Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient

The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

A combination of straw incorporation and polymer-coated urea offsets soil ammonia and nitrous oxide emissions in winter wheat fields

The combined effects of straw incorporation(SI)and polymer-coated urea(PCU)application on soil ammonia(NH_(3))and nitrous oxide(N_(2)O)emissions from agricultural fields have not been comprehensively evaluated in Northwest China.We conducted a two-year field experiment to assess the effects of combining SI with either uncoated urea(U)or PCU on soil NH_(3)emissions,N_(2)O emissions,winter wheat yields,yield-scaled NH_(3)(/NH_(3)),and yield-scaled N_(2)O(/N_(2)O).Five treatments were investigated,no nitrogen(N)fertilizer(N_(0)),U application at 150 kg N ha-1 with and without SI(SI+U and S_(0)+U),and PCU application at 150 kg N ha^(-1) with and without SI(SI+PCU and S_(0)+PCU).The results showed that the NH_(3);emissions increased by 20.98-34.35%following Sl compared to straw removal,mainly due to increases in soil ammonium(NH_(4)^(+)-N)content and water-flled pore space(WFPS).SI resulted in higher N_(2)O emissions than under the So scenario by 13.31-49.23%due to increases in soil inorganic N(SIN)contents,WFPS,and soil microbial biomass.In contrast,the PCU application reduced the SIN contents compared to the U application,reducing the NH_(3)and N_(2)O emissions by 45.99-58.07 and 18.08-53.04%,respectively.Moreover,no significant positive effects of the SI or PCU applications on the winter wheat yield were observed.The lowest /NH_(3) and /N_(2)O values were observed under the S_(0)+PCU and SI+PCU treatments.Our results suggest that single PCU applications and their combination with straw are the optimal agricultural strategies for mitigating gaseous N emissions and maintaining optimal winter wheat yields in Northwest China.

Comparison on Winter Wheat Yield Estimating Models Based on Radarsat-2 and HJ Satellite in Huaihe River Region

The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.

Effect of N Fertilization on Grain Yield of Winter Wheat and Apparent N Losses

Excessive nitrogen (N) fertilizer application to winter wheat is a common problem on the North China Plain. To determine the optimum fertilizer N rate for winter wheat production while minimizing N losses, field experiments were conducted for two growing seasons at eight sites, in Huimin County, Shandong Province, from 2001 to 2003. The optimum N rate for maximum grain yield was inversely related to the initial soil mineral N content (Nmin) in the top 90 cm of the soil profile before sowing. There was no yield response to the applied N at the three sites with high initial soil mineral N levels (average 212 kg N ha-1). The average optimum N rate was 96 kg N ha-1 for the five sites with low initial soil Nmin (average 155 kg N ha-1) before sowing. Residual nitrate N in the top 90 cm of the soil profile after harvest increased with increasing fertilizer N application rate. The apparent N losses during the wheat-growing season also increased with increasing N application rate. The average apparent N losses with the optimum N rates were less than 15 kg N ha-1, whereas the farmers' conventional N application rate resulted in losses of more than 100 kg N ha-1. Therefore, optimizing N use for winter wheat considerably reduced N losses to the environment without compromising crop yields.

Integrating remotely sensed water stress factor with a crop growth model for winter wheat yield estimation in the North China Plain during 2008–2018

Accurate estimation of regional-scale crop yield under drought conditions allows farmers and agricultural agencies to make well-informed decisions and guide agronomic management. However, few studies have focused on using the crop model data assimilation(CMDA) method for regional-scale winter wheat yield estimation under drought stress and partial-irrigation conditions. In this study, we developed a CMDA framework to integrate remotely sensed water stress factor(MOD16 ET PET) with the WOFOST model using an ensemble Kalman filter(En KF) for winter wheat yield estimation at the regional scale in the North China Plain(NCP) during 2008–2018. According to our results, integration of MOD16 ET PETwith the WOFOST model produced more accurate estimates of regional winter wheat yield than open-loop simulation. The correlation coefficient of simulated yield with statistical yield increased for each year and error decreased in most years, with r ranging from 0.28 to 0.65 and RMSE ranging from 700.08 to1966.12 kg ha. Yield estimation using the CMDA method was more suitable in drought years(r = 0.47, RMSE = 919.04 kg ha) than in normal years(r = 0.30, RMSE = 1215.51 kg ha). Our approach performed better in yield estimation under drought conditions than the conventional empirical correlation method using vegetation condition index(VCI). This research highlighted the potential of assimilating remotely sensed water stress factor, which can account for irrigation benefit, into crop model for improving the accuracy of winter wheat yield estimation at the regional scale especially under drought conditions, and this approach can be easily adapted to other regions and crops.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

Yield and water use responses of winter wheat to irrigation and nitrogen application in the North China Plain

With increasing water shortage resources and extravagant nitrogen application, there is an urgent need to optimize irrigation regimes and nitrogen management for winter wheat（Triticum aestivum L.） in the North China Plain（NCP）. A 4-year field experiment was conducted to evaluate the effect of three irrigation levels（W1, irrigation once at jointing stage; W2, irrigation once at jointing and once at heading stage; W3, irrigation once at jointing, once at heading, and once at filling stage; 60 mm each irrigation） and four N fertilizer rates（N0, 0; N1, 100 kg N ha-（-1）; N2, 200 kg N ha-（-1）; N3, 300 kg N ha-（-1）） on wheat yield, water use efficiency, fertilizer agronomic efficiency, and economic benefits. The results showed that wheat yield under W2 condition was similar to that under W3, and greater than that under W1 at the same nitrogen level. Yield with the N1 treatment was higher than that with the N0 treatment, but not significantly different from that obtained with the N2 and N3 treatments. The W2 N1 treatment resulted in the highest water use and fertilizer agronomic efficiencies. Compared with local traditional practice（W3 N3）, the net income and output-input ratio of W2 N1 were greater by 12.3 and 19.5%, respectively. These findings suggest that two irrigation events of 60 mm each coupled with application of 100 kg N ha-（–1） is sufficient to provide a high wheat yield during drought growing seasons in the NCP.

Integrated management strategy for improving the grain yield and nitrogen-use efficiency of winter wheat

Understanding of how combinations of agronomic options can be used to improve the grain yield and nitrogen use efficiency（NUE） of winter wheat is limited. A three-year experiment involving four integrated management strategies was conducted from 2013 to 2015 in Tai＇an, Shandong Province, China, to evaluate changes in grain yield and NUE. The integrated management treatments were as follows： current practice（T1）; improvement of current practice（T2）; high-yield management（T3）, which aimed to maximize grain yield regardless of the cost of resource inputs; and integrated soil and crop system management（T4） with a higher seeding rate, delayed sowing date, and optimized nutrient management. Seeding rates increased by 75 seeds m^-2 with each treatment from T1（225 seeds m^-2） to T4（450 seeds m^-2）. The sowing dates were delayed from T1（5 th Oct.） to T2 and T3（8 th Oct.）, and to T4 treatment（12 th Oct.）. T1, T2, T3, and T4 received 315, 210, 315, and 240 kg N ha^-1, 120, 90, 210 and 120 kg P2O5 ha^-1, 30, 75, 90, and 45 kg K2O ha^-1, respectively. The ratio of basal application to topdressing for T1, T2, T3, and T4 was 6：4, 5：5, 4：6, and 4：6, respectively, with the N topdressing applied at regreening for T1 and at jointing stage for T2, T3, and T4. The P fertilizers in all treatments were applied as basal fertilizer. The K fertilizer for T1 and T2 was applied as basal fertilizer while the ratio of basal application to topdressing（at jointing stage） of K fertilizer for both T3 and T4 was 6：4. T1, T2, T3, and T4 were irrigated five, four, four and three times, respectively. Treatment T3 produced the highest grain yield among all treatments over three years and the average yield was 9 277.96 kg ha^-1. Grain yield averaged across three years with the T4 treatment（8 892.93 kg ha^-1） was 95.85% of that with T3 and was 21.72 and 6.10% higher than that with T1（7 305.95 kg ha^-1） and T2（8 381.41 kg ha^-1）, respectively. Treatment T2 produced the highest NUE of all the integrated treatments. The NUE with T4 was 95.36% of that with T2 and was 51.91 and 25.62% higher than that with T1 and T3, respectively. The N uptake efficiency（UPE） averaged across three years with T4 was 50.75 and 16.62% higher than that with T1and T3, respectively. The N utilization efficiency（UTE） averaged across three years with T4 was 7.74% higher than that with T3. The increased UPE with T4 compared with T3 could be attributed mostly to the lower available N in T4, while the increased UTE with T4 was mainly due to the highest N harvest index and low grain N concentration, which consequently led to improved NUE. The net profit for T4 was the highest among four treatments and was 174.94, 22.27, and 28.10% higher than that for T1, T2, and T3, respectively. Therefore, the T4 treatment should be a recommendable management strategy to obtain high grain yield, high NUE, and high economic benefits in the target region, although further improvements of NUE are required.

Identifying the critical phosphorus balance for optimizing phosphorus input and regulating soil phosphorus effectiveness in a typical winter wheat-summer maize rotation system in North China

Phosphorus(P)is a nonrenewable resource and a critical element for plant growth that plays an important role in improving crop yield.Excessive P fertilizer application is widespread in agricultural production,which not only wastes phosphate resources but also causes P accumulation and groundwater pollution.Here,we hypothesized that the apparent P balance of a crop system could be used as an indicator for identifying the critical P input in order to obtain a high yield with high phosphorus use efficiency(PUE).A 12-year field experiment with P fertilization rates of 0,45,90,135,180,and 225 kg P_(2)O_(5)ha^(-1)was conducted to determine the crop yield,PUE,and soil Olsen-P value response to P balance,and to optimize the P input.Annual yield stagnation occurred when the P fertilizer application exceeded a certain level,and high yield and PUE levels were achieved with annual P fertilizer application rates of 90-135 kg P_(2)O_(5)ha^(-1).A critical P balance range of 2.15-4.45 kg P ha^(-1)was recommended to achieve optimum yield with minimal environmental risk.The critical P input range estimated from the P balance was 95.7-101 kg P_(2)O_(5)ha^(-1),which improved relative yield(>90%)and PUE(90.0-94.9%).In addition,the P input-output balance helps in assessing future changes in Olsen-P values,which increased by 4.07 mg kg^(-1)of P for every 100 kg of P surplus.Overall,the P balance can be used as a critical indicator for P management in agriculture,providing a robust reference for limiting P excess and developing a more productive,efficient and environmentally friendly P fertilizer management strategy.

Fitting of Water Requirement and Yield of Winter Wheat in North China Plain Based on Artificial Neural Network

The fitting of water requirement and yield during the growth period of winter wheat can improve yield effectively and improve irrigation water use efficiency with a certain amount of resource input. This paper selects the irrigation amount, precipitation and yield of winter wheat at the Wuqiao Scientific Observation and Experimental Station. Fitting the water requirement and yield of winter wheat based on three types of artificial neural networks. This paper uses support vector machine (SVM), thought evolution algorithm to optimize BP neural network (MAE-BP) and generalized regression neural network (GRNN) to fit the water requirement and yield of two crops. The SVM is the model with the highest fitting accuracy among the three models, the RMSE, MAE, NS and R2 between predictive value and true value are 7.45 kg/hectares, 213.64 kg/hectares, 0.8086, 0.9409 respectively.

Influences of wide-narrow seeding on soil properties and winter wheat yields under conservation tillage in North China Plain

In order to promote the winter wheat yield and guarantee seeding quality in double-cropping system,no-tillage or reduced tillage planting modes with different row spacing have been implemented to result in different levels of yield.A three-year(2012-2015)field experiment was conducted on the experimental farm at Zhuozhou of Hebei Province in North China Plain to compare winter wheat yield from the two planting modes:wide-narrow row space planting mode(WN)and uniform row space planting mode(UR)Both planting modes were performed under reduced tillage conditions with straw mulching.The results showed that in North China Plain WN had positive impacts on crop yield,yield components,leaf area index(LAI)and intercepted photosynthetically active radiation(IPAR)index.Comparing with the UR,IPAR and LAI index for WN were enhanced by 4.8%and 5.2%,respectively.The average yield for WN was 7.2%,significantly greater than that of UR under the same quantity and density.In addition,for WN mode,machinery could pass through with less blocking under large amount of straw mulching,which largely improved tillage efficiency and potentially popularized the conservation tillage technology in North China plain.It is therefore recommended that wide-narrow row space planting mode(WN)combined with reduced tillage and straw mulching be more suitable for conservation tillage in double-cropping pattern areas in North China Plain.

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

Agricultural drought threatens food security.Numerous remote-sensing drought indices have been developed,but their different principles,assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas.Here,we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces,northern China(predominantly rain-fed and irrigated agriculture,respectively):vegetation health index(VHI),temperature vegetation dryness index(TVDI)and drought severity index(DSI).We compared the agreement between these indices and the standardized precipitation index(SPI),soil moisture,winter wheat yield and National Meteorological Drought Monitoring(NMDM)maps.On average,DSI outperformed the other indices,with stronger correlations with SPI and soil moisture.DSI also corresponded better with soil moisture and NMDM maps.The jointing and grain-filling stages of winter wheat are more sensitive to water stress,indicating that winter wheat required more water during these stages.Moreover,the correlations between the drought indices and SPI,soil moisture,and winter wheat yield were generally stronger in Shaanxi province than in Henan province,suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.

Responses of Irrigated Winter Wheat Yield in North China to Increased Temperature and Elevated CO2Concentration

North China is one of the main regions of irrigated winter wheat production in China. Climate warming is apparent in this region, especially during the growing season of winter wheat. To understand how the yield of irrigated winter wheat in North China might be affected by climate warming and CO2 concentration enrichment in future, a set of manipulative field experiments was conducted in a site in the North China Plain under increased temperature and elevated CO2 concentration by using open top chambers and infrared radiator heaters. The results indicated that an average temperature increase of 1.7℃ in the growing season with CO2 concentration of 560 μmol mol-1 did not reduce the yield of irrigated winter wheat. The thousand- kernel weight of winter wheat did not change significantly despite improvement in the filling rate, because the increased temperature shortened the duration of grain filling. The number of effective panicles and the grain number per ear of winter wheat did not show significant changes. There was a large increase in the shoot biomass because of the increase in stem number and plant height. Consequently, under the prescribed scenario of asymmetric temperature increases and elevated CO2 concentration, the yield of irrigated winter wheat in North China is not likely to change significantly, but the harvest index of winter wheat is likely to be greatly reduced.