An assessment of regional vulnerability of rice to climate change in India

A simulation analysis was carried out using the InfoCrop-rice model to quantify impacts and adaptation gains, as well as to identify vulnerable regions for irrigated and rain fed rice cultivation in future climates in India. Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model (MIROC3.2.HI) and a regional climate model (PRECIS) were considered for the study. On an aggregated scale, the mean of all emission scenarios indicate that climate change is likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069), and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios they are projected to decrease only marginally (<2.5 %). However, spatial variations exist for the magnitude of the impact, with some regions likely to be affected more than others. Adaptation strategies comprising agronomical management can offset negative impacts in the near future—particularly in rainfed conditions—but in the longer run, developing suitable varieties coupled with improved and efficient crop husbandry will become essential. For irrigated rice crop, genotypic and agronomic improvements will become crucial; while for rainfed conditions, improved management and additional fertilizers will be needed. Basically climate change is likely to exhibit three types of impacts on rice crop: i) regions that are adversely affected by climate change can gain in net productivity with adaptation; ii) regions that are adversely affected will still remain vulnerable despite adaptation gains; and iii) rainfed regions (with currently low rainfall) that are likely to gain due to increase in rainfall can further benefit by adaptation. Regions falling in the vulnerable category even after suggested adaptation to climate change will require more intensive, specific and innovative adaptation options. The present analysis indicates the possibility of substantial improvement in yields with efficient utilization of inputs and adoption of improved varieties.     

The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States

We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO₂ runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO₂, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions.     

The Effects of Spatial Scale of Climate Scenarios on Economic Assessments: An Example from U.S. Agriculture

The appropriate level of spatial resolution for climate scenarios is a key uncertainty in climate impact studies and regional integrated assessments. To the extent that such uncertainty may affect the magnitude of economic estimates of climate change, it has implications for the public policy debates concerning the efficiency of CO₂ control options. In this article, we investigate the effects that different climate scenario resolutions have on economic estimates of the impacts of future climate changeon agriculture in the United States. These results are derived via a set of procedures and an analytical model that has been used previously in climate change assessments. The results demonstrate that the spatial scale of climate scenarios affects the estimates of both regional changes in crop yields and the economic impact on the agricultural sector as a whole. An assessment based on the finer scale climatological information consistently yielded a less favorable assessment of the implications of climate change. Regional indicators of economic activity were of opposite sign in some regions, based on the scenario scale. Such differences in economic magnitudes or signs are potentially important in examining whether past climate change assessments may misstate the economic consequences of such changes. The results reported here suggest that refinement of the spatial scale of scenarios should be carefully considered in future impacts research.     

Climate Change and Rice Yields in Diverse Agro-Environments of India. I. Evaluation of Impact Assessment Models

This paper reports results of a comparison of two popular rice growth models- Ceres-Rice and ORYZA1N for the same input conditions. Both models use different approaches for simulating growth and yield, are sensitive to climate change parameters, and represent two major schools of crop modelling. A dataset of 32 experiments consisting of 98 treatments was assembled from an extensive literature search. These experiments were conducted over the period of 1980–1993 in diverse Indian locations from 11° N–33° N. The treatments varied in N management, sowing dates, varieties and seasons. The flowering duration in the dataset varied between 37 and 86 days and grain yields between 2587 kg ha^–1 and 8877 kg ha^–1. Seven treatments from this dataset, one for each variety, were selected for calibration. The genetic coefficients of different varieties used in the analysis for both models were estimated from this short-listed dataset by repeated iterations until a close match between simulated and observed phenology and yield was obtained in these treatments. Similarly 11 treatments with zero or low N applications were used for calibration of initial soil N for different locations. The remaining 80 treatments were used for validation of the models. Both models predicted satisfactorily the trends of leaf area and dry matter growth, grain number, days to flowering and grain yields. Simulated yields were within +15% of the measurements. Considering that the field measurements also generally have 10–15% error and that the treatments widely varied in weather conditions, particularly in temperature, it was concluded that both models are adequate to simulate the effects of climate change on rice yields in diverse agro-environments of India that are free from all pests.     

First-order impacts on winter and summer crops assessed with various high-resolution climate models in the Iberian Peninsula

The first-order or initial agricultural impacts of climate change in the Iberian Peninsula were evaluated by linking crop simulation models to several high-resolution climate models (RCMs). The RCMs provided the daily weather data for control, and the A2 and B2 IPCC scenarios. All RCMs used boundary conditions from the atmospheric general circulation model (AGCM) HadAM3 while two were also bounded to two other AGCMs. The analyses were standardised to control the sources of variation and uncertainties that were added in the process. Climatic impacts on wheat and maize of climate were derived from the A2 scenario generated by RCMs bounded to HadAM3. Some results derived from B2 scenarios are included for comparisons together with impacts derived from RCMs using different boundary conditions. Crop models were used as impact models and yield was used as an indicator that summarised the effects of climate to quantify initial impacts and differentiate among regions. Comparison among RCMs was made through the choice of different crop management options. All RCM-crop model combinations detected crop failures for winter wheat in the South under control and future scenarios, and projected yield increases for spring wheat in northern and high altitude areas. Although projected impacts differed among RCMs, similar trends emerged for relative yields for some regions. RCM-crop model outputs compared favourably to others using European Re-Analysis data (ERA-15), establishing the feasibility of using direct daily outputs from RCM for impact analysis. Uncertainties were quantified as the standard deviation of the mean obtained for all RCMs in each location and differed greatly between winter (wheat) and summer (maize) seasons, being smaller in the latter.     

Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa

Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6–24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40–55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture.     

Effects of climate change on grain maize yield potential in the european community

Grain maize yield in the main arable areas of the European Community (E.C.) was calculated with a simulation model, WOFOST, using historical weather data and average soil characteristics. The sensitivity of the model to individual weather variables was determined. Subsequent analyses were made using climate change scenarios with and without the direct effects of increased atmospheric CO₂. The impact of crop management (sowing date, irrigation and cultivar type) in a changed climate was also assessed. Scenario climate change generally results in larger grain yields for the northern E.C., similar or slightly smaller yields for the central E.C. and considerably smaller yields for the southern E.C. The various climate change scenarios used appear to give considerably different changes in grain yield, both for each location and for the E.C. as a whole. Management analyses show that for both current and scenario climates the largest grain yield will be attained by varieties with an early start of grain filling, that average irrigation requirements to attain potential grain yield in the E.C. will increase with climate change but will decrease with both increased CO₂ and climate change, and that sowing at both current and scenarios climate should occur as early as possible.The U.S. Government right to retain a nonexclusive, royalty-free licence in and to any copyright is acknowledged.     

Potential impact of future climate change on crop yield in northeastern China

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China(NEC) by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5(CMIP5) under the Representative Concentration Pathway 4.5 scenario(RCP4.5), the projected maize yield changes for three future periods [2010–39(period 1), 2040–69(period 2), and 2070–99(period 3)] relative to the mean yield in the baseline period(1976–2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase(but with great uncertainty) against the background of global warming, which may offer some valuable guidance to government policymakers.     

Climate change impacts on regional rice production in China

Rice (Oryza sativa L.) production is an important contributor to China’s food security. Climate change, and its impact on rice production, presents challenges in meeting China’s future rice production requirements. In this study, we conducted a comprehensive analysis of how rice yield responds to climate change under different scenarios and assessed the associated simulation uncertainties of various regional-scale climate models. Simulation was performed based on a regional calibrated crop model (CERES-Rice) and spatially matched climatic (from 17 global climate models), soil, management, and cultivar parameters. Grain-filling periods for early rice were shortened by 2–7 days in three time slices (2030s, 2050s, and 2070s), whereas grain-filling periods for late rice were shortened by 10–19 days in three time slices. Most of the negative effects of climate change were predicted to affect single-crop rice in central China. Average yields of single-crop rice treated with CO₂ fertiliser in central China were predicted to be reduced by 10, 11, and 11% during the 2030s, 2050s, and 2070s, respectively, compared to the 2000s, if planting dates remained unchanged. If planting dates were optimised, single-crop rice yields were predicted to increase by 3, 7, and 11% during the 2030s, 2050s, and 2070s, respectively. In response to climate changes, early and single-crop rice should be planted earlier, and late rice planting should be delayed. The predicted net effect would be to prolong the grain-filling period and optimise rice yield.     

Analysis of farm performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity

The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships. Our results suggest that socio-economic conditions and farm characteristics should be considered when analyzing effects of climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should be considered in impact assessment models. Different farm types with different management strategies will adapt differently.     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia

Future climate projections and impact analyses are pivotal to evaluate the potential change in crop yield under climate change. Impact assessment of climate change is also essential to prepare and implement adaptation measures for farmers and policymakers. However, there are uncertainties associated with climate change impact assessment when combining crop models and climate models under different emission scenarios. This study quantifies the various sources of uncertainty associated with future climate change effects on wheat productivity at six representative sites covering dry and wet environments in Australia based on 12 soil types and 12 nitrogen application rates using one crop model driven by 28 global climate models (GCMs) under two representative concentration pathways (RCPs) at near future period 2021–2060 and far future period 2061–2100. We used the analysis of variance (ANOVA) to quantify the sources of uncertainty in wheat yield change. Our results indicated that GCM uncertainty largely dominated over RCPs, nitrogen rates, and soils for the projections of wheat yield at drier locations. However, at wetter sites, the largest share of uncertainty was nitrogen, followed by GCMs, soils, and RCPs. In addition, the soil types at two northern sites in the study area had greater effects on yield change uncertainty probably due to the interaction effect of seasonal rainfall and soil water storage capacity. We concluded that the relative contributions of different uncertainty sources are dependent on climatic location. Understanding the share of uncertainty in climate impact assessment is important for model choice and will provide a basis for producing more reliable impact assessment.     

Agricultural risk management policies under climate uncertainty

Climate change is forecasted to increase the variability of weather conditions and the frequency of extreme events. Due to potential adverse impacts on crop yields it will have implications for demand of agricultural risk management instruments and farmers’ adaptation strategies. Evidence on climate change impacts on crop yield variability and estimates of production risk from farm surveys in Australia, Canada and Spain, are used to analyse the policy choice between three different types of insurance (individual, area-yield and weather index) and ex post payments. The results are found to be subject to strong uncertainties and depend on the risk profile of different farmers and locations; the paper provides several insights on how to analyse these complexities. In general, area yield performs best more often across our countries and scenarios, in particular for the baseline and marginal climate change (without increases in extreme events). However, area yield can be very expensive if farmers have limited information on how climate change affects yields (misalignment in expectations), and particularly so under extreme climate change scenarios. In these more challenging cases, ex post payments perform well to increase low incomes when the risk is systemic like in Australia; Weather index performs well to reduce the welfare costs of risks when the correlation between yields and index is increased by the extreme events. The paper also analyses the robustness of different instruments in the face of limited knowledge of the probabilities of different climate change scenarios; highlighting that this added layer of uncertainty could be overcome to provide sound policy advice under uncertainties introduced by climate change. The role of providing information to farmers on impacts of climate change emerges as a crucial result of this paper as indicated by the significantly higher budgetary expenditures occurring across all instruments when farmers’ expectations are misaligned relative to actual impacts of climate change.     

Impact assessment of climate change,carbon dioxide fertilization and constant growing season on rice yields in China

Rice is the staple food in China, and the country’s enlarging population puts increasing pressure on its rice production as well as on that of the world. In this study, we estimate the impact of climate change, CO₂ fertilization, crop adaptation and the interactions of these three factors on the rice yields of China using model simulation with four hypothetical scenarios. According to the results of the model simulation, the rice yields without CO₂ fertilization are predicted to decrease by 3.3 % in the 2040s. Considering a constant rice-growing season (GS), the rice yields are predicted to increase by 3.2 %. When the effect of CO₂ fertilization is integrated into the Agro-C model, the expected rice yields increase by 20.9 %. When constant GS and CO₂ fertilization are both integrated into the model, the predicted rice yield increases by 28.6 %. In summary, the rice yields in China are predicted to decrease in the 2040s by 0.22 t/ha due to climate change, to increase by 0.44 t/ha due to a constant GS and to increase by 1.65 t/ha due to CO₂ fertilization. The benefits of crop adaptation would completely offset the negative impact of climate change. In the future, the most of the positive effects of climate change are expected to occur in northeastern and northwestern China, and the expansion of rice cultivation in northeastern China should further enhance the stability of rice production in China.     

Vulnerability and adaptation assessments of agriculturalcrops under climate change in the Southeastern USA

Summary  It is expected that a change in climatic conditions due to global warming will directly impact agricultural production. Most climate change studies have been applied at very large scales, in which regions were represented by only one or two weather stations, which were mainly located at airports of major cities. The objective of this study was to determine the potential impact of climate change at a local level, taking into account weather data recorded at remote locations. Daily weather data for a 30-year period were obtained for more than 500 sites, representing the southeastern region of the USA. Climate change scenarios, using transient and equilibrium global circulation models (GCM), were defined, created and applied to the daily historical weather data. The modified temperature, precipitation and solar radiation databases corresponding to each of the climate change scenarios were used to run the CERES v.3.5 simulation model for maize and winter wheat and the CROPGRO v.3.5 model for soybean and peanut. The GCM scenarios projected a shorter duration of the crop-growing season. Under the current level of CO₂, the GCM scenarios projected a decrease of crop yields in the 2020s. When the direct effects of CO₂ were assumed in the study, the scenarios resulted in an increase in soybean and peanut yield. Under equilibrium , the GCM climate change scenarios projected a decrease of maize and winter wheat yield. The indirect effects of climate change also tended to decrease soybean and peanut yield. However, when the direct effects of CO₂ were included, most of the scenarios resulted in an increase in legume yields. Possible changes in sowing data, hybrids and cultivar selection, and fertilization were considered as adaptation options to mitigate the potential negative impact of potential warming. Received July 20, 1999/Revised April 18, 2000     

Climate change impact assessment: the role of climate extremes in crop yield simulation

This work was aimed at assessing the role of climate extremes in climate change impact assessment of typical winter and summer Mediterranean crops by using Regional Circulation Model (RCM) outputs as drivers of a modified version of the CropSyst model. More specifically, climate change effects were investigated on sunflower (Helianthus annuus L.) and winter wheat (Triticum aestivum L.) development and yield under the A2 and B2 scenarios of the IPCC Special Report on Emissions Scenarios (SRES). The direct impact of extreme climate events (i.e. heat stress at anthesis stage) was also included. The increase in both mean temperatures and temperature extremes under A2 and B2 scenarios (2071?C2100) resulted in: a general advancement of the main phenological stages, shortening of the growing season and an increase in the frequency of heat stress during anthesis with respect to the baseline (1961?C1990). The potential impact of these changes on crop yields was evaluated. It was found that winter and summer crops may possess a different fitting capacity to climate change. Sunflower, cultivated in the southern regions of the Mediterranean countries, was more prone to the direct effect of heat stress at anthesis and drought during its growing cycle. These factors resulted in severe yield reduction. In contrast, the lower frequency of heat stress and drought allowed the winter wheat crop to attain increased yields with respect to the baseline period. It can be concluded that the impact of extreme events should be included in crop-modelling approaches, otherwise there is the risk of underestimating crop yield losses, which in turn would result in the application of incorrect policies for coping with climate change.     

Agricultural Vulnerability and Adaptation in Developing Countries: The Asia-Pacific Region

During the last decades, a large number of climate change impact studies on agriculture have been conducted qualitatively and quantitatively in many regions of the Asia-Pacific. Changes in average climate conditions and climate variability will have a significant consequence on crop yields in many parts of the Asia-Pacific. Crop yield and productivity changes will vary considerably across the region. Vulnerability to climate change depends not only on physical and biological response but also on socioeconomic characteristics. Adaptation strategies that consider changes in crop varieties or in the timing of agricultural activities imply low costs and, if readily undertaken, can compensate for some of the yield loss simulated with the climate change scenarios. The studies reviewed here suggest that the regions of Tropical Asia appear to be among the more vulnerable; some areas of Temperate Asia also appear to be vulnerable.     

Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part I. The Uncertainty Due to Spatial Scale

We investigated the effect of two different spatial scales of climate change scenarios on crop yields simulated by the EPIC crop model for corn, soybean, and wheat, in the central Great Plains of the United States. The effect of climate change alone was investigated in Part I. In Part II (Easterling et al., 2001) we considered the effects ofCO₂ fertilization effects and adaptation in addition to climate change. The scenarios were formed from five years of control and 2 ×CO₂ runs of a high resolution regional climate model (RegCM) and the same from an Australian coarse resolution general circulation model (GCM), which provided the initial and lateral boundary conditions for the regional model runs. We also investigated the effect of two different spatial resolutions of soil input parameters to the crop models. We found that for corn and soybean in the eastern part of the study area, significantly different mean yield changes were calculated depending on the scenario used. Changes in simulated dryland wheat yields in the western areas were very similar, regardless of the scale of the scenario. The spatial scale of soils had a strong effect on the spatial variance and pattern of yields across the study area, but less effect on the mean aggregated yields. We investigated what aspects of the differences in the scenarios were most important for explaining the different simulated yield responses. For instance, precipitation changes in June were most important for corn and soybean in the eastern CSIRO grid boxes. We establish the spatial scale of climate changescenarios as an important uncertainty for climate change impacts analysis.     

Spatial Analysis of Environmental Change Impacts on Wheat Production in Mid-Lower North, South Australia

Three environmental change scenarios (the best scenario, the most likely scenario and the worst scenario) were used by the APSIM (Agricultural Production System sIMulator) Wheat module to study the possible impacts of future environmental change (climate change plus pCO₂ change) on wheat production in the Mid-Lower North of South Australia. GIS software was used to manage spatial-climate data and spatial-soil data and to present the results. Study results show that grain yield (kg ha⁻¹) was adversely affected under the worst environmental change scenario (−100% ∼ −42%) and the most likely environmental change scenario (−58% ∼ −3%). Grain nitrogen content (% N) either increased or decreased depending on the environmental change scenarios used and climate divisions (−25% ∼ +42%). Spatial variability was found for projected impact outcomes within climate divisions indicating the necessity of including the spatial distribution of soil properties in impact assessment.     

A New Economic Assessment Index for the Impact of Climate Change on Grain Yield

The impact of climate change on agriculture has received wide attention by the scientific community. This paper studies how to assess the grain yield impact of climate change, according to the climate change over a long time period in the future as predicted by a climate system model. The application of the concept of a traditional ＂yield impact of meteorological factor （YIMF）＂ or ＂yield impact of weather factor＂ to the grain yield assessment of a decadal or even a longer timescale would be suffocated at the outset because the YIMF is for studying the phenomenon on an interannual timescale, and it is difficult to distinguish between the trend caused by climate change and the one resulting from changes in non-climatic factors. Therefore, the concept of the yield impact of climatic change （YICC）, which is defined as the difference in the per unit area yields （PUAY） of a grain crop under a changing and an envisaged invariant climate conditions, is presented in this paper to assess the impact of global climate change on grain yields. The climatic factor has been introduced into the renowned economic Cobb-Douglas model, yielding a quantitative assessment method of YICC using real data. The method has been tested using the historical data of Northeast China, and the results show that it has an encouraging application outlook.