Huang Huai Research Articles

Design and Experiment of an Inter-Row Weeding Machine Applied in Soybean and Corn Strip Compound Planting (SCSCP)

To address the lack of specialized machinery for the mechanical weeding of SCSCP in the Huang Huai Hai region, this study designs a mechanized inter-row weeding machine for SCSCP. The machine features a reciprocating weeding shovel and an adaptive contouring mechanism for cultivation and soil loosening. This paper details the machine’s principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. Furthermore, by optimizing the design of the weeding shovel’s reciprocating motion mechanism, combining EDEM simulation with the weeding shovel–soil interaction, it has been determined that, at various PTO shaft speeds, the optimal weeding efficacy is achieved with a blade-type weeding shovel structure when operating at a forward speed of 3.5 km/h. Field experiments were conducted with different PTO shaft speeds and weeding depths, using weeding and seedling injury rates as performance indicators. The results showed that, based on the optimal speed, the PTO shaft speed is 760 r/min, the operating depth is 3–5 cm, and the average row weeding rate is 90.4%. The average soybean and corn seedling injury rate is 3.4% and 4.2%, meeting the technical requirements for mechanical weeding.

Effect of water vapor transport and budget on precipitation in the Yangtze–Huang–Huai–Hai River Basin

Study regionThe Yangtze−Huang−Huai−Hai River basin (3.19 million km2) in China, which is vital for China's economic development. Study focusWe employed the Eulerian method to identify the primary water vapor channels from 2005 to 2020, and the water vapor net budget over the basin was calculated. Additionally, the Lagrangian method was utilized to simulate water vapor transport trajectories across four seasons, with the aim of investigating the influence of changes in water vapor on precipitation. New hydrological insights for the regionUtilizing both Eulerian and Lagrangian methods, the findings indicate that: (1) Precipitation in the basin experienced a downward trend of −3.5 mm·a−1 during 2005−2020, mainly in spring (−1.4 mm·a−1) and summer (−1.7 mm·a−1); (2) The water vapor net budget has a substantial downward trend (−0.9 kg·m−1·s−1·a−1), mainly in spring (−0.4 kg·m−1·s−1·a−1) and summer (−0.3 kg·m−1·s−1·a−1), which aligns with the declining trend of precipitation. (3) The Hybrid Single Particle Lagrangian Integrated Trajectory Model demonstrates that the decrease in long−distance water vapor transport trajectories impacts the basin’s water vapor channels. The decline in spring is predominantly influenced by the water vapor transport trajectories from Asia, Europe, and Africa−Arctic Ocean (−0.7 %·a−1), while the summer is mainly driven by the water vapor transport trajectory from the Indian Ocean−South China Sea (−0.3 %·a−1). This study improves the existing understanding of the hydrological cycle in the context of the basin, and offers a crucial scientific basis for water resource management and regulation.

Future Ozone Changes and Their Impacts on Vegetation and Human Health in China Under the Shared Socio‐Economic Pathways

AbstractOzone concentrations in China are increasing in recent years and future changes of ozone and their impacts have attracted much attention. We use global chemical transport model (GEOS‐Chem) to simulate the surface ozone concentrations in China in 2020 and 2050 under four Shared Socio‐economic Pathways and evaluate the impacts of future ozone pollution on vegetation and premature mortality in four polluted regions (Beijing–Tianjin–Hebei, BTH; Yangtze River Delta, YRD; Pearl River Delta, PRD; Sichuan Basin, SCB) and three major crop growing areas (Huang–Huai–Hai, HHH; Northeast Plain, NEP; middle and lower reaches of Yangtze River, MLRY) in China. The changes of simulated seasonal maximum daily 8‐hr average (MDA8) ozone from 2020 to 2050 (−15.5 to +11.9 ppbv) are significant under SSP126 (low forcing pathway) and SSP245 (medium forcing pathway) scenarios in all regions due to large changes of emissions. MDA8 ozone in summer 2050 will be above the WHO guidelines (100 μg/m3) in BTH, YRD, HHH and MLRY under four scenarios. By 2050, W126 (vegetative ozone exposure index) in summer will be much above the maximum of US secondary standard (21 ppm‐h) in HHH under SSP245, SSP370 (medium to high forcing pathway) and SSP585 (high forcing pathway) scenarios, and in MLRY under SSP370 and SSP585 scenarios. Annual ozone‐related deaths for people over 30 years old will mainly decrease in four polluted areas from 2020 to 2050 under SSPs scenarios, but only increase much under SSP245 scenario in BTH (+3.1 to +4.2 thousand) and YRD (+1.1 to +1.6 thousand).

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

Refined Evaluation of Climate Suitability of Maize at Various Growth Stages in Major Maize-Producing Areas in the North of China

The Northeast region of China and Huang Huai Hai (3H) region are vital maize production bases in northern China that are crucial for national food security. The absence of phenological data hinders a detailed assessment of the alignment between maize development stages and climatic resources. This study combines the authors’ maize phenology data with climate suitability modeling to evaluate maize’s climate suitability at different developmental stages in both regions. This study shows that during the maize growth cycle, the average temperature, precipitation, sunshine, and comprehensive climate suitability were 0.77, 0.49, 0.87, and 0.65, respectively, in the Northeast. In contrast, the average temperature, precipitation, sunshine, and comprehensive climate suitability in the 3H region were 0.98, 0.53, 0.73, and 0.70, respectively. Precipitation is a major factor influencing maize growth, with temperature and sunshine impacting growth differently across regions. Temperature significantly affects maize in the Northeast, while sunshine plays a greater role in the 3H region. The Northeast is suitable for drought-resistant maize varieties, and implementing a late harvest policy in Liaoning could enhance maize yield. The 3H region generally has favorable climatic conditions. Apart from certain parts of Henan needing drought-resistant varieties, areas with ample growing seasons can adopt long-duration varieties to maximize thermal resource utilization. Our results have important implications for optimizing maize planting strategies and enhancing regional resilience, aiming to assess meteorological factors’ impact on maize growth in key production areas.

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

The Impact of Food Production Comparative Advantage on Green Total Factor Productivity: The Moderating Role of Environmental Regulation

Guaranteeing an increase in ecologically sustainable food production is a sufficient prerequisite for the long-term development of national food security. This study’s primary goal is to determine strategies for improving the nation’s green total factor productivity (GTFP) of food. We begin by measuring the GTFP of food with the Global Malmquist–Luenberger (GML) index. Second, the food production comparative advantage is determined using the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. The food production comparative advantage is then used as a leaping point to experimentally study the pathway to enhancing the GTFP of food. The 510 sample statistics for this study come from 30 provinces in China from 2003 to 2019. The study’s findings indicate that (i.) China’s “food production comparative advantage” and “GTFP of Food” have shown an ascending pattern. China’s Northeast and Huang–Huai–Hai regions have the greatest comparative advantages in food production. The regions with the highest food GTFP are the Northeast and Middle and Lower reaches of the Yangtze River. (ii.) Food production comparative advantage can effectively contribute to green total factor productivity, but there is a time lag. (iii.) As food production’s comparative advantage rises, its contribution to GTFP becomes more apparent. (iv.) Environmental regulation moderates the influence of food production comparative advantage on GTFP. In addition, environmental regulations exert a greater moderating effect in regions with lower green total factor production rates than in regions with higher green total factor production rates. (v.) The food production comparative advantage improves the GTFP through both structural and technological effects. This study not only expands the research horizon of GTFP of food but also offers planning recommendations for technological advancement and structural adjustment in food production.

Seasonal Variations in Plant Species Diversity and Phylogenetic Diversity in Abandoned Farmland of China’s Huang–Huai Plain

Amidst urbanization and industrialization in China, abandoned farmland plays a crucial role in safeguarding plant diversity within agricultural landscapes. This study aimed to examine the patterns of seasonal variation in plant diversity within abandoned farmland habitats in the Huang–Huai Plain region. Nonparametric tests were employed to analyze plant species diversity and phylogenetic diversity across seasons. Redundancy analysis and linear regression were conducted to examine the associations between plant species composition, species diversity, phylogenetic diversity, and soil environmental factors. Our results showed that plant species diversity, richness, and phylogenetic diversity were highest in spring, followed by summer, and lowest in autumn. The phylogenetic structure of plant communities demonstrated a tendency to diverge in spring, become random in summer, and cluster in autumn. Soil available potassium and soil organic matter emerged as important factors influencing plant species composition. The content of soil organic matter and ammonium nitrogen level exhibited a significantly positive correlation with the species diversity and phylogenetic diversity of plants. This study underscores the significance of considering seasonal and temporal scales when investigating plant diversity and provides a theoretical basis for biodiversity conservation in agricultural landscapes.

Exploring the Potential of Solar-Induced Chlorophyll Fluorescence Monitoring Drought-Induced Net Primary Productivity Dynamics in the Huang-Huai-Hai Plain Based on the SIF/NPP Ratio

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF’s capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 > 0.8, p < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 > 0.5, p < 0.01 for SIF vs FAPAR and R2 > 0.6, p < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship.

Nitrogen reduction combined with ETc irrigation maintained summer maize yield and increased water and nitrogen use efficiency.

High rainfall and excessive urea application are counterproductive to summer maize growth requirements and lower grain yield and water/nitrogen (N) use efficiency. The objective of this study was to determine whether ETc irrigation based on summer maize demand and reduced nitrogen rate in the Huang Huai Hai Plain increased water and nitrogen use efficiency without sacrificing yield. To achieve this, we conducted an experiment with four irrigation levels [ambient rainfall (I0) and 50% (I1), 75% (I2), and 100% (I3) of actual crop evapotranspiration (ETc)] and four nitrogen rates [no nitrogen fertilizer (N0), recommended nitrogen rate of urea (NU), recommended nitrogen rate of blending controlled-release urea with conventional urea fertilizer (BCRF) (NC), and reduced nitrogen rate of BCRF (NR)] in 2016-2018. The results show that reduced irrigation and nitrogen rate reduced Fv/Fm, 13C-photosynthate, and nitrogen accumulation both in the kernel and plant. I3NC and I3NU accumulated higher 13C-photosynthate, nitrogen, and dry matter. However, 13C-photosynthate and nitrogen distribution to the kernel was decreased from I2 to I3 and was higher in BCRF than in urea. I2NC and I2NR promoted their distribution to the kernel, resulting in a higher harvest index. Compared with I3NU, I2NR increased root length density by 32.8% on average, maintaining considerable leaf Fv/Fm and obtaining similar kernel number and kernel weight. The higher root length density of I2NR of 40-60 cm promoted 13C-photosynthate and nitrogen distribution to the kernel and increased the harvest index. As a result, the water use efficiency (WUE) and nitrogen agronomic use efficiency (NAUE) in I2NR increased by 20.5%-31.9% and 11.0%-38.0% than that in I3NU, respectively. Therefore, 75%ETc deficit irrigation and BCRF fertilizer with 80% nitrogen rate improved root length density, maintained leaf Fv/Fm in the milking stage, promoted 13C-photosynthate, and distributed nitrogen to the kernel, ultimately providing a higher WUE and NAUE without significantly reducing grain yield.

Spatial–temporal variation of extreme precipitation in the Yellow–Huai–Hai–Yangtze Basin of China

Climate warming leads to frequent extreme precipitation events, which is a prominent manifestation of the variation of the global water cycle. In this study, data from 1842 meteorological stations in the Huang–Huai–Hai–Yangtze River Basin and 7 climate models of CMIP6 were used to obtain the historical and future precipitation data using the Anusplin interpolation, BMA method, and a non-stationary deviation correction technique. The temporal and spatial variations of extreme precipitation in the four basins were analysed from 1960 to 2100. The correlation between extreme precipitation indices and their relationship with geographical factors was also analysed. The result of the study indicates that: (1) in the historical period, CDD and R99pTOT showed an upward trend, with growth rates of 14.14% and 4.78%, respectively. PRCPTOT showed a downward trend, with a decreasing rate of 9.72%. Other indices showed minimal change. (2) Based on SSP1-2.6, the intensity, frequency, and duration of extreme precipitation changed by approximately 5% at SSP3-7.0 and 10% at SSP5-8.5. The sensitivity to climate change was found to be highest in spring and autumn. The drought risk decreased, while the flood risk increased in spring. The drought risk increased in autumn and winter, and the flood risk increased in the alpine climate area of the plateau in summer. (3) Extreme precipitation index is significantly correlated with PRCPTOT in the future period. Different atmospheric circulation factors significantly affected different extreme precipitation indices of FMB. (4) CDD, CWD, R95pD, R99pD, and PRCPTOT are affected by latitude. On the other hand, RX1day and RX5day are affected by longitude. The extreme precipitation index is significantly correlated with geographical factors, and areas above 3000 m above sea level are more sensitive to climate change.

Hatching and development of maize cyst nematode Heterodera zeae infecting different plant hosts

The occurrence, distribution, and rapid molecular detection technology of Heterodera zeaeKoshy et al. 1971, have been reported in China. We explored the biological characteristics of H. zeae sampled in Henan Province, China to understand its interaction with plants. Cysts and second-stage juveniles (J2s) were identified under an optical and scanning electron microscope, internal transcribed spacer (ITS) phylogenetic tree, and sequence characterized amplified region (SCAR)-PCR analyses. The optimum hatching temperatures of H. zeae were 30°C and 28°C, with cumulative hatching rates of 16.5 and 16.1%, respectively, at 30 days post-hatching (dph). The hatching rate of H. zeae eggs was improved by 20- and 50-time maize soil leachate and root juice, and 10-time root exudates. The hatching rate in 10-time root exudates was the highest (25.9%). The 10-time root exudates of maize and millet produced the highest hatching rate at 30 dph (25.9 and 22.9%, respectively), followed by wheat (19.9%), barley (18.3%), and rice (17.6%). Heterodera zeae developed faster in maize than in other crops. Fourth-stage juveniles (J4s) were detected in maize roots 8 days post-inoculation (dpi) at 28°C but not in other crops. Combined with hatching tests, the Huang–Huai–Hai summer maize region and the south and central-southwest mountainous maize areas are highly suitable for H. zeae in China. This is the first systematically study of the hatching and infection characteristics on different plant hosts of corn cyst nematode H. zeae in temperate regions. This study laid a theoretical foundation for the rapid spread and high environmental adaptability of corn cyst nematode.

Maize (Zea mays L.) responses to heat stress: Mechanisms that disrupt the development and hormone balance of tassels and pollen

AbstractGlobal climate changes have led to frequent and recurrent heat stress, which has seriously affected the world maize production. The experiment presented in this paper was carried out during the maize‐growing season in 2021 and 2022 at the Huang–Huai–Hai Region Maize Science and Technology Innovation Center (36°09′N, 117°09′E) in Tai'an, Shandong province, China. The test site is located in the semi‐humid warm temperate continental monsoon climate zone, soil is brown loam. We selected different heat‐sensitive maize varieties: heat‐tolerant variety Zhengdan 958 (ZD958) and heat‐sensitive variety Xianyu335 (XY335) to study the stress effects of high temperature on the development of tassel in the key stage of its differentiation (the 12‐leaf stage, V12). We found that exposure to heat stress during the V12 stage significantly reduced the tassel size and the anther dehiscence area, making it difficult to disperse pollen, thus effectively reducing pollen production. In addition, heat stress had significant negative effects on pollen development, resulting in deformed pollen and lower pollen viability and germination rate. Our results also showed that heat stress significantly increased the activities of key reactive oxygen species‐scavenging enzymes including superoxide dismutase, peroxidase and catalase, and that the malondialdehyde and H2O2 content in tassels exposed to heat stress were significantly higher than those control group, where it should be noted that for the XY335, these effects were more pronounced than for the ZD958. The endogenous hormone content of tassels exhibited a variety of responses. After heat stress, the zeatin and salicylic acid content in tassels of both maize varieties were significantly lower than those in the respective control groups, while the abscisic acid and gibberellin acid content were significantly higher. With respect to the jasmonic acid and 3‐indoleacetic acid content, the two maize varieties exhibited opposite responses: in tassels from ZD958 plants subjected to heat stress were higher than those in the respective control group, while in tassels from XY335 plants, they were lower. The observed changes in protective enzyme activities and endogenous hormone contents induced by heat stress significantly affected the development of maize tassels and significantly reduced the amount, activity and germination rate of pollen.

Can Agricultural Production Services Influence Smallholders’ Willingness to Adjust Their Agriculture Production Modes? Evidence from Rural China

The transformation of agricultural production modes is an inevitable trend in China’s agricultural development. It is important to study the willingness of smallholder farmers to adjust their agricultural production modes to promote the transformation of China’s agricultural production modes. Agricultural production services are an effective way to realize the organic linkage between smallholder farmers and modern agriculture. To analyze the impact of agricultural production services on smallholder farmers’ willingness to adjust their agricultural production modes, this study uses data from a thematic study of 590 smallholder farmers in the Huang–Huai–Hai regions in 2022 to measure the extent of smallholder farmers’ full-cycle adoption of agricultural production services from the perspective of regional cropping systems for the first time and analyzes the impact of agricultural production services on farmers’ willingness to adjust their agricultural production modes. The study results show that the extent of full-cycle adoption of agricultural production services significantly affects farmers’ willingness to adjust their agricultural production modes. For each unit increase in the extent of full-cycle adoption of agricultural production services in line with the regional cropping system, the probability of farmers choosing “farmland transfer” and “agricultural production trusteeship” becomes 11.31 and 7.24 times higher than that of choosing “self-growing”, respectively. Based on the research results, this paper proposes the following countermeasures: first, we should pay more attention to the supply and demand of agricultural production services, especially to support the key weak links in developing agricultural production services for the process of grain production. Second, we should undertake more active and effective publicity measures to make full use of expressions that are acceptable to farmers to improve their understanding of productive agricultural services further and enhance their willingness to adopt productive agricultural services.

Ensemble learning prediction of soybean yields in China based on meteorological data

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning. Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data, it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China (Northeast China and the Huang–Huai region), covering 34 years. Three effective machine learning algorithms (K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model’s generalizability was further improved through 5-fold cross-validation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error (MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

Effects of nitrogen application rate on the accumulation of 13C assimilates after flowering and water-nitrogen use efficiency of wheat under supplemental irrigation based on soil moisture.

This study aimed to explore nitrogen fertilizer management measures to synergistically improve wheat yields and water and nitrogen use efficiency under supplemental irrigation based on soil moisture in the Huang Huai winter wheat area. Wheat variety "Yannong 1212" was used as the test material. There were three nitrogen application levels, 150 kg·hm-2 (N1), 210 kg·hm-2 (N2), and 270 kg·hm-2 (the conventional nitrogen application rate in the Huang Huai winter wheat area, N3), with the relative soil water content of 0-40 cm of each treatment was supplemented to 70% at the jointing and flowering stages. We investigated the effects of nitrogen rates on photosynthetic characteristics of flag leaves after flowering, 13C assimilate accumulation and transport, and water and nitrogen use efficiency after flowering of wheat. The results showed that photosynthetic capacity of flag leaves in the N2 and N3 was significantly higher than that in N1 14-35 days after flowering, and that there was no significant diffe-rence between N2 and N3 treatments. The 13C isotope tracing results showed that the translocation amount of 13C assimilates in vegetative organs in N2 was 12.1% and 7.1% higher than that in N1 and N3, respectively. The distribution amount of 13C assimilates in grains at maturity was 10.1% and 5.3% higher than that of N1 and N3, respectively. The amount of nitrogen fertilizer affected water consumption, water consumption proportion, and total water consumption in different growth stages of wheat. Water consumption during the whole growth period showed no difference between N2 and N3 treatments, but both were significantly higher than that for N1. Water consumption and water consumption proportion of N2 were higher from the jointing to maturity stages, water use efficiency of N2 was 7.5% and 4.8%, and grain yield was 4.7% and 10.9% higher than that of N3 and N1 treatments, respectively. The partial productivity of nitrogen fertilizer was 34.6% higher in the N2 than that of N3. Considering wheat grain yield and water and nitrogen use efficiency, 210 kg·hm-2 nitrogen application was the best rate under water-saving condition of supplementary irrigation after soil moisture measurement in the study area.

Reconstructing the Historical Terrestrial Water Storage Variations in the Huang–Huai–Hai River Basin With Consideration of Water Withdrawals

The Huang-Huai-Hai River Basin in eastern China has suffered from severe water scarcity during recent decades due to the effects of climate change and human activities. Quantifying the changes in the amount of terrestrial freshwater available in this region and their driving factors is important for understanding hydrological processes and developing a sustainable water policy. This study proposed an ensemble learning model to reconstruct historical variations in the terrestrial water storage (TWS) of the Huang-Huai-Hai River Basin, China. The model was trained using the observations of the variations in TWS from the Gravity Recovery and Climate Experiment mission (GRACE) satellites, climatic driving, and human withdrawal datasets produced on a monthly scale. The variations in the reconstructed TWS were compared with the results of several land surface and hydrological models with a variety of in situ measurements of the soil water content. The contributions of the climate and human activity to the ensemble learning model were also quantified. The results show that the proposed approach generally outperforms the land surface and hydrological models examined in this study, matches the patterns in the GRACE solutions, and reconstructs past changes in TWS, which are consistent with the GRACE observations. Climatic variables are the most important in the ensemble learning model, with precipitation over the prior month being a critical factor. The model that includes human intervention tends to perform better than without it. Irrigation, industry, and domestic water withdrawals contribute equally to the model. This study provides a flexible and easily implementable model that can bridge the gap between GRACE observations and past changes in TWS. The model is applicable in areas with intense human activities, and the results have the potential to be assimilated into and enhance hydrological models.

Identification of tolerance to high density and lodging in short petiolate germplasm M657 and the effect of density on yield-related phenotypes of soybean

Soybean yield has traditionally been increased through high planting density, but investigating plant height and petiole traits to select for compact architecture, lodging resistance, and high yield varieties is an underexplored option for further improving yield. We compared the relationships between yield-related traits, lodging resistance, and petiole-associated phenotypes in the short petiole germplasm M657 with three control accessions during 2017–2018 in four locations in the Huang–Huai region, China. The results showed that M657 exhibited stable and high tolerance to high planting density and resistance to lodging, especially at the highest density (8×105 plants ha−1). The regression analysis indicated that a shorter petiole length was significantly associated with increased lodging resistance. The yield analysis showed that M657 achieved higher yields under higher densities, especially in the northern part of the Huang–Huai region. Among the varieties, there were markedly different responses to intra- and inter-row spacing designs with respect to both lodging and yield that were related to location and density. Lodging was positively correlated with planting density, plant height, petiole length, and number of effective branches, but negatively correlated with stem diameter, seed number per plant, and seed weight per plant. The yield of soybean was increased by appropriately increasing the planting density on the basis of the current soybean varieties in the Huang–Huai region. This study provides a valuable new germplasm resource for the introgression of compact architecture traits that are amenable to providing a high yield in high density planting systems, and it establishes a high-yield model of soybean in the Huang–Huai region.

Grain-filling characteristics and yield formation of wheat in two different soil fertility fields in the Huang-Huai-Hai Plain.

Clarifying factors that underpinning the variation in wheat yield components between high and middle soil fertility fields is critical to increase grain production and narrow yield gap for smallholder farming systems in the Huang–Huai–Hai Plain (3HP), which characterized by a large variation in soil fertility. Two-year field experiments were conducted to investigate wheat tillering, leaf photosynthesis, and grain filling characteristics in different soil fertility fields: high soil fertility field (HF) and middle soil fertility field (MF). Results showed that the spike formation rate in HF was 12.7%–13.0% higher than that in MF, leading to an 18.0%–19.8% increase in spike number. In addition, HF improved canopy light interception and leaf photosynthesis characteristics after anthesis and delayed leaf senescence, contributing to the increase in both the active grain filling period and grain filling rate. This resulted in a higher 1,000 grain weight in HF, which was 8.2%–8.3% higher than that in MF. Compared to MF, HF obtained higher yields at 9,840 kg ha−1 in 2017/18 and 11,462 kg ha−1 in 2018/19, respectively. In summary, higher spike number and 1,000-grain weight, which were mediated by spike-formation rate, maximization of light interception and improved leaf photosynthesis. These results would have important implications for narrowing yield gap between MF and HF in the 3HP.

Assessing the impact of climate change in the wheat–maize cropping system across the Huang–Huai–Hai Plain under future climate scenarios

Abstract General circulation models suggest that changes in climatic parameters will have an effect on food production globally. Therefore, this study assessed the impact of climate change on wheat–maize rotation areas in the Huang–Huai–Hai Plain (3H Plain). The projections generated suggest an increase in precipitation during the wheat growth period (WGP) by 1.33–4.16% (2.6–8.3 mm) and 3.13–8.16% (6.2–18.0 mm) under RCP4.5 and RCP8.5, respectively, relative to the base period (1981–2016). Across the 3H Plain, the mean temperature during the WGP is projected to increase between 1.17–1.21 and 1.17–1.28 °C under RCP4.5 and RCP8.5, respectively. The projections during the maize growth period (MGP) indicate an increase in the temperature between 1.29–1.92 and 1.84–2.08 °C under RCP4.5 and RCP8.5, respectively. During the MGP, precipitation is also projected to increase by 7.41–9.73% (33.6–45.1 mm) and 6.63–14.78% (29.8–72.7 mm) under RCP4.5 and RCP8.5 scenarios, respectively. For each 1% change in climatic factors, the comprehensive effect on yield was projected to be 0.65 and 0.58% for wheat and −1.08 and −1.11% for maize, under RCP4.5 and RCP8.5, respectively, when other factors were kept constant. The change in water resources will be insignificant during the WGP and more pronounced during the MGP. The study provides an overview of changes in meteorological parameters and scientific evidence for climate change adaptation in the wheat–maize cropping system.