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.

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.

Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain

The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer.Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland.However,the previous work mainly focused on crops in single cropping system,less work was done in multiple cropping systems.This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index(LAI),latent heat flux(LHF)and sensible heat flux(SHF)of winter wheat growing in double cropping system in the North China Plain(NCP).The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP.The phenophases,LAI and heat fluxes in 2004–2006 at Yucheng Station,Shandong Province,China were used to calibrate the model.The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station,Hebei Province,China in 2009–2010.The results showed the significant accuracy of the calibrated model in simulating these variables,with which the R2,root mean square error(RMSE)and index of agreement(IOA)between simulated and observed variables were obviously improved than the original code.The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model.Overall,the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.

A simulation of winter wheat crop responses to irrigation management using CERES-Wheat model in the North China Plain

To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency（WUE） responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications（full irrigation） using historical weather data from crop seasons over 33 years（1981–2014）. The data were classified into three types according to seasonal precipitation： 〈100 mm, 100–140 mm, and 〉140 mm. Our results showed that the grain and biomass yield, harvest index（HI） and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage（T3） resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage（T1） improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.

Winter Wheat Drought Monitoring and Comprehensive Risk Assessment： Case Study of Xingtai Administrative District in North China

Drought monitoring is the base for drought coping and adaptation. Xingtai is located in North China＇s key winter wheat production areas where drought is severe and frequent. The rainfall during winter wheat growing season is just about 1/3 of total demand. Xingtai has typical mountainous, hilly and plain agricultural zones, compound rain-fed and irrigated farming patterns. The winter wheat irrigation has heavily depended on overdraw of groundwater in recent decades. In the study, the MODIS （Moderate-Resolution Imaging Spectroradiometer） images taken at the key winter wheat growing season （Mar. to May） in normal rainfall year （2006） were selected, extracted NDVI （Normalized Difference Vegetation Index） and LST （Land Surface Temperature） data, calculated TVDI （Temperature and Vegetation Drought Index）, classified and mapped winter wheat drought intensity. Further, based on TVDI, a CDRA （Comprehensive Drought Risk Assessment） model for winter wheat drought disaster risk assessment was constructed and zoning was made. Verified by winter wheat yield, the risk zoning by CDRA is consistent with actual crop failure space. This method can be used in drought risk management.

Mapping Spatial and Temporal Variations of Leaf Area Index for Winter Wheat in North China

Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.

REMOTE SENSING BASED ESTIMATION SYSTEM FOR WINTER WHEAT YIELD IN NORTH CHINA PLAIN

This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechniques are described systematically about winter wheat yield estimation system, including automatically extractingwheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. Pattern recognition technique was applied to extract sown area using TM data. Temporal NDVI(Normal Division Vegetation Index) profiles were produced from 8 - 12 times AVHRR data during wheat growth dynamically. A remote sensing yield model for large area was developed based on greenness accumulation, temperature andgreenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which consists of 4 subsystems, namely databases management, image processing, models bank management and production prediction system.The accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.

Risk Assessment and Zoning of Winter Wheat Drought in Anhui Province

[Objective]The study aimed to assess and zone the drought risk of winter wheat in Anhui Province. [Method] The zoning factors were chosen from three aspects of disaster-causing factors,disaster-bearing bodies and environment conducive to drought,and then their data were standardized,rasterized and graded. Using analytic hierarchy process（ AHP）,we determined the weight of each index at various levels and then established the assessment models of drought intensity,sensitivity,vulnerability and resistance of winter wheat in the whole growth period and at heading and filling stage. Finally,the zoning map of drought risk for winter wheat in Anhui Province was obtained using the farmland data mask of Anhui Province. [Result]The drought risk of winter wheat in Anhui Province in the whole growth period and at heading and filling stage was divided into six grades,which reflected the distribution characteristics and regional difference of drought risk for winter wheat in Anhui Province. Drought risk was the maximum in the main producing areas of winter wheat in the north of Huaihe River,followed by the area along Huaihe River and the area between Yangtze River and Huaihe River,while the drought risk of winter wheat was very low in the south of Anhui Province. The drought risk of winter wheat was markedly affected by the sensitivity to drought,vulnerability and the drought resistance of winter wheat. [Conclusion] The research could provide scientific references for rational distribution of winter wheat and establishment of strategies for disaster prevention and mitigation.

Change of winter wheat planting area and its impacts on groundwater depletion in the North China Plain

The North China Plain is one of the most water-stressed areas in China. Irrigation of winter wheat mainly utilizes groundwater resources, which has resulted in severe environmental problems. Accurate estimation of crop water consumption and net irrigation water consumption is crucial to guarantee the management of agricultural water resources. An actual crop evapotranspiration(ET) estimation model was proposed, by combining FAO Penman-Monteith method with remote sensing data. The planting area of winter wheat has a significant impact on water consumption; therefore, the planting area was also retrieved. The estimated ET showed good agreement with field-observed ET at four stations. The average relative bias and root mean square error(RMSE) for ET estimation were –2.2% and 25.5 mm, respectively. The results showed the planting area and water consumption of winter wheat had a decreasing trend in the Northern Hebei Plain(N-HBP) and Southern Hebei Plain(S-HBP). Moreover, in these two regions, there was a significant negative correlation between accumulated net irrigation water consumption and groundwater table. The total net irrigation water consumption in the N-HBP and S-HBP accounted for 12.9×10~9 m^3 and 31.9×10~9 m^3 during 2001–2016, respectively. Before and after 2001, the decline rate of groundwater table had a decreasing trend, as did the planting area of winter wheat in the N-HBP and S-HBP. The decrease of winter wheat planting area alleviated the decline of groundwater table in these two regions while the total net irrigation water consumption was both up to 28.5×10~9 m^3 during 2001–2016 in the Northwestern Shandong Plain(NW-SDP) and Northern Henan Plain(N-HNP). In these two regions, there was no significant correlation between accumulated net irrigation water consumption and groundwater table. The Yellow River was able to supply irrigation and the groundwater table had no significant declining trend.

Effects of Drought on Winter Wheat Yield in North China During 2012-2100

Winter wheat is one of China＇s most important staple food crops, and its production is strongly influenced by weather, especially droughts. As a result, the impact of drought on the production of winter wheat is associated with the food security of China. Simulations of future climate for scenarios A2 and AIB provided by GFDL-CM2, MPI_ECHAM5, MRI_CGCM2, NCAR_CCSM3, and UKMO_HADCM3 during 2001- 2100 are used to project the influence of drought on winter wheat yields in North China. Winter wheat yields are simulated using the crop model WOFOST （WOrld FOod STudies）. Future changes in temperature and precipitation are analyzed. Temperature is projected to increase by 3.9-5.5 ℃ for scenario A2 and by 2.9-5.1 ℃ for scenario A1B, with fairly large interannual variability. Mean precipitation during the growing season is projected to increase by 16.7 and 8.6 mm （10 yr）-1, with spring precipitation increasing by 9.3 and 4.8 mm （10 yr）-1 from 2012 2100 for scenarios A2 and AIB, respectively. For the next 10-30 years （2012- 2040）, neither the growing season precipitation nor the spring precipitation over North China is projected to increase by either scenario. Assuming constant winter wheat varieties and agricultural practices, the influence of drought induced by short rain on winter wheat yields in North China is simulated using the WOFOST crop model. The drought index is projected to decrease by 9.7% according to scenario A2 and by 10.3% according to scenario A1B during 2012 2100. This indicates that the drought influence on winter wheat yields may be relieved over that period by projected increases in rain and temperature as well as changes in the growth stage of winter wheat. However, drought may be more severe in the near future, as indicated by the results for the next 10 30 years.

Impact of Droughts on Winter Wheat Yield in Different Growth Stages during 2001–2016 in Eastern China

Remote sensing can provide near real-time and dynamic monitoring of drought. The drought severity index(DSI), based on the normalized difference vegetation index(NDVI) and evapotranspiration/potential evapotranspiration(ET/PET), has been used for drought monitoring. This study examined the relationship between the DSI and winter wheat yield for prefecture-level cities in five provinces of eastern China during 2001–2016. We first analyzed the spatial and temporal distribution of droughts in the study area. Then the correlation coefficient between drought-affected area and detrended yield of winter wheat was quantified and the impact of droughts of different intensities on winter wheat yield during different growth stages was investigated. The results show that incipient drought during the wintering period has no significant impact on the yield of winter wheat, while moderate drought in the same period can reduce yield. Drought affects winter wheat yield significantly during the flowering and filling stages. Droughts of higher intensity have more significant negative effects on the yield of winter wheat. Monitoring of droughts and irrigation is critical during these periods to ensure normal yield of winter wheat. This study has important practical implications for the planning of irrigation and food security.

Influence of Climate Change on Winter Wheat Growth in North China During 1950-2000

The crop model World Food Studies (WOFOST) was tuned and validated withmeteorological as well as winter wheat growth and yield data at 24 stations in 5 provinces of NorthChina from 1997 to 2003. The parameterization obtained by the tuning was then used to model theimpacts of climate change on winter wheat growth for all stations using long-term weather data from1950 to 2000. Two simulations were made, one with all meteorological data (rainfed) and the otherwithout water stress (potential). The results indicate that the flowering and maturity datesoccurred 3.3 and 3 days earlier in the 1990s than that in the 1960s due to a 0.65℃ temperatureincrease. The simulated rainfed yields show that the average drought induced yields (potential minusrainfed yields) have decreased by 9.7% over the last 50 years. This is to be compared with a 0.02%decrease in yield if the precipitation limit is lifted. Although the precipitation during thegrowing season has decreased over the last 50 years, the drought effects on the rainfed yieldsremained to be practically unchanged as the spring precipitation did not decrease markedly.

华北平原冬小麦-夏玉米农田生态系统土壤自养和异养呼吸模型构建

土壤呼吸是陆地生态系统碳循环的重要过程,准确估算土壤呼吸对估算陆地生态系统碳源汇具有重要意义。通过在华北平原典型农田内开展土壤呼吸及其组分的原位观测实验,构建了适用于华北平原冬小麦-夏玉米轮种制农田生态系统的半经验半机理土壤异养呼吸和土壤自养呼吸模型。结果表明,冬小麦-夏玉米农田土壤异养呼吸模型可表达为土壤温度和土壤水分的函数,其中,土壤温度对土壤异养呼吸的影响适合用Arrhenius方程描述,而土壤水分的影响适合用对称的倒抛物线描述。验证表明,该模型的R^(2)和RMSE分别为0.68和0.52μmol m^(-2)s^(-1)。土壤自养呼吸模型包括维持呼吸和生长呼吸两个模块,其中,维持呼吸表达为土壤温度和叶面积指数的函数,其形式分别为Van′t Hoff指数方程和米氏方程;生长呼吸表达为总初级生产力与维持呼吸之差的线性函数。冬小麦季和夏玉米季土壤自养呼吸模型的结构相同,但是两种作物的模型参数差异较大。验证表明,冬小麦季土壤自养呼吸模型的R2和RMSE分别为0.64和0.50μmol m^(-2)s^(-1),夏玉米季土壤自养呼吸模型的R2和RMSE分别为0.67和0.37μmol m^(-2)s^(-1)。相比于不区分土壤异养呼吸和土壤自养呼吸的土壤总呼吸模型,本研究构建的土壤异养呼吸和土壤自养呼吸模型能够更加准确地模拟土壤呼吸的季节变化和年际变化过程,可为华北平原冬小麦-夏玉米轮种制农田生态系统的土壤呼吸估算提供方法依据。

Long-term(2013-2022)mapping of winter wheat in the North China Plain using Landsat data:classification with optimal zoning strategy

Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies.Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area.Although various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse time-series.Machine learning is an effective method for crop classification using Landsat data.Yet,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area.To address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps.Then,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification features.For each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data.Field sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different years.The derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial level.The results underscore the reliability of the produced annual winter wheat maps.Additional experiments demonstrate that our proposed optimal zoning method outper-forms other zoning methods,including Köppen climate zoning,wheat planting zoning and non-zoning methods,in enhancing wheat mapping accuracy.It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

华北平原冬小麦农田能量平衡特征及气象影响因素分析

农田生态系统能量平衡分量变化特征与水循环过程、水资源管理和农业可持续发展息息相关。本文基于安阳国家气候观象台2020-2021年的涡度相关系统和气象要素数据,分析了冬小麦农田不同生长阶段净辐射、潜热通量、显热通量和土壤热通量的日变化和月变化特征,明确了影响华北平原农田生态系统能量平衡分量的主要气象因素。结果表明:(1)冬小麦不同生育期能量平衡分量日变化趋势基本保持一致,呈单峰二次曲线变化。出苗期和越冬期白天潜热通量小于显热通量,而拔节期和乳熟期白天潜热通量明显大于显热通量。整个生长季土壤热通量均较小,其中越冬期的最小。(2)从10月到翌年5月,净辐射和潜热通量的月变化特征整体上呈先减少后增加的趋势,1月份的潜热通量最小,为0.39 MJ·m^(-2)·d^(-1),5月份的最大,为9.38 MJ·m^(-2)·d^(-1);显热通量的月变化与潜热的相反;整个生长季,土壤热通量12月的最小,5月的最大。随着冬小麦的生长,潜热通量占净辐射的比值呈先减少后增加的趋势,显热通量则相反,二者呈现此消彼长的现象。(3)小时尺度上,潜热通量、显热通量、土壤热通量与气温、饱和水汽压差和风速呈极显著正相关,与相对湿度和土壤含水量呈极显著负相关。日尺度上,除显热通量外,潜热通量、土壤热通量与气温、饱和水汽压差和土壤温度呈极显著正相关,与相对湿度的相关性不显著。

改进密闭室抽气法以准确定量旱地土壤氨挥发

密闭室抽气法被认为是定量土壤NH_(3)挥发较为可靠的方法,具有灵敏度高且成本低的优点。然而,大部分研究忽略了密闭室抽气法大气进气口NH_(3)浓度,可能会高估土壤NH_(3)挥发量,目前尚缺乏相关定量研究。本研究以华北冬小麦-夏玉米轮作为对象,对比分析了2019——2021年间冬小麦基肥(2次)、追肥(1次)、夏玉米四叶肥(2次)、夏玉米十叶肥(2次)共7次施肥后,改进密闭室抽气法前后测定农田土壤NH_(3)挥发的结果。改进前为包含大气背景NH_(3)的测定方法,为常规测定方法;改进后为扣除大气背景NH_(3)的测定方法,通过在田间加装3套采集距地面2.5 m高度大气的装置实现。结果表明:改进后的NH_(3)挥发通量(以N计)比改进前低0~0.23 kg·hm^(-2)·d^(-1)(0%~100%),平均值为0.06 kg·hm^(-2)·d^(-1)(33%),且37%的样本存在显著性差异(P<0.05,n=931)。改进后的NH_(3)挥发累积量比改进前低0.53~2.66 kg·hm^(2)(3%~53%),平均值为1.15 kg·hm^(-2)(29%),且47%的样本存在显著性差异(P<0.05,n=49)。改进前后NH_(3)挥发量(包括通量和累积量)存在显著差异的样本占比随着NH_(3)挥发量降低而升高。回归分析表明,改进前的NH_(3)挥发累积排放量可以用算式校正得到真实的NH_(3)累积排放量,校正式为y=0.94x-0.78(R2=0.99,P<0.01,n=49),其中y和x分别为校正后和校正前的NH_(3)挥发累积量,该算式可应用于华北土壤-作物体系NH_(3)挥发累积量的校正。综上,利用抽气法定量农田土壤NH_(3)挥发需要扣除大气背景NH_(3)以得到准确的测定结果,对于未扣除大气背景的旱地NH_(3)挥发累积量,可采用校正算式进行校正。

基于WHCNS模型的华北平原麦田灌溉制度优选研究

华北地区的粮食生产在全国范围内地位突出,但是农业用水严重短缺;该研究以华北平原冬小麦为研究对象,首先利用华北地区2004年和2006年试验数据分析传统灌溉制度下水分运移规律及利用效率,结合WHCNS模型进行不同组合灌溉情景(研究设置总灌水量变化范围由100 mm至450 mm,提高灌水次数至5次)的模拟研究,使用综合指数法对灌溉量进行优选研究。最终得出350 mm为设定组合情境中的最佳灌溉量。

Comparison of winter wheat yield sensitivity to climate variables under irrigated and rain-fed conditions

Crop simulation models provide alternative, less time-consuming, and cost-effective means of deter- mining the sensitivity of crop yield to climate change. In this study, two dynamic mechanistic models, CERES （Crop Environment Resource Synthesis） and APSIM （Agricultural Production Systems Simulator）, were used to simulate the yield of wheat （Triticum aestivum L.） under well irrigated （CFG） and rain-fed （YY） conditions in relation to different climate variables in the North China Plain （NCP）. The study tested winter wheat yield sensitivity to different levels of temperature, radiation, precipitation, and atmospheric carbon dioxide （COa） concentration under CFG and YY conditions at Luancheng Agro-ecosystem Experimental Stations in the NCR The results from the CERES and APSIM wheat crop models were largely consistent and suggested that changes in climate variables influenced wheat grain yield in the NCR There was also significant variation in the sensitivity of winter wheat yield to climate variables under different water （CFG and YY） conditions. While a temperature increase of 2℃ was the threshold beyond which temperature negatively influenced wheat yield under CFG, a temperature rise exceeding 1℃ decreased winter wheat grain yield under YY. A decrease in solar radiation decreased wheat grain yield under both CFG and YY conditions. Although the sensitivity of winter wheat yield to precipitation was small under the CFG, yield decreased significantly with decreasing precipitation under the rain- fed YY treatment. The results also suggest that wheat yield under CFG linearly increased by ≈ 3.5% per 60 ppm （parts per million） increase in CO2 concentration from 380 to560ppm, and yield under YY increased linearly by ≈ 7.0% for the same increase in CO2 concentration.

Establishment of Winter Wheat Regional Simulation Model Based on Remote Sensing Data and Its Application

Accurate crop growth monitoring and yield forecasting are significant to the food security and the sustainable development of agriculture. Crop yield estimation by remote sensing and crop growth simulation models have highly potential application in crop growth monitoring and yield forecasting. However, both of them have limitations in mechanism and regional application, respectively. Therefore, approach and methodology study on the combination of remote sensing data and crop growth simulation models are concerned by many researchers. In this paper, adjusted and regionalized WOFOST （World Food Study） in North China and Scattering by Arbitrarily Inclined Leaves-a model of leaf optical PROperties SPECTra （SAIL-PROSFPECT） were coupled through LAI to simulate Soil Adjusted Vegetation Index （SAVI） of crop canopy, by which crop model was re-initialized by minimizing differences between simulated and synthesized SAVI from remote sensing data using an optimization software （FSEOPT）. Thus, a regional remote-sensingcrop-simulation-framework-model （WSPFRS） was established under potential production level （optimal soil water condition）. The results were as follows： after re-initializing regional emergence date by using remote sensing data, anthesis, and maturity dates simulated by WSPFRS model were more close to measured values than simulated results of WOFOST; by re-initializing regional biomass weight at turn-green stage, the spatial distribution of simulated storage organ weight was more consistent with measured yields and the area with high values was nearly consistent with actual high yield area. This research is a basis for developing regional crop model in water stress production level based on remote sensing data.

干旱条件下APSIM模型修正及华北冬小麦产量模拟效果

干旱是影响华北地区冬小麦产量的主要农业气象灾害之一,作物生长模型是评估干旱对作物产量影响主要方法之一,但作物生长模型对极端天气气候条件下(如干旱)作物产量模拟效果仍存在不确定性。为提高作物模型在干旱条件下对作物产量模拟的精准性,该研究利用调参验证后的农业生产系统模型(agricultural production systems simulator,APSIM),通过查阅与华北地区冬小麦相关的186篇大田试验文献获得1 876对观测数据,以作物水分亏缺指数为干旱指标,评估APSIM模型在冬小麦拔节-开花和开花-成熟阶段干旱对产量影响的模拟效果,提出APSIM在拔节-开花和开花-成熟阶段干旱对小麦产量影响的修正系数。基于历史气候条件、SSP245和SSP585未来气候情景资料,分析了冬小麦拔节-开花和开花-成熟阶段干旱时空分布特征,并采用修正系数校正后的APSIM模型评估华北地区冬小麦拔节-开花和开花-成熟阶段不同等级干旱对其产量的影响。结果表明,APSIM模型低估了拔节-开花阶段干旱对冬小麦产量影响程度,轻旱、中旱和重旱校正系数分别为0.85、0.91和0.85;APSIM模型可准确模拟开花-成熟阶段轻旱和中旱对冬小麦产量影响,但高估了重旱对冬小麦产量影响,重旱校正系数为1.33。历史和未来气候情景下,拔节-开花和开花-成熟阶段干旱导致冬小麦减产率均呈由北到南依次递减的空间分布特征,且开花-成熟阶段干旱对冬小麦负面影响高于拔节-开花阶段。未来气候情景下冬小麦拔节-开花和开花-成熟阶段不同等级干旱导致的冬小麦减产率均低于历史气候条件。未来干旱对华北冬小麦产量的负面影响程度有所缓解。研究为有效评估干旱对冬小麦影响提供方法支撑。