and Their Effect on Grain Yield and Quality

The effectiveness of protecting winter wheat from pests and diseases, yield and grain quality were studied by using tank mixtures of new pesticides and agrochemicals, mainly of domestic production. The study was conducted in small-scale experiments in the conditions of the forest-steppe of the Central Chernozem region in crops of winter soft wheat (Triticum aestivum L.) of the Krasa Dona variety. The size of plots in the experiments is 30 m2, the repeatability is fourfold, the placement of plots is randomized. Tank insectofungicidal mixtures of Sparring + Ace and Klonrin + Elatus Ace individually and in combination with micronutrient Biostim Zernovoy were studied. To control the insectofungicidal effect, a tank mixture of Vostorg + Ceriax Plus, studied earlier, was used. Application of insectofungal tank mixtures Sparring + Ace and Klonrin + Elatus Ace individually and with micro-fertilization of Biostim Zernovoy in winter soft wheat crops of the Krasa Dona variety provided high biological efficacy against a complex of pests and diseases. The insecticidal activity of tank mixtures was 96.5–100, fungicidal – 78.3–90.5%. Spraying of winter wheat with tank insectofungicidal mixtures individually and with micro-fertilization contributed to a significant increase in the yield of winter soft wheat. The largest grain yield increases of 12.9 and 13.6 c/ha were obtained in variants with Sparring + Ace + Biostim Zernovoy and Klonrin + Elatus Ace + Biostim Zernovoy combinations. The use of the studied tank mixtures individually and with micro-fertilization increased the quality of winter wheat grain: the content of crude gluten increased by 2.1–3.4% relative to the control, and its quality improved.

Winter wheat yield prediction based on convolutional neural network–bidirectional long short-term memory–attention model using time series synthetic aperture radar images

Winter wheat yield prediction based on convolutional neural network–bidirectional long short-term memory–attention model using time series synthetic aperture radar images

Reconstruction of Fine-Spatial-Resolution FY-3D-Based Vegetation Indices to Achieve Farmland-Scale Winter Wheat Yield Estimation via Fusion with Sentinel-2 Data

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data.

Easy yield mapping for precision agriculture

ABSTRACT Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture.

Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices

Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices

Effect of High Nighttime Temperatures on Growth, Yield, and Quality of Two Wheat Cultivars During the Whole Growth Period.

It is a consensus that Earth's climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period.

The impact of weather patterns on inter-annual crop yield variability

Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions.Our study examines 30 distinct UK Met Office weather patterns (MO30) based on mean sea level pressure. We investigate their association with weather conditions that limit winter wheat yield in the UK (1990–2020). Blocked, negative North Atlantic Oscillation (NAO) patterns create the highest risk of temperatures that are below optimal for crop yield. However, the connection between weather patterns and yield is complex, with differing effects at a regional scale and even at which point in the growth cycle they appear. It was found that anticyclonic weather patterns during sowing, emergence, vernalisation, anthesis, and grain filling exhibit a relationship with good crop yields with a Spearman correlation coefficient of up to 0.55 for a single weather pattern (WP3 during vernalisation in South East England), whilst cyclonic patterns can help during the terminal spikelet phenological phase. The strongest positive correlations were during sowing, emergence, and vernalisation, whilst the largest negatives were observed in anthesis and grain filling.The potential of combining weather patterns with existing crop simulation models to produce earlier and more accurate yield predictions is shown. This would enable effective crop management and climate mitigation strategies, critical to strengthening food security. Projected changes in weather pattern occurrences in the late 21st century will likely reduce crop yields. This is due to increased cyclonic weather patterns, which bring warmer, wetter conditions during the wheat's vernalisation stage, followed by warmer, drier conditions during the anthesis and grain-filling phases.

Long-term effects of agronomic practices on winter wheat yield and NUE in dryland regions of USA and China: a long-term meta-analysis

Dryland agriculture is fundamental to global crop production and vital to food security. Conservation tillage is extensively practiced in USA wheat cultivation. Meanwhile, the adoption of conservation tillage by Chinese farmers is limited. This meta-analysis compared the yield and nitrogen use efficiency (NUE) between conservation tillage and conventional tillage (CT) with different types of cropping systems, mulching methods, levels of nitrogen fertilizer (NF), and addition of manure. The meta-analysis presented that conservation tillage at high-NF enhanced the yield and NUE, and reduced the yield and NUE at low-NF, compared to CT. The interaction of conservation tillage with leguminous cover crops (LCC) and manure application increased the yield and NUE at low-NF, compared to CT. Non-leguminous cover crops (NLCC) increased the yield and NUE under high-NF than low-NF. The interaction of conservation tillage with management practices showed that the no-tillage (NT) with leguminous cover crops (LCC) significantly increased wheat yield by 58% and NUE by 47% under low-NF compared to CT. However, increasing the rate of NF did not increase the yield under such interaction. Cropping systems, mulching types, and manure application mainly determined the effects of conservation tillage on wheat yield and NUE. The adverse impact of CT on yield and NUE could be alleviated with the application of LCC and manure under moderate NF. We demonstrate that adding LCC and manure have a generally substitutive relationship with N fertilizer, resulting in a significant increase in wheat yield and NUE at low-NF doses as at high N fertilizer dosages. Therefore, based on the obtained results, moderate NF with LCC and manure application is recommended for growing winter wheat in dryland regions of the USA and China.

Nutrient Mass in Winter Wheat in the Cereal Critical Window Under Different Nitrogen Levels—Effect on Grain Yield and Grain Protein Content

The mass of nutrients accumulated in the vegetative parts of winter wheat (WW) in the period from the beginning of booting to the full flowering stage (Critical Cereal Window, CCW) allows for the reliable prediction of the grain yield (GY) and its components, and the grain protein content (GPC) and its yield. This hypothesis was verified in a one-factor field experiment carried out in the 2013/2014, 2014/2015, and 2015/2016 growing seasons. The field experiment included seven nitrogen-fertilized variants: 0, 40, 80, 120, 160, 200, and 240 kg N ha−1. The N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu content in wheat vegetative parts (leaves, stems) was determined in two growth stages: (i) beginning of booting (BBCH 40) and (ii) full flowering (BBCH 65). We examined the response of eight WW traits (ear biomass at BBCH 65, EAB; grain yield, GY; grain protein content, GPC; grain protein yield, GPY; canopy ear density, CED; number of grains per ear, GE; number of grains per m−2—canopy grain density, CGD; and thousand grain weight, TGW) to the amount of a given nutrient accumulated in the given vegetative part of WW before flowering. The average GY was very high and ranged from 7.2 t ha−1 in 2016 to 11.3 t ha−1 in 2015. The mass of ears in the full flowering stage was highest in 2016, a year with the lowest GY. The highest N mass in leaves was also recorded in 2016. Only the biomass of the stems at the BBCH 65 stage was the highest in 2015, the year with the highest yield. Despite this variability, 99% of GY variability was explained by the interaction of CGD and TGW. Based on the analyses performed, it can be concluded that in the case of large yields of winter wheat, GE is a critical yield component that determines the CGD, and in consequence the GY. The leaf nutrient mass at the BBCH 40 stage was a reliable predictor of the GPC (R2 = 0.93), GPY (0.92), GE (0.84), and CED (0.76). The prediction of the GY (0.89), CGD (0.90), and TGW (0.89) was most reliable based on the leaf nutrient mass at the BBCH 65 stage. The best EAB prediction was obtained based on the mass of nutrients in WW stems at the BBCH 65 stage. The magnesium accumulated in WW parts turned out to be, with the exception of TGW, a key predictor of the examined traits. In the case of the TGW, the main predictor was Ca. The effect of Mg on the tested WW traits most often occurred in cooperation with other nutrients. Its presence in the developed stepwise regression models varied depending on the plant part and the WW trait. The most common nutrients accompanying Mg were micronutrients, while Zn, Fe, Mn, and Ca were the most common macronutrients accompanying Mg. Despite the apparently small impact of N, its yield-forming role was indirect. Excessive N accumulation in leaves in relation to its mass in stems, which appeared in the full flowering phase, positively impacted the EAB and GPC, but negatively affected the GE. Increasing the LE/ST ratio for both Mg and Ca resulted in a better formation of the yield components, which, consequently, led to a higher yield. This study clearly showed that nutritional control of WW during the CCW should focus on nutrients controlling N action.

No-till, crop residue management and winter wheat-based crop rotation strategies under rainfed environment

Rainfed agriculture is primarily limited by unstable low precipitation, poor soil fertility and monocropping, which are the main factors leading to decreased crop production. This long-term research was conducted under a rainfed agroecosystem from 2019 to 2023 on the sierozem soil of the Karshi steppe, Uzbekistan. Along with winter wheat (WW) which was the main crop covering 50% of each proposed cropping pattern, chickpea (CH), safflower (SA), flax (FL), barley (BA) and canola (CA) were evaluated to find the most suitable rotation systems under no-till (NT) i.e. NT1: WW–CH–WW–FL, NT2: CH–WW–SA–WW, NT3: WW–SA–WW–BA and NT4: SA–WW–CA–WW compared against continuous WW produced with conventional tillage (CT). Results showed that the integrated effect of NT x crop diversification x residue retention positively affected crop productivity; however, their impact were significantly higher under the NT2 treatment, but not with continuous WW under CT. The highest grain yield of WW in the 2020-2021 growing season was recorded under NT2 and NT4 treatments with values of 1.47 and 1.30 Mg ha-1, while the lowest index (1.02 Mg ha-1) was found at the CT treatment. The grain yield in the NT treatments increased with the improvement of soil chemical and physical parameters, i.e. NPK and humus content. When comparing NT2 to CT treatment at the project end, the total N, P, and K values at the 0–20 cm soil profile were 27.9%, 13.9%, and 33.9% higher, respectively. This study concluded that implementation of NT along with strategic selection of legumes incorporated into the cropping system and residue management can be prioritized as rehabilitation measures in rainfed croplands.

Optimization of water and nitrogen management in wheat cultivation affected by biochar application − Insights into resource utilization and economic benefits

In the context of global climate change and natural resource scarcity, agricultural production is facing multiple challenges in improving crop yields and optimizing natural resource use. It is of great practical significance to optimize agricultural practices to achieve sustainable agricultural development. In this study, a two-year winter wheat field experiment was conducted in the Guanzhong Plain, Shaanxi, China, from 2020 to 2022 to assess the effects of biochar, irrigation, and N fertilizer rates on the yield, water-nitrogen use efficiency, and economic benefits of winter wheat. Specifically, biochar was applied to winter wheat at 30 t ha−1 in combination with two irrigation application rates, including regular irrigation (I100; actual evapotranspiration) and deficit irrigation (I80; 0.8 actual evapotranspiration), and three different N fertilizer rates at 210 (NH; conventional applied N rate by local farmers), 160 (NM; moderate N rate), and 110 kg ha−1 (NL; low N rate). The control groups in this study consisted of experimental plots under the NM and N0 (no N) without biochar application. The aboveground dry matter mass (ADM), yield, and net ecosystem economic budget (NEEB) of winter wheat showed increasing trends with increasing N application rates without significant differences between the NH and NM treatments. On the other hand, the water use efficiency (WUE), agronomic N fertilizer use efficiency (aNUE), and N fertilizer recovery efficiency (NRE) showed increasing-decreasing trends with increasing N fertilizer rates, reaching the highest values under the NM treatment scenario. The biochar addition significantly increased the winter wheat yield and WUE (P<0.05). On the other hand, the I80 treatment resulted in higher WUE and irrigation water use efficiency (IWUE) than those under I100 by 5.63 and 13.52 %, respectively. TOPSIS results indicated that the combined I80B1NM treatment was the optimal winter wheat management practice, maintaining high productivity while improving resource use efficiency and economic benefits. The results of the present study provide an important scientific basis and guidance for ensuring efficient and high-quality agricultural products in the Guanzhong Plain and other regions in China with similar climatic characteristics.

Estimation of Surface Ozone Effects on Winter Wheat Yield across the North China Plain

Estimation of Surface Ozone Effects on Winter Wheat Yield across the North China Plain

Compound extreme climate events intensify yield anomalies of winter wheat in France

Abstract Compound extreme climate events (ECEs) are increasingly recognized for their potential to exacerbate food insecurity risks beyond those posed by isolated events. The notion of ‘compound event’ encompasses not only co-occurring ECEs but also multiple ECEs across (different) growth stages (mECEs). The additional effects of these mECEs on crop yield, particularly considering various types of ECEs and regional scales, remain poorly understood. To close this knowledge gap, we consider droughts, pluvials, heatwaves, and coldwaves, and further identify which types of compound events have additional effects on winter wheat yield in France, using statistical methods and datasets encompassing 94 counties over a 68-year period. Our results indicate co-occurring drought heatwaves in summer and spring, along with co-occurring pluvial heatwaves and pluvial coldwaves in winter, have negative additional effects on yield compared with single ECEs. We further identify the types of mECEs that have intensified effects, with the majority showing negative effects on yield. Key interactions leading to intensified yield loss include droughts in winter or spring combined with summer co-occurring drought heatwaves, pluvials across multiple growth stages, pluvials combined with coldwaves, and the transition between droughts and pluvials, with the most severe anomaly attaining −17.2%. Coldwaves are the main ECE related to intensified yield increases, while their frequency is decreasing. Overall, this study stresses the interactions among ECEs on crop yield, and the identified types of mECEs could serve as foundational information for designing control experiments and improving process-based crop models.

A Mixture of Summer Legume and Nonlegume Cover Crops Enhances Winter Wheat Yield, Nitrogen Uptake, and Nitrogen Balance

Cover crops protecting soil erosion during the summer fallow in the monsoon weather may enhance dryland winter wheat yield and N relations. We examined the effects of four summer cover crops (soybean (Glycine max L., SB), sudangrass (Sorghum sudanense {Piper} Stapf, SG), soybean and sudangrass mixture (SS), and no cover crop (CK)) and three N fertilization rates (0, 60, and 120 kg N ha−1) on winter wheat yield, quality, and N relations from 2017–2018 to 2020–2021 in the Loess Plateau of China. Cover crop biomass and N accumulation, soil mineral N, and winter wheat yield, protein concentration, and N uptake were greater for SB and SS than other cover crops at most N fertilization rates and years. The N fertilization rate had variable effects on these parameters. Winter wheat aboveground biomass and grain N productivities were greater for CK than other cover crops at all N fertilization rates and years. Nitrogen balance was greater for SS than other cover crops at 60 and 120 kg N ha−1 in all years. The SS with 120 kg N ha−1 can enhance soil mineral N, winter wheat yield and quality, and N balance compared to CK and SG with or without N fertilization rates.

Investigating the impacts of different degrees of deficit irrigation and nitrogen interactions on assimilate translocation, yield, and resource use efficiencies in winter wheat

Water and nitrogen (N) are recognized as the primary determinants influencing the development and output of winter wheat. They affect the growth of winter wheat not only through their own changes, but also through their interaction. The translocation and accumulation of pre- and post-anthesis assimilates (including dry matter and N) are important physiological processes in winter wheat, which are closely related to the resource use efficiency and yield. However, a dearth of research exists that examines the complex interplay between varying water levels, especially severe water deficit, and N deficiency levels on the growth dynamics of winter wheat. The purpose of this study was to quantify and compare the effects of different degrees of water deficit and N interaction on assimilate translocation, water and N use efficiency and yield of winter wheat. A four-year long-term field experiment conducted under the rain-out shelter including four irrigation and two N levels was launched during 2019–2023. This method avoided the impact of precipitation on water treatment and helped to achieve serious water deficit treatment. The findings indicated that the higher N application improved the wheat's ability by 49.6 % - 362.3 % to utilize the available soil water. The pre- and post-anthesis translocation and accumulation of assimilates (including dry matter and N) in winter wheat increase alongside elevated levels of water and N application, except under severe water deficit treatment. Plants under mild to moderate water deficit were better able to translocate the pre-anthesis assimilates. However, the severe water deficit hindered the re-translocation of assimilates before anthesis. Providing additional N during periods of severe water scarcity leads to a 13.4 % - 44.7 % reduction in water use efficiency (WUE). Furthermore, both the irrigation water use efficiency and WUE diminished by 10.3 % - 60.5 % and 4.5 % - 41.4 % with higher levels of irrigation, while improved by 24.4 % - 48.2 % and 12.6 % - 39.4 % with higher N application rates. Conversely, N partial factor productivity and N use efficiency followed the opposite trend. Similar to WUE, increasing N application under severe water deficit would result in a yield reduction of 38.7 %. The reduction in crop yield resulting from severe water stress was primarily ascribed to the decline in the kernels number per spike (up to 74.9 %), with the reduction in spike number per unit area following closely behind (up to 29.1 %). This integrated approach, combining resource use efficiency with a detailed assessment of assimilate dynamics, provides a holistic view of how water and N management strategies impact winter wheat performance. The findings will offer precise insights for developing targeted irrigation and N scheduling strategies for sustaining both winter wheat production and environmental sustainability.

Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture.