Fulu Tao

How do various maize crop models vary in their responses to climate change factors

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

Future climate change, the agricultural water cycle, and agricultural production in China

Abstract Climate change would have a major impact on the hydrological cycle and consequently on available water resources, the potential for flood and drought, and agricultural productivity. In this study, the impacts of climate change on the agricultural water cycle and their implications for agricultural production in the 2020s were assessed by water-balance calculations for Chinese croplands. Temporal and spatial changes in potential evapotranspiration, actual evapotranspiration, soil-moisture, soil-moisture deficit, yield index, and cropland surface runoff under the baseline climate and a HADCM2 general circulation model (GCM) climate-change scenario were mapped on a grid of 0.5° latitude/longitude resolution. According to the analysis, agricultural water demand in south China is projected to decrease generally, and the cropland soil-moisture deficit would decrease due to climate change. However, in north China, agricultural water demand is expected to increase, and the soil-moisture deficit would increase generally. The changes in the water resources would have consequent impacts on the yield index. Cropland surface runoff during the growing period is expected to increase on some sloping croplands in the southwest mountain areas and in some areas along the south coast. These changes would have important implications for agricultural production. Particularly the rain-fed crops in the north China plain and northeast China would face water-related challenges in coming decades due to the expected increases in water demands and soil-moisture deficit, and decreases in precipitation.

Changes in agricultural water demands and soil moisture in China over the last half-century and their effects on agricultural production

It has become obvious in recent years that water is the most critical resource for Chinese agricultural ecosystems. Changes in agricultural water demands and soil moisture have significant implications for China’s water supply, the potential for drought and flood, and agricultural production. In the studies, we explored the changing trends in agricultural water demands, the changing trends and variability in soil moisture associated with both drought and increased surface runoff in Chinese croplands during the last half-century, and their impacts on agricultural production. We plotted temporal and spatial changes in agricultural water demands, soil moisture, soil-moisture variability, soil-moisture deficit, yield index, and surface runoff on a grid of 0.5 ◦ resolution. We found a trend toward agricultural water demands increasing, soil drying and significant changes in soil-moisture variability on the North China Plain and the Northeast China Plain. There was a significant decrease in agricultural water demands and a significant increase in soil-moisture levels in Southwest China, and a generally insignificant increase or decrease trend in agricultural water demands and soil-moisture levels in Southeast China. These changes in agricultural water demands and soil-moisture levels had corresponding impacts on soil-moisture deficit, and consequently on agricultural production. Increased surface runoff was found in the mountainous areas of the southwest and northeast, and in some areas along the South Coast.

Surface water quality and its control in a river with intensive human impacts–a case study of the Xiangjiang River, China

Surface water quality and its natural and anthropogenic controls in the Xiangjiang River were investigated using multivariate statistical approaches and a comprehensive observation dataset collected from 2004 to 2008. Cluster analysis (CA) grouped the 15 different sampling stations into five clusters with similar hydrochemistry characteristics and pollution levels. Four principal components (PCs), nutrients, heavy metals, natural components, and organic components, were extracted from the entire dataset. Comparison of the different regional characteristics of these four PCs revealed a decreasing trend for heavy metals and an increasing trend for organic factor on an annual scale, and the seasonal trend was only observed for natural factor. We also conducted analysis of variance (ANOVA) in combination with principal component analysis (PCA) to quantify the relative contribution of spatial and temporal variations to each of the four PCs. The results revealed that 62% of the contributions from the spatial sites were responsible for variations in heavy metals, while 83% of the contributions from the sampling time were responsible for natural variations observed. However, no significant spatial or temporal contributions were found to be responsible for the nutrient and organic variations. Finally, some suggestions regarding water management were put forward based on the current status and future trends of surface water quality in the Xiangjiang River.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981-2009 in China, and late rice was just opposite

Based on the crop trial data during 1981-2009 at 57 agricultural experimental stations across the North Eastern China Plain (NECP) and the middle and lower reaches of Yangtze River (MLRYR), we investigated how major climate variables had changed and how the climate change had affected crop growth and yield in a setting in which agronomic management practices were taken based on actual weather. We found a significant warming trend during rice growing season, and a general decreasing trend in solar radiation (SRD) in the MLRYR during 1981-2009. Rice transplanting, heading, and maturity dates were generally advanced, but the heading and maturity dates of single rice in the MLRYR (YZ_SR) and NECP (NE_SR) were delayed. Climate warming had a negative impact on growth period lengths at about 80% of the investigated stations. Nevertheless, the actual growth period lengths of YZ_SR and NE_SR, as well as the actual length of reproductive growth period (RGP) of early rice in the MLRYR (YZ_ER), were generally prolonged due to adoption of cultivars with longer growth period to obtain higher yield. In contrast, the actual growth period length of late rice in the MLRYR (YZ_LR) was shortened by both climate warming and adoption of early mature cultivars to prevent cold damage and obtain higher yield. During 1981-2009, climate warming and decrease in SRD changed the yield of YZ_ER by -0.59 to 2.4%; climate warming during RGP increased the yield of YZ_LR by 8.38-9.56%; climate warming and decrease in SRD jointly reduced yield of YZ_SR by 7.14-9.68%; climate warming and increase in SRD jointly increased the yield of NE_SR by 1.01-3.29%. Our study suggests that rice production in China has been affected by climate change, yet at the same time changes in varieties continue to be the major factor driving yield and growing period trends.

Changes in rice disasters across China in recent decades and the meteorological and agronomic causes

Both climate extremes and agricultural disasters have been reported to increase in recent decades; however, so far, we have little idea on the characteristics of agricultural disasters changes, as well as their meteorological and agronomic causes. Here, using the observed records on rice disasters at agro-meteorological stations across China and the meteorological indexes, we investigated the temporal and spatial changes of major rice disasters occurrence frequency and their relationships to climate change, climate extremes and agronomic practices from 1991 to 2009. We presented the temporal and spatial changes in occurrence frequency of major rice disasters, including droughts, floods, heat stress, chilling damage, insects and diseases, during the warmer period of 2000–2009, in comparison with the period of 1991–2000, based on both the observed records and the meteorological indexes. The results showed that changes in rice disasters could be largely ascribed to changes in climate extremes in recent decades. Floods, insects and diseases occurred more frequently at earlier growth stages; in contrast, chilling damage occurred more frequently at later growth stages in southwestern China during the period of 2000–2009, in comparison with the period of 1991–2000. Our findings highlighted the options should be taken timely and scientifically to reduce the disasters and to cope with ongoing climate change, based on the characteristics of agricultural disasters changes in recent decades.

A review on statistical models for identifying climate contributions to crop yields

Statistical models using historical data on crop yields and weather to calibrate relatively 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.