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.

Estimating the Drought-Induced Yield Loss for Winter Wheat in a Semi-Arid Region of the Southern United States Using a Drought Index

The economy of most rural locations in the semi-arid region of Llano Estacado in the southern United States is predominantly based on agriculture, primarily beef and wheat (Triticum aestivum L.) production. This region is prone to drought and is projected to experience a drier climate. Droughts that coincide with the critical phenological phases of a crop can be remarkably costly. Although drought cannot be prevented, its losses can be minimized through mitigation measures if it is predicted in advance. Predicting yield loss from an imminent drought is an important need of stakeholders. One way to fulfill this need is using an agricultural drought index, such as the Agricultural Reference Index for Drought (ARID). Being plant physiology-based, ARID can represent drought-yield relationships accurately. This study developed an ARID-based yield model for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to water stress. The reasonable values of the drought sensitivity coefficients of the yield model indicated that it could reflect the phenomenon of water stress decreasing the winter wheat yields in this region reasonably. The values of the various metrics used to evaluate the model, including Willmott Index (0.86), Nash-Sutcliffe Index (0.61), and percentage error (26), indicated that the yield model performed fairly well at predicting the drought-induced yield loss for winter wheat. The yield model may be useful for predicting the drought-induced yield loss for winter wheat in the study region and scheduling irrigation allocation based on phenological phase-specific drought sensitivity.

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.

Effects of Long-term Located Fertilization on Wheat Yield and Soil Nutrients of Three Types of Soils in Shandong Province

[Objective] This study aimed to investigate the effects of long-term differ- ent fertilization in three types of soils on wheat yield and soil nutrient variation in Shandong Province. [Method] A 30-year located experiment in Jinan of Shandong Province was selected and the results of soil nutrient and crop yield in 1984, 1987, 1988, 1989, 1990, 2001, 2005, 2006, 2007 and 2010 were measured and collected. In this study, five treatments： CK, NP, NK, PK and NPK of the located experiment were selected. [Result] The three types of soils in wheat yields decreased signifi- cantly in the first several years and in 2006. Wheat yields of the treatments with P fertilizers were obviously higher than those without P fertilizers; it was shown that phosphorus is the primary nutritional factor for high-yielding of wheat. The highest yield is from cinnamon soil, followed by that from brown soil, and the lowest pro- duction is from fluvo-aquic soil. Under the same fertilization, the influence of other factors on wheat yield of brown soil is the smallest, while cinnamon soil is vulnera- ble to the influence of external conditions, resulting in larger fluctuation of annual wheat yield. The alkali-hydro nitrogen contents of three kinds of soils first de- creased, then raised, and at last reduced apparently. Since 2007, the change of al- kali-hydro nitrogen content appeared rebounded. The available P contents of no- phosphorus treatments decreased over time while those of the treatments with P fertilizers increased at first, then decreased, and after that kept relatively stable. The available K contents of no K treatments decreased slowly. The treatments of PK and NK had higher available K content than NPK treatment. [Conclusion] Thus, it is an effective fertilization measure to improve the wheat yield by supplying reasonable phosphate fertilizer and nitrogen fertilizer and making up potassium fertilizer.

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.

Wheat Grain Yield and Yield Stability in a Long-Term Fertilization Experiment on the Loess Plateau

To provide a scientific basis for sustainable land management, a 20-year fertility experiment was conducted in Changwu County, Shaanxi Province, China to investigate the effects of long-term application of chemical fertilizers on wheat grain yield and yield stability on the Loess Plateau using regression and stability analysis. The experiment consisted of 17 fertilizer treatments, containing the combinations of different N and P levels, with three replications arranged in a randomized complete block design. Nitrogen fertilizer was applied as urea, and P was applied as calcium superphosphate. Fertilizer rates had a large effect on the response of wheat yield to fertilization. Phosphorus, combined with N, increased yield significantly （P 〈 0.01）. In the unfertilized control and the N or P sole application treatments, wheat yield had a declining trend although it was not statistically significant. Stability analysis combined with the trend analysis indicated that integrated use of fertilizer N and P was better than their sole application in increasing and sustaining the productivity of rainfed winter wheat.

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.

Effect of Fertilization on Soil Fertility and Wheat Yield of Dryland in the Loess Plateau

Long-term fertility experiments have become an important tool for investigating the sustainability of cropping systems. Therefore, a long-term (18-year) fertilization experiment was conducted in Changwu County, Shaanxi Province, China, to ascertain the effect of the long-term application of chemical fertilizers and manure on wheat yield and soil fertility in the Loess Plateau, so as to provide a scientific basis for sustainable land management. The experiment consisted of nine fertilizer treatments with thr…

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.

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.

The impacts of climate change on wheat yield in the Huang-Huai-Hai Plain of China using DSSAT-CERES-Wheat model under different climate scenarios

Climate change has been documented as a major threat to current agricultural strategies.Progress in understanding the impact of climate change on crop yield is essential for agricultural climate adaptation,especially for the Huang-Huai-Hai Plain(3H Plain)of China which is an area known to be vulnerable to global warming.In this study,the impacts of climate change on winter wheat(Triticum aestivum L.)yield between the baseline period(1981–2010)and two Representative Concentration Pathways(RCP8.5 and RCP4.5)were simulated for the short-term(2010–2039),the medium-term(2040–2069)and the long-term(2070–2099)in the 3H Plain,by considering the relative contributions of changes in temperature,solar radiation and precipitation using the DSSAT-CERES-Wheat model.Results indicated that the maximum and minimum temperatures(TMAX and TMIN),solar radiation(SRAD),and precipitation(PREP)during the winter wheat season increased under these two RCPs.Yield analysis found that wheat yield increased with the increase in SRAD,PREP and CO2 concentration,but decreased with an increase in temperature.Increasing precipitation contributes the most to the total impact,increasing wheat yield by 9.53,6.62 and 23.73%for the three terms of future climate under RCP4.5 scenario,and 11.74,16.38 and 27.78%for the three terms of future climate under RCP8.5 scenario.However,as increases in temperature bring higher evapotranspiration,which further aggravated water deficits,the supposed negative effect of increasing thermal resources decreased wheat yield by 1.92,4.08 and 5.24%for the three terms of future climate under RCP4.5 scenario,and 3.64,5.87 and 5.81%for the three terms of future climate under RCP8.5 scenario with clearly larger decreases in RCP8.5.Counterintuitively,the impacts in southern sub-regions were positive,but they were all negative in the remaining sub-regions.Our analysis demonstrated that in the 3H Plain,which is a part of the mid-high latitude region,the effects of increasing thermal resources were counteracted by the aggravated water deficits caused by the increase in temperature.

Optimization of sowing date and seeding rate for high winter wheat yield based on pre-winter plant development and soil water usage in the Loess Plateau,China

Sowing date and seeding rate are critical for productivity of winter wheat(Triticum aestivum L.).A three-year field experiment was conducted with three sowing dates(20 September(SD1),1 October(SD2),and 10 October(SD3)) and three seeding rates(SR67.5,SR90,and SR112.5) to determine suitable sowing date and seeding rate for high wheat yield.A large seasonal variation in accumulated temperature from sowing to winter dormancy was observed among three growing seasons.Suitable sowing dates for strong seedlings before winter varied with the seasons,that was SD2 in 2012–2013,SD3 in 2013–2014,and SD2 as well as SD1 in 2014–2015.Seasonal variation in precipitation during summer fallow also had substantial effects on soil water storage,and consequently influenced grain yield through soil water consumption from winter dormancy to maturity stages.Lower consumption of soil water from winter dormancy to booting stages could make more water available for productive growth from anthesis to maturity stages,leading to higher grain yield.SD2 combined with SR90 had the lowest soil water consumption from winter dormancy to booting stages in 2012–2013 and 2014–2015; while in 2013–2014,it was close to that with SR67.5 or SR112.5.For productive growth from anthesis to maturity stages,SD2 with SR90 had the highest soil water consumption in all three seasons.The highest water consumption in the productive growth period resulted in the best grain yield in both low and high rainfall years.Ear number largely contributed to the seasonal variation in grain yield,while grain number per ear and 1 000-grain weight also contributed to grain yield,especially when soil water storage was high.Our results indicate that sowing date and seeding rate affect grain yield through seedling development before winter and also affect soil water consumption in different growth periods.By selecting the suitable sowing date(1 October) in combination with the proper seeding rate of 90 kg ha–1,the best yield was achieved.Based on these results,we recommend that the current sowing date be delayed from 22 or 23 September to 1 October.

Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.

Contribution of ear photosynthesis to grain yield under rainfed and irrigation conditions for winter wheat cultivars released in the past 30 years in North China Plain

To understand the contribution of ear photosynthesis to grain yield and its response to water supply in the improvement of winter wheat, 15 cultivars released from 1980 to 2012 in North China Plain（NCP） were planted under rainfed and irrigated conditions from 2011 to 2013, and the ear photosynthesis was tested by ear shading. During the past 30 years, grain yield significantly increased, the flag leaf area slightly increased under irrigated condition but decreased significantly under rainfed condition, the ratio of grain weight：leaf area significantly increased, and the contribution of ear photosynthesis to grain yield changed from 33.6 to 64.5% and from 32.2 to 57.2% under rainfed and irrigated conditions, respectively. Grain yield, yield components, and ratio of grain weight：leaf area were positively related with contribution of ear photosynthesis. The increase in grain yield in winter wheat was related with improvement in ear photosynthesis contribution in NCP, especially under rainfed condition.

Grain yield and water use of winter wheat as affected by water and sulfur supply in the North China Plain

Water shortage has threatened sustainable development of agriculture globally as well as in the North China Plain（NCP）.Irrigation,as the most effective way to increase food production in dry land,may not be readily available in the situation of drought.One of the alternatives is to supply plants with enough nutrients so that they can be more sustainable to the water stress.The objective of this study was to explore effects of irrigation and sulphur（S）application on water consumption,dry matter accumulation（DMA）,and grain yield of winter wheat in NCP.Three irrigation regimes including no irrigation（rainfed,I0）during the whole growth period,once irrigation only at jointing stage（90 mm,I1）,and twice respective irrigation at jointing and anthesis stages（90 mm plus 90 mm,I2）,and two levels of S application including 0S0and 60 kg ha^–1（S60）were designed in the field experiment in NCP.Results showed that increasing irrigation times significantly increased mean grain yield of wheat by 12.5–23.7%and nitrogen partial factor productivity（NPFP）by 21.2–45.0%in two wheat seasons,but markedly decreased crop water use efficiency（YWUE）.Furthermore,S supply 60 kg ha^–1 significantly increased mean grain yield,YWUE,IWUE and NPFP by 5.6,6.1,23.2,and 5.6%（across two wheat seasons）,respectively.However,we also found that role of soil moisture prior to S application was one of important greater factors on improving the absorption and utilization of storage water and nutrients of soil.Thus,water supply is still the most important factor to restrict the growth of wheat in the present case of NCP,supplying 60 kg ha^–1 S with once irrigation 90 mm at the jointing stage is a relatively appropriate recommended combination to improve grain yield and WUE of wheat when saving water resources is be considered in irrigated wheat farmlands of NCP.

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.

Long-term inorganic plus organic fertilization increases yield and yield stability of winter wheat

An understanding of wheat yield and yield stability response to fertilization is important for sustainable wheat production. A 36-year long-term fertilization experiment was employed to evaluate the yield and yield stability of winter wheat. Five fertilization regimes were compared,including(1) CK, no fertilizer;(2) NPK, inorganic fertilizer only;(3) O, organic fertilizer only;(4)NPKO, 50% of NPK plus 50% of O, and(5) HNPKO, 80% of NPK plus 80% of O. The greatest yield increase was recorded in HNPKO, followed by NPKO, with O producing the lowest mean yield increase. Over the 36 years, the rate of wheat yield increase in fertilized plots ranged from95.31 kg ha-1 year-1 in the HNPKO to 138.65 kg ha-1 year-1 in the O. Yield stability analysis using the additive main effects and multiplicative interactions(AMMI) method assigned 62.3%, 26.3%,and 11.4% of sums of squares to fertilization effect, environmental effect, and fertilization ×environment interaction effect, respectively. The combination of inorganic and organic fertilization(NPKO and HNPKO) appeared to produce more stable yields than O or NPK, with lower coefficients of variation and AMMI stability value. However, wheat grown with O seemed to be the most susceptible to climate change and the least productive among the fertilized plots.Significant correlations of grain yield with soil properties and with mean air temperature were observed. These findings suggest that inorganic + organic fertilizer can increase wheat yield and its stability by improvement in soil fertility and reduction in variability to climate change.

Differences in Chlorophyll Fluorescence Parameters, Yield and Its Components Between Different Genotypes of Wheat Under Waterlogging Conditions at Anthesis

Waterlogging is one of the most factors limiting wheat production in the middle and lower reaches of the Yangtze River Plain of China,especially in the middle and late stages of wheat.Wheat varieties‘Jingmai102’(JM102)and‘Yangmai158’(YM158)were planted to study the dynamic changes of photosynthetic characteristics in flag leaf and the influence of waterlogging at anthesis on the yield and components and dry matter accumulation and remobilization of winter wheat in above ground.The results showed that the SPAD values slightly increased at 1 day after anthesis(d),and then kept decreasing with the increase of waterlogging time.The decrease in SPAD value was more remarkably in YM158 than that in JM102.As for the chlorophyll fluorescence parameters,the photochemical efficiency(Fv/Fm),potential activity(Fv/Fo)of photosystem II,and electronic transmission(Fm/Fo)on photosystem II increased first and then decreased with the increase of waterlogging days after anthesis.The quantum ratio of heat dissipation(Fo/Fm)had a tendency opposite to that of Fv/Fm,and the change range of JM102 was lower than that of YM158.For the grain yield and components,waterlogging at anthesis decreased the dry weight of single stem,grain yield,1 000-kernel weight,spikelet per panicle,and harvest index,and the reduction of JM102 was smaller than that of YM158.As for the accumulation and remobilization of dry matter,the accumulation of dry matter after anthesis decreased significantly under waterlogging condition(WL),and the reduction of JM102 was smaller than that of YM158.In conclusion,waterlogging at anthesis significantly affected the photosynthetic characteristics,yield and components in both varieties,but different varieties exhibited different tolerances to waterlogging stress and YM158 was more sensitive to water stress than JM102.

Breaking wheat yield barriers requires integrated efforts in developing countries

Most yield progress obtained through the so called ＂Green Revolution＂, particularly in the irrigated areas of Asia, has reached a limit, and major resistance genes are quickly overcome by the appearance of new strains of disease causing organisms.New plant stresses due to a changing environment are difficult to breed for as quickly as the changes occur.There is consequently a continual need for new research programs and breeding strategies aimed at improving yield potential, abiotic stress tolerance and resistance to new, major pests and diseases.Recent advances in plant breeding encompass novel methods of expanding genetic variability and selecting for recombinants, including the development of synthetic hexaploid, hybrid and transgenic wheats.In addition, the use of molecular approaches such as quantitative trait locus（QTL） and association mapping may increase the possibility of directly selecting positive chromosomal regions linked with natural variation for grain yield and stress resistance.The present article reviews the potential contribution of these new approaches and tools to the improvement of wheat yield in farmer＇s fields, with a special emphasis on the Asian countries, which are major wheat producers, and contain the highest concentration of resource-poor wheat farmers.