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Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale spatial distribution with high accuracy for planting dates is essential for the widespread application of crop growth models. In this study, a planting date prediction method based on environmental similarity was developed in accordance with the third law of geography. Spring maize planting date observations from 124 agricultural meteorological experiment stations in China over the years 1992–2010 were used as the data source. Samples spanning from 1992 to 2009 were allocated as training data, while samples from 2010 constituted the independent validation set. The results indicated that the root mean square error (RMSE) for spring maize planting date based on environmental similarity was 10 days, which is better than that of multiple regression analysis (RMSE = 13 days) in 2010. Additionally, when applied at varying scales, the accuracy of national-scale prediction was better than that of regional-scale prediction in areas with large differences in planting dates. Consequently, the method based on environmental similarity can effectively and accurately estimate planting date parameters at multiple scales and provide reasonable parameter support for large-scale crop growth modelling. [ABSTRACT FROM AUTHOR]

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region. [ABSTRACT FROM AUTHOR]

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing. [ABSTRACT FROM AUTHOR]

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity. [ABSTRACT FROM AUTHOR]

Seasonal drought happens frequently in the lower slope hilly areas of China, which leads to a huge economic loss to China's famous tea production. An accurate determination of the evapotranspiration (ET) value of different seasons can provide a crucial decision parameter for irrigation management. The surface renewal (SR) method is an accurate and inexpensive method compared with the eddy covariance (EC) method, which is widely used to calculate the sensible heat flux (H). The latent heat flux (LE) evapotranspiration can be estimated indirectly when combined with the energy–balance equation. This research investigated the traditional and improved calculation methods of H (SRsnyder and SRchen), based on the surface renewal theory, over a tea field for one year. The calculation accuracy was obtained from the statistical analysis between the SR and EC methods. Different months' applicability was evaluated to determine the best calculation method for the tea field. The traditional calculation method (SRsnyder) is based on the van slope model using the second, third, and fifth structure function. The improved SR model (SRchen) introduces a third order temperature function and friction velocity for calculation. The results indicate that SRchen shows a good calculation accuracy of H in the spring seasons (February to April), summer (May to July), and autumn (August to October). The determination coefficients of regression analysis (R2) ranges were [0.66, 0.88] with most values greater than 0.8. The root mean square error (RMSE) ranges were [34.15, 69.22] W/m2. However, during this period, SRsnyder had a poor calculation accuracy of H, and the range of R2 was [0.45, 0.74] with the RMSE ranges of [32.28, 63.25] W/m2. In the winter (November to January), the calculation accuracy of both models was relatively low with R2 almost 30% lower than that of other seasons. Therefore, this study suggests the use of the SRchen method to estimate the H of a tea field ecosystem in the low slope hilly area of the Yangtze River region in the spring, summer, and autumn. While in the winter, the SRsnyder method is recommended. [ABSTRACT FROM AUTHOR]

Rubber (Hevea brasiliensis) plantations have high water consumption through evapotranspiration, which can contribute to water scarcity. In addition, there is a lack of spatial observation data and estimation methods for evapotranspiration (ET) for rubber plantations. To alleviate the water stress of expanding rubber plantations caused by seasonal drought in Xishuangbanna, Southwest China, an up to 7 days crop evapotranspiration (ETc) forecast method, "Kc-ET0" for rubber plantations with limited meteorological data, was proposed by using rubber crop coefficient Kc and public weather forecasts. The results showed that the average absolute error (MAE) of forecasted ETc was 0.68 mm d−1, the root mean square error (RMSE) was 0.85 mm d−1, and the average correlation coefficient (R) was 0.69 during the rainy season, while during the dry season these metrics were 0.52 mm d−1, 0.68 mm d−1, and 0.85, respectively. The accuracy of ETc forecast in the dry season was higher. Additionally, Kc was the main factor influencing the accuracy of rubber plantations ETc forecast, while the accuracy of the temperature forecast and the chosen Hargreaves-Samani (HS) model were also considerable. Our results suggested that the "Kc-ET0" short-term rubber plantation ETc forecasting method shows good performance and acceptable accuracy, especially in the dry season. The study provides an important basis for creating ET-based irrigation scheduling for improving regional-scale water management in high water consumption rubber plantations. [ABSTRACT FROM AUTHOR]

Gynaephora alpherakii (G. alpherakii) is one of the dominant pests in the alpine meadow; its outbreak has substantial impacts on grass vegetation production and carrying capacity. The increasing frequency of G. alpherakii outbreaks will take place with global warming and human activities. Therefore, it is crucial to identify inhabitable areas to apply prevention measures efficiently and facilitate sustainable grassland management. In this study, field measurements of G. alpherakii (from 2010 to 2021) were used to explore the relationships between its density and environmental factors in the National Park of Qilian Mountain, China. Then, the inhabitability area was mapped. The results showed that (1) eight of the twenty-five factors studied (average and maximum value of solar radiation; minimum, standard deviation, and average value of normalized difference vegetation index (NDVI); maximum precipitation; and digital elevation model) had a greater influence on G. alpherakii density. (2) Among all of the estimation models, models based on the average of solar radiation, the minimum of NDVI, and the maximum precipitation were better than other factors, with a higher determination coefficient (R2) of 0.53–0.66 and a lower root mean square error (RMSE) of 40.54–47.32 head/m2. Models based on rest factors had a lower accuracy, with R2 within 0.38–0.49 and an RMSE of 50.91–58.68 head/m2. (3) The inhabitable area which was most suited for G. alpherakii growth, development, and frequent outbreaks was located in the northeast of the research area (24.72%), with inhabitability decreasing from southeast to northwest. This method is helpful for clarifying the distribution regions and occurrence dynamics of the G. alpherakii in the alpine meadow. The spatial distribution of G. alpherakii in the National Park of Qilian Mountain can also be clearly defined by using this method, which can provide data support for its prevention and control. [ABSTRACT FROM AUTHOR]

Excessive nitrogen (N) application rate led to low N use efficiency and environmental risks in a potato (Solanum tuberosum L.) production system in northwest China. Process-based models are effective tools in agroecosystems that can be used to optimize integrated management practices for improving potato yield and N use efficiency. The objectives of this study were (1) to calibrate and evaluate the DeNitrification-DeComposition (DNDC) and soil Water Heat Carbon Nitrogen Simulator of Vegetable (WHCNS_Veg) models using the measurements of potato yield, above-ground biomass, N uptake, soil moisture and temperature, and soil inorganic N based on a field experiment in northwest China (2017–2020) and (2) to explore optimal management practices for improving yield and N use efficiency under long-term climate variability (1981–2020). Both models overall performed well in simulating potato tuber yield (normalized root mean square error (NRMSE) = 5.4–14.9%), above-ground biomass (NRMSE = 6.0–14.7%), N uptake (NRMSE = 18.1–25.6%), daily soil temperature (index of agreement (d) > 0.9 and Nash–Sutcliffe efficiency (EF) > 0.8), and acceptable in-soil moisture and inorganic N content (d > 0.6 and EF > ‒1) for N-applied treatments. However, the two models underestimated tuber yield and soil N content for no N fertilization treatment which was partially attributed to the underestimated soil N mineralization rate under N stress conditions. The sensitivity analysis showed that the greatest tuber yield and N use efficiency were achieved at the N rate of 150–180 kg ha−1 with 2–3 splits, fertilization depth of 15–25 cm, and planting date of 25 April to 10 May in both models. This study highlights the importance of integrated management strategies in obtaining high N use efficiency and crop yield in potato production systems. [ABSTRACT FROM AUTHOR]

Basic soil productivity (BSP) is the ability of a soil, in its normal environment to support plant growth. However, the assessment of BSP remains controversial. The aim of this study is to quantify and analyze the trends of BSP in winter wheat seasons using the decision support system for agrotechnologie transfer (DSSAT) model under a long-term fertilization experiment in the dark loessal soil region of the Loess Plateau of China. In addition, we evaluated the contribution percentage of BSP to yield and its influencing factors. A long-term fertilization experiment with a winter wheat/spring maize rotation was established in 1979 in a field of the Gaoping Agronomy Farm, Pingliang, Gansu, China, including six treatments: (1) no fertilizer as a control (CK), (2) chemical nitrogen fertilizer input annually (N), (3) chemical nitrogen and phosphorus fertilizer input annually (NP), (4) straw return and chemical nitrogen fertilizer input annually plus phosphorus fertilizer added every second year (SNP), (5) manure input annually (M), and (6) M plus N and P fertilizers added annually (MNP). The application of the DSSAT-CERES-Wheat model showed a satisfactory performance with good Wilmott d-index (0.78~0.95) and normalized root mean square error (NRMSE) (7.03%~18.72%) values for the tested genetic parameters of winter wheat. After the 26-years experiment, the yield by BSP of winter wheat under the M and MNP treatment significantly increased, at the rate of 2.7% and 3.82% a year, respectively, whereas that of CK and N treatments significantly decreased, at the rate of 0.23% and 3.03%. Moreover, the average contribution percentage of BSP to yield was 47.0%, 39.4%, 56.3%, 50.0%, and 61.9% in N, NP, SNP, M, and MNP treatments, respectively. In addition, soil organic carbon contents were the main controls of BSP under the different fertilization conditions in the dark loessial soil area. As a result, the combined application of organic fertilizer or straw and chemical fertilizer can be an effective form of fertilization management to greatly enrich basic soil productivity, continually promote the contribution percentage of BSP, and ultimately increase crop yield. [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) is experiencing serious groundwater level decline and groundwater nitrate contamination due to excessive water pumping and application of nitrogen (N) fertilizer. In this study, grain yield, water and N use efficiencies under different cropping systems including two harvests in 1 year (winter wheat–summer maize) based on farmer (2H1Y)FP and optimized practices (2H1Y)OPT, three harvests in 2 years (winter wheat–summer maize–spring maize, 3H2Y), and one harvest in 1 year (spring maize, 1H1Y) were evaluated using the water-heat-carbon-nitrogen simulator (WHCNS) model. The 2H1YFP system was maintained with 100% irrigation and fertilizer, while crop water requirement and N demand for other cropping systems were optimized and managed by soil testing. In addition, a scenario analysis was also performed under the interaction of linearly increasing and decreasing N rates, and irrigation levels. Results showed that the model performed well with simulated soil water content, soil N concentration, leaf area index, dry matter, and grain yield. Statistically acceptable ranges of root mean square error, Nash–Sutcliffe model efficiency, index of agreement values close to 1, and strong correlation coefficients existed between simulated and observed values. We concluded that replacing the prevalent 2H1YFP with 1H1Y would be ecofriendly at the cost of some grain yield decline. This cropping system had the highest average water use (2.1 kg m−3) and N use efficiencies (4.8 kg kg–1) on reduced water (56.64%) and N (81.36%) inputs than 2H1YFP. Whereas 3H2Y showed insignificant results in terms of grain yield, and 2H1YFP was unsustainable. The 2H1YFP system consumed a total of 745 mm irrigation and 1100 kg N ha–1 in two years. When farming practices were optimized for two harvests in 1 year system (2H1Y)OPT, then grain yield improved and water (18.12%) plus N (61.82%) consumptions were minimized. There was an ample amount of N saved, but water conservation was still unsatisfactory. However, considering the results of scenario analyses, it is recommended that winter wheat would be cultivated at <200 mm irrigation by reducing one irrigation event. [ABSTRACT FROM AUTHOR]