Statistical models using historical data on crop yields and weather to calibrate rela- tively simple regression equations have been widely and extensively applied in previous studies, and have provided a common altern...Statistical models using historical data on crop yields and weather to calibrate rela- tively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale （e.g. global scale, national scale, provincial scale and county scale）. Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.展开更多
Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and cli...Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model （GCM） downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change （at 700 ppm CO2 eq correct） appears within ±10% for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.展开更多
The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of ...The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of N, P2O5, K2O in cotton leaves, stems, squares and bolls decreased obviously with the time over the whole growth duration and the falling extent was greater in high-yield cotton than in CK. Contents of N in leaves, squares and bolls, in particular in the leaves of fruit-bearing shoot was higher in high-yield cotton than in CK. Contents of P2O5 in squares and bolls and that of K2O in stems were higher in high-yield cotton than in CK during the whole growing period. The accumulations of N, P2O5 and K2O in the cotton plants could be described with a logistic curve equation. There was the fastest nutrient uptake at about 90 d for N, 92 d for P2O5 and 85 d for K2O after emergence, respectively. Total nutrient accumulation of N, P2O5 and K2O was 385.8, 244. 7 and 340.3 kg ha-1, respectively. Approximately 12. 5 kg N, 8. 0 kg P2O5 and 11.1 kg K2O were needed for producing 100 kg lint with the leaves and stems under the super high yield condition of 3 000 kg ha-1 in Xinjiang.展开更多
Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far o...Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.展开更多
The Tarim River Basin(TRB)is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China.In the context of climate change,however,the increased frequency of ex...The Tarim River Basin(TRB)is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China.In the context of climate change,however,the increased frequency of extreme weather and climate events is having numerous negative impacts on the region's agricultural production.To better understand how unfavorable climatic conditions affect crop production,we explored the relationship of extreme weather and climate events with crop yields and phenology.In this research,ten indicators of extreme weather and climate events(consecutive dry days(CDD),min Tmax(TXn),max Tmin(TNx),tropical nights(TR),warm days(Tx90p),warm nights(Tn90p),summer days(SU),frost days(FD),very wet days(R95p),and windy days(WD))were selected to analyze the impact of spatial and temporal variations on the yields of major crops(wheat,maize,and cotton)in the TRB from 1990 to 2020.The three key findings of this research were as follows:extreme temperatures in southwestern TRB showed an increasing trend,with higher extreme temperatures at night,while the occurrence of extreme weather and climate events in northeastern TRB was relatively low.The number of FD was on the rise,while WD also increased in recent years.Crop yields were higher in the northeast compared with the southwest,and wheat,maize,and cotton yields generally showed an increasing trend despite an earlier decline.The correlation of extreme weather and climate events on crop yields can be categorized as extreme nighttime temperature indices(TNx,Tn90p,TR,and FD),extreme daytime temperature indices(TXn,Tx90p,and SU),extreme precipitation indices(CDD and R95p),and extreme wind(WD).By using Random Forest(RF)approach to determine the effects of different extreme weather and climate events on the yields of different crops,we found that the importance of extreme precipitation indices(CDD and R95p)to crop yield decreased significantly over time.As well,we found that the importance of the extreme nighttime temperature(TR and TNx)for the yields of the three crops increased during 2005-2020 compared with 1990-2005.The impact of extreme temperature events on wheat,maize,and cotton yields in the TRB is becoming increasingly significant,and this finding can inform policy decisions and agronomic innovations to better cope with current and future climate warming.展开更多
A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overest...A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.展开更多
According to the biomechanic theory and method, the dynamic mechanism of crop growth under the external force action of multi_environment factors (light, temperature,soil and nutrients etc.) was comprehensively explor...According to the biomechanic theory and method, the dynamic mechanism of crop growth under the external force action of multi_environment factors (light, temperature,soil and nutrients etc.) was comprehensively explored.Continuous_time Markov(CTM) approach was adopted to build the dynamic model of the crop growth system and the simulated numerical method. The growth rate responses to the variation of the external force and the change of biomass saturation value were studied. The crop grew in the semiarid area was taken as an example to carry out the numerical simulation analysis, therefore the results provide the quantity basis for the field management. Comparing the dynamic model with the other plant growth model, the superiority of the former is that it displays multi_dimension of resource utilization by means of combining macroscopic with microcosmic and reveals the process of resource transition. The simulation method of crop growth system is advanced and manipulated. A real simulation result is well identical with field observational results.展开更多
Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the...Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the two can improve the real-time mechanism and accuracy of agricultural monitoring and evaluation. The research is based on the MERSI data carried by China’s new generation Fengyun-3 meteorological satellite, combined with the US ALMANAC crop model, established the NDVI-LAI model and realized the acquisition of LAI data from point to surface. Because of the principle of the relationship between the morphological changes of LAI curve and the growth of crops, an index that can be used to determine the growth of crops is established to realize real-time, dynamic and wide-scale monitoring of winter wheat growth. At the same time, the index was used to select the different key growth stages of winter wheat for yield estimation. The results showed that the relative error of total yield during the filling period was low, nearly 5%. The research results show that the combination of domestic meteorological satellite Fengyun-3 and ALMANAC crop model for crop growth monitoring and yield estimation is feasible, and further expands the application range of domestic satellites.展开更多
<div style="text-align:justify;"> <span style="font-family:Verdana;"></span>Process-based crop simulation models are useful for simulating the impacts of climate change on crop yi...<div style="text-align:justify;"> <span style="font-family:Verdana;"></span>Process-based crop simulation models are useful for simulating the impacts of climate change on crop yields. Currently, estimation of spatially calibrated soil parameters for crop models can be challenging, as it requires the availability of long-term and detailed input data from several sentinel sites. The use of aggregated regional data for model calibrations has been proposed but not been employed in regional climate change studies. The study: 1) employed the use of county-level data to estimate spatial soil parameters for the calibration of CROPGRO-Soybean model and 2) used the calibrated model, assimilated with future climate data, in assessing the impacts of climate change on soybean yields. The CROPGRO-Soybean model was calibrated using major agricultural soil types, crop yield and current climate data at county level, for selected counties in Alabama for the period 1981-2010. The calibrated model simulations were acceptable with performance indicators showing Root Mean Square Error percent of between 27 - 43 and Index of Agreement ranging from 0.51 to 0.76. Projected soybean yield decreased by an average of 29% and 23% in 2045, and 19% and 43% in 2075, under Representative Concentration Pathways 4.5 and 8.5, respectively. Results showed that late-maturing soybean cultivars were most resilient to heat, while late-maturing cultivators needed optimized irrigation to maintain appropriate soil moisture to sustain soybean yields. The CROPGRO-Soybean phenological and yield simulations suggested that the negative effects of increasing temperatures could be counterbalanced by increasing rainfall, optimized irrigation, and cultivating late-maturing soybean cultivars. </div>展开更多
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.展开更多
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） v...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.展开更多
Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental...Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.展开更多
The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictiona...The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.展开更多
This study estimates of the impact of climate change on yields for the four most commonly grown crops (millet, maize, sorghum and cassava) in Sub-Saharan Africa (SSA). A panel data approach is used to relate yields to...This study estimates of the impact of climate change on yields for the four most commonly grown crops (millet, maize, sorghum and cassava) in Sub-Saharan Africa (SSA). A panel data approach is used to relate yields to standard weather variables, such as temperature and precipitation, and sophisticated weather measures, such as evapotranspiration and the standardized precipitation index (SPI). The model is estimated using data for the period 1961-2002 for 37 countries. Crop yields through 2100 are predicted by combining estimates from the panel analysis with climate change predictions from general circulation models (GCMs). Each GCM is simulated under a range of greenhouse gas emissions (GHG) assumptions. Relative to a case without climate change, yield changes in 2100 are near zero for cassava and range from –19% to +6% for maize, from –38% to –13% for millet and from –47% to –7% for sorghum under alternative climate change scenarios.展开更多
With a continuously increasing population and better food consumption levels, im- proving the efficiency of arable land use and increasing its productivity have become funda- mental strategies to meet the growing food...With a continuously increasing population and better food consumption levels, im- proving the efficiency of arable land use and increasing its productivity have become funda- mental strategies to meet the growing food security needs in China. A spatial distribution map of medium- and low-yield cropland is necessary to implement plans for cropland improvement In this study, we developed a new method to identify high-, medium-, and low-yield cropland from Moderate Resolution Imaging Spectroradiometer （MODIS） data at a spatial resolution of 500 m. The method could be used to reflect the regional heterogeneity of cropland productiv- ity because the classification standard was based on the regionalization of cropping systems in China. The results showed that the proportion of high-, medium-, and low-yield cropland in China was 21%, 39%, and 40%, respectively. About 75% of the low-yield cropland was lo- cated in hilly and mountainous areas, and about 53% of the high-yield cropland was located in plain areas. The five provinces with the largest area of high-yield cropland were all located in the Huang-Huai-Hai region, and the area amounted to 42% of the national high-yield cropland area. Meanwhile, the proportion of high-yield cropland was lower than 15% in Hei- Iongjiang, Sichuan, and Inner Mongolia, which had the largest area allocated to cropland in China. If all the medium-yield cropland could be improved to the productive level of high-yield cropland and the low-yield cropland could be improved to the level of medium-yield cropland, the total productivity of the land would increase 19% and 24%, respectively.展开更多
The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated con...The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated conditions in semi arid environment, Sargodha, Punjab. The model was evaluated with observed data collected in trials which were conducted during spring season in 2010 and 2011 in Sargodha, Punjab, Pakistan. Split plot design was used in layout of experiment with three replications. The hybrids (Hysun-33 & S-278) and N levels (0, 75, 150 and 225 kg.ha-1) were allotted in main and sub plots, respectively. The OILCROP-SUN model showed that the model was able to simulate growth and yield of sunflower with an average of 10.44 error% between observed and simulated achene yield (AY). The results of simulation analysis indicated that nitrogen rate of 150 kg.N.ha-1 (N3) produced the highest yield as compared to other treatments. Furthermore, the economic analysis through mean Gini Dominance also showed the dominance of this treatment compared to other treatment combinations. Thus management strategy consisting?of treatment 150 kg.N.ha-1 was the best for high yield of sunflower hybrids.展开更多
Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitr...Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitrogen levels. The effect of nitrogen (N) on growth and yield components of different sunflower (Helianthus annuus L.) hybrids were evaluated under agro-climatic conditions of Sargodha, Pakistan during spring 2013. The experiment was laid out in a randomized complete block design with split plot arrangement having three replications, keeping cultivars in the main plots and nitrogen levels (0, 45, 90,135 and 180 kg/ha) in sub plots. OIL CROP-SUN Model showed that the model was able to simulate the growth and yield of sunflower with an average of 10.44 error% between observed and simulate achene yield (AY). The result of simulation indicates that nitrogen rate of 180 kg/ha produced highest achene yield in S-78 hybrid as compared to other treatments and Hysun-33 cultivar.展开更多
Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors,of which the most important one is the increasing carbon dioxide(CO_(2))concentrations.Estimates ...Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors,of which the most important one is the increasing carbon dioxide(CO_(2))concentrations.Estimates of CO_(2) fertilization effect(CFE)on rice,however,still had large uncertainties.Therefore,using the rice planting areas in East China as the study area,we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models,and found that the CFE predicted by these models had significant differences.We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province.Using CFE measurements from a field experiment as benchmark,we have developed an experiment–model integration approach aiming to reduce this variation.This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty,which is beneficial for the accurate prediction of future global rice yield in the context of climate change.展开更多
基金National Natural Science Foundation of China, No.41001057 The Science and Technology Strategic Pilot of the Chinese Academy of Sciences, No.XDA05090308+1 种基金 No.XDA05090310 Project Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology, No.2011-KF-06
文摘Statistical models using historical data on crop yields and weather to calibrate rela- tively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale （e.g. global scale, national scale, provincial scale and county scale）. Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.
基金funded by the National 973 Program of China (2012CB955904)the National Natural Science Foundation of China (31171452)the Sustainable Agriculture Innovation Network initiated and funded by Defra UK and Minstry of Agriculture of China (H5105000)
文摘Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model （GCM） downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change （at 700 ppm CO2 eq correct） appears within ±10% for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.
基金supported by the National Key Technologies R&D Program in 10th Five-year Plan of China(2001BA507A)the National Natural Sicence Foundation of China(39760040).
文摘The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of N, P2O5, K2O in cotton leaves, stems, squares and bolls decreased obviously with the time over the whole growth duration and the falling extent was greater in high-yield cotton than in CK. Contents of N in leaves, squares and bolls, in particular in the leaves of fruit-bearing shoot was higher in high-yield cotton than in CK. Contents of P2O5 in squares and bolls and that of K2O in stems were higher in high-yield cotton than in CK during the whole growing period. The accumulations of N, P2O5 and K2O in the cotton plants could be described with a logistic curve equation. There was the fastest nutrient uptake at about 90 d for N, 92 d for P2O5 and 85 d for K2O after emergence, respectively. Total nutrient accumulation of N, P2O5 and K2O was 385.8, 244. 7 and 340.3 kg ha-1, respectively. Approximately 12. 5 kg N, 8. 0 kg P2O5 and 11.1 kg K2O were needed for producing 100 kg lint with the leaves and stems under the super high yield condition of 3 000 kg ha-1 in Xinjiang.
基金co-supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2021B0301030007)the National Key Research and Development Program of China (Grant Nos. 2017YFA0604302 and 2017YFA0604804)+1 种基金the National Natural Science Foundation of China (Grant No. 41875137)the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)。
文摘Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.
基金co-supported by the Guangdong Major Project of Basic and Applied Basic Research [grant number 2021B0301030007]the National Key Research and Development Program of China [grant number 2017YFA0604302]+1 种基金the National Natural Science Foundation of China [grant number 41875137]the National Key Scientific and Technological Infrastructure project"Earth System Science Numerical Simulator Facility"(EarthLab)
基金funded by the Tianshan Yingcai Program of the Xinjiang Uygur Autonomous Region(2022TSYCCX0038)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Y2022108)the Postdoctoral Fellowship Program of Chinese Postdoctoral Science Foundation(CPSF)(GZC20232962).
文摘The Tarim River Basin(TRB)is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China.In the context of climate change,however,the increased frequency of extreme weather and climate events is having numerous negative impacts on the region's agricultural production.To better understand how unfavorable climatic conditions affect crop production,we explored the relationship of extreme weather and climate events with crop yields and phenology.In this research,ten indicators of extreme weather and climate events(consecutive dry days(CDD),min Tmax(TXn),max Tmin(TNx),tropical nights(TR),warm days(Tx90p),warm nights(Tn90p),summer days(SU),frost days(FD),very wet days(R95p),and windy days(WD))were selected to analyze the impact of spatial and temporal variations on the yields of major crops(wheat,maize,and cotton)in the TRB from 1990 to 2020.The three key findings of this research were as follows:extreme temperatures in southwestern TRB showed an increasing trend,with higher extreme temperatures at night,while the occurrence of extreme weather and climate events in northeastern TRB was relatively low.The number of FD was on the rise,while WD also increased in recent years.Crop yields were higher in the northeast compared with the southwest,and wheat,maize,and cotton yields generally showed an increasing trend despite an earlier decline.The correlation of extreme weather and climate events on crop yields can be categorized as extreme nighttime temperature indices(TNx,Tn90p,TR,and FD),extreme daytime temperature indices(TXn,Tx90p,and SU),extreme precipitation indices(CDD and R95p),and extreme wind(WD).By using Random Forest(RF)approach to determine the effects of different extreme weather and climate events on the yields of different crops,we found that the importance of extreme precipitation indices(CDD and R95p)to crop yield decreased significantly over time.As well,we found that the importance of the extreme nighttime temperature(TR and TNx)for the yields of the three crops increased during 2005-2020 compared with 1990-2005.The impact of extreme temperature events on wheat,maize,and cotton yields in the TRB is becoming increasingly significant,and this finding can inform policy decisions and agronomic innovations to better cope with current and future climate warming.
基金supported by the National Natural Science Foundation of China(Grant Nos.41775156 and 41590875)
文摘A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.
文摘According to the biomechanic theory and method, the dynamic mechanism of crop growth under the external force action of multi_environment factors (light, temperature,soil and nutrients etc.) was comprehensively explored.Continuous_time Markov(CTM) approach was adopted to build the dynamic model of the crop growth system and the simulated numerical method. The growth rate responses to the variation of the external force and the change of biomass saturation value were studied. The crop grew in the semiarid area was taken as an example to carry out the numerical simulation analysis, therefore the results provide the quantity basis for the field management. Comparing the dynamic model with the other plant growth model, the superiority of the former is that it displays multi_dimension of resource utilization by means of combining macroscopic with microcosmic and reveals the process of resource transition. The simulation method of crop growth system is advanced and manipulated. A real simulation result is well identical with field observational results.
文摘Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the two can improve the real-time mechanism and accuracy of agricultural monitoring and evaluation. The research is based on the MERSI data carried by China’s new generation Fengyun-3 meteorological satellite, combined with the US ALMANAC crop model, established the NDVI-LAI model and realized the acquisition of LAI data from point to surface. Because of the principle of the relationship between the morphological changes of LAI curve and the growth of crops, an index that can be used to determine the growth of crops is established to realize real-time, dynamic and wide-scale monitoring of winter wheat growth. At the same time, the index was used to select the different key growth stages of winter wheat for yield estimation. The results showed that the relative error of total yield during the filling period was low, nearly 5%. The research results show that the combination of domestic meteorological satellite Fengyun-3 and ALMANAC crop model for crop growth monitoring and yield estimation is feasible, and further expands the application range of domestic satellites.
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;"></span>Process-based crop simulation models are useful for simulating the impacts of climate change on crop yields. Currently, estimation of spatially calibrated soil parameters for crop models can be challenging, as it requires the availability of long-term and detailed input data from several sentinel sites. The use of aggregated regional data for model calibrations has been proposed but not been employed in regional climate change studies. The study: 1) employed the use of county-level data to estimate spatial soil parameters for the calibration of CROPGRO-Soybean model and 2) used the calibrated model, assimilated with future climate data, in assessing the impacts of climate change on soybean yields. The CROPGRO-Soybean model was calibrated using major agricultural soil types, crop yield and current climate data at county level, for selected counties in Alabama for the period 1981-2010. The calibrated model simulations were acceptable with performance indicators showing Root Mean Square Error percent of between 27 - 43 and Index of Agreement ranging from 0.51 to 0.76. Projected soybean yield decreased by an average of 29% and 23% in 2045, and 19% and 43% in 2075, under Representative Concentration Pathways 4.5 and 8.5, respectively. Results showed that late-maturing soybean cultivars were most resilient to heat, while late-maturing cultivators needed optimized irrigation to maintain appropriate soil moisture to sustain soybean yields. The CROPGRO-Soybean phenological and yield simulations suggested that the negative effects of increasing temperatures could be counterbalanced by increasing rainfall, optimized irrigation, and cultivating late-maturing soybean cultivars. </div>
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
基金supported by the National Natural Science Foundation of China (41401491,41371396,41301457,41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2016-X38)+1 种基金the Agricultural Scientific Research Fund of Outstanding Talentsthe Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘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.
基金This work is supported by New Zealand Ministry of Foreign Affairs and Trade PhD Scholarship and the University of Auckland’s Postgraduate Research Student SupportMinistry of Foreign Affairs and Trade,New Zealand,University of Auckland.
文摘Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.
文摘The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.
文摘This study estimates of the impact of climate change on yields for the four most commonly grown crops (millet, maize, sorghum and cassava) in Sub-Saharan Africa (SSA). A panel data approach is used to relate yields to standard weather variables, such as temperature and precipitation, and sophisticated weather measures, such as evapotranspiration and the standardized precipitation index (SPI). The model is estimated using data for the period 1961-2002 for 37 countries. Crop yields through 2100 are predicted by combining estimates from the panel analysis with climate change predictions from general circulation models (GCMs). Each GCM is simulated under a range of greenhouse gas emissions (GHG) assumptions. Relative to a case without climate change, yield changes in 2100 are near zero for cassava and range from –19% to +6% for maize, from –38% to –13% for millet and from –47% to –7% for sorghum under alternative climate change scenarios.
基金STS Project of CAS,No.KFJ-EW-STS-001National Natural Science Foundation of China,No.41430861
文摘With a continuously increasing population and better food consumption levels, im- proving the efficiency of arable land use and increasing its productivity have become funda- mental strategies to meet the growing food security needs in China. A spatial distribution map of medium- and low-yield cropland is necessary to implement plans for cropland improvement In this study, we developed a new method to identify high-, medium-, and low-yield cropland from Moderate Resolution Imaging Spectroradiometer （MODIS） data at a spatial resolution of 500 m. The method could be used to reflect the regional heterogeneity of cropland productiv- ity because the classification standard was based on the regionalization of cropping systems in China. The results showed that the proportion of high-, medium-, and low-yield cropland in China was 21%, 39%, and 40%, respectively. About 75% of the low-yield cropland was lo- cated in hilly and mountainous areas, and about 53% of the high-yield cropland was located in plain areas. The five provinces with the largest area of high-yield cropland were all located in the Huang-Huai-Hai region, and the area amounted to 42% of the national high-yield cropland area. Meanwhile, the proportion of high-yield cropland was lower than 15% in Hei- Iongjiang, Sichuan, and Inner Mongolia, which had the largest area allocated to cropland in China. If all the medium-yield cropland could be improved to the productive level of high-yield cropland and the low-yield cropland could be improved to the level of medium-yield cropland, the total productivity of the land would increase 19% and 24%, respectively.
文摘The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated conditions in semi arid environment, Sargodha, Punjab. The model was evaluated with observed data collected in trials which were conducted during spring season in 2010 and 2011 in Sargodha, Punjab, Pakistan. Split plot design was used in layout of experiment with three replications. The hybrids (Hysun-33 & S-278) and N levels (0, 75, 150 and 225 kg.ha-1) were allotted in main and sub plots, respectively. The OILCROP-SUN model showed that the model was able to simulate growth and yield of sunflower with an average of 10.44 error% between observed and simulated achene yield (AY). The results of simulation analysis indicated that nitrogen rate of 150 kg.N.ha-1 (N3) produced the highest yield as compared to other treatments. Furthermore, the economic analysis through mean Gini Dominance also showed the dominance of this treatment compared to other treatment combinations. Thus management strategy consisting?of treatment 150 kg.N.ha-1 was the best for high yield of sunflower hybrids.
文摘Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitrogen levels. The effect of nitrogen (N) on growth and yield components of different sunflower (Helianthus annuus L.) hybrids were evaluated under agro-climatic conditions of Sargodha, Pakistan during spring 2013. The experiment was laid out in a randomized complete block design with split plot arrangement having three replications, keeping cultivars in the main plots and nitrogen levels (0, 45, 90,135 and 180 kg/ha) in sub plots. OIL CROP-SUN Model showed that the model was able to simulate the growth and yield of sunflower with an average of 10.44 error% between observed and simulate achene yield (AY). The result of simulation indicates that nitrogen rate of 180 kg/ha produced highest achene yield in S-78 hybrid as compared to other treatments and Hysun-33 cultivar.
基金supported by the National Natural Science Foundation of China (32322064,32101340)the Jiangsu Provincial Natural Science Foundation for Distinguished Young Scholars (BK20220083)+1 种基金the Carbon Peak and Carbon Neutralization Key Science and technology Program of Suzhou (ST202228)Songhan Wang acknowledges the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2021QNRC001).
文摘Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors,of which the most important one is the increasing carbon dioxide(CO_(2))concentrations.Estimates of CO_(2) fertilization effect(CFE)on rice,however,still had large uncertainties.Therefore,using the rice planting areas in East China as the study area,we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models,and found that the CFE predicted by these models had significant differences.We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province.Using CFE measurements from a field experiment as benchmark,we have developed an experiment–model integration approach aiming to reduce this variation.This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty,which is beneficial for the accurate prediction of future global rice yield in the context of climate change.
