Agricultural Meteorological Stations Research Articles

An integrated approach to obtain high-precision regional root water uptake maps

Root water uptake (RWU) is exceptionally difficult to determine in situ due to its unique nature of occurring underground. Accurately characterizing the spatial–temporal characteristics of RWU at a regional scale remains a significant challenge, and high-precision regional-scale RWU maps have not yet been reported. In this study, we develop a novel approach that integrates the Google Earth Engine (GEE) platform, mathematical models, and machine learning algorithms to generate high-precision regional maps of maize RWU in China’s primary maize production area. The GEE platform was used to extract essential parameters such as soil properties, leaf area index, and growth period. These variables, along with meteorological data from 122 agricultural meteorological stations, were used to calculate RWU at a point scale using the Hydrus-1D and AquaCrop models. Finally, we utilized Hydrus-calculated point-scale RWU fluxes, along with 31 covariates extracted from the GEE platform, in conjunction with the CatBoost algorithm to develop a machine-learning model for estimating regional-scale RWU. The accuracy of the developed machine-learning model was validated using statistical measures, such asa mean absolute error (0.0299 cm d−1), a root mean squared error (0.0398 cm d−1), and the correlation coefficient (0.9178). The model was then applied to generate high-precision (500 m × 500 m) regional-scale RWU maps from 2015 to 2019. Additionally, our analysis identified several parameters, including TempDew (i.e., 2-m dewpoint temperature), SurfaceSM (i.e., top layer soil moisture), TempMax (i.e., max 2-m air temperature), and Latitude, as key factors influencing regional-scale RWU. This approach provides a robust strategy for accurately generating regional-scale RWU maps, even with limited data. Furthermore, it can be extended to obtain other regional indices, offering valuable insights for various real-world applications.

ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China

Abstract. Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis. Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar (https://doi.org/10.7910/DVN/EUP8EY, Liu et al., 2023). Overall, the ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by +0.7, −0.7, and −5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by −0.5, +2.7, and −0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by −0.7, +3.8, and −1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change.

Estimation of Spring Maize Planting Dates in China Using the Environmental Similarity Method

Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale spatial distribution with high accuracy for planting dates is essential for the widespread application of crop growth models. In this study, a planting date prediction method based on environmental similarity was developed in accordance with the third law of geography. Spring maize planting date observations from 124 agricultural meteorological experiment stations in China over the years 1992–2010 were used as the data source. Samples spanning from 1992 to 2009 were allocated as training data, while samples from 2010 constituted the independent validation set. The results indicated that the root mean square error (RMSE) for spring maize planting date based on environmental similarity was 10 days, which is better than that of multiple regression analysis (RMSE = 13 days) in 2010. Additionally, when applied at varying scales, the accuracy of national-scale prediction was better than that of regional-scale prediction in areas with large differences in planting dates. Consequently, the method based on environmental similarity can effectively and accurately estimate planting date parameters at multiple scales and provide reasonable parameter support for large-scale crop growth modelling.

Reclassifying the Spring Maize Drought Index on the Loess Plateau under a Changing Climate

Drought is the main meteorological disaster that affects the yield and quality of spring maize on the Loess Plateau. This study used data of the spring maize growth period, relative soil humidity, and yield at 18 agricultural meteorological observation stations on the Loess Plateau from 1997 to 2013 to determine the drought category based on the yield reduction rate. Through the drought index according to the conformity rate of category standard and individual case verification, a refined suitability drought index of spring maize on the Loess Plateau was constructed, and the spatial distribution characteristics of drought in different growth stages of spring maize were analyzed. The results showed the following: (1) The number of days in the whole growth period of spring maize in all regions of the Loess Plateau has been extended. The average sowing date of spring maize in the northwest region of the Loess Plateau was 9 April, and that in the east and central regions was 26 April. In terms of spatial distribution, each growth period was gradually delayed from west to east. (2) The correlation between relative soil humidity and yield of spring maize at the jointing stage and heading stage was the best, followed by the milky stage and mature stage, and the relative soil humidity at the sowing stage and emergence stage had little effect on the yield. (3) According to the national drought category standard “Drought Category of Spring Maize in the North”, based on the data of yield reduction rate, the drought index of spring maize on the Loess Plateau was refined by region and growth stage. The drought category index values of spring maize in different growth stages and regions changed according to the revised drought category standard, with 71.4% of the sites in the sowing seedling stage and 85.7% of the sites in the seedling jointing stage, and the revised drought category was more severe than the national drought category standard, while at 57.1% of the sites in the jointing and tasseling stages and 71.4% in the tasseling and milking stages, the revised drought category was less severe than the national drought category standard. (4) Based on the revised refined drought index for spring maize on the Loess Plateau, the spatial distribution of drought occurrence frequency across different growth stages of spring maize on the Loess Plateau was analyzed. The frequency of drought occurrence during the seeding and emergence stages was 25–75%. With the change in growth stages, the high-value area of drought occurrence frequency gradually moved northward, and the overall frequency of drought occurrence decreased. For the milky mature stage, the frequency of drought occurrence in a few regions was around 42%, and the drought frequency in most regions was between 8% and 33%.

Effects of Climate Change on Wheat Yield and Nitrogen Losses per Unit of Yield in the Middle and Lower Reaches of the Yangtze River in China

Nitrogen fertilizer is one of the essential nutrients for wheat growth and development, and it plays an important role in increasing and stabilizing wheat yield. Future climate change will affect wheat growth, development, and yield, since climate change will also alter nitrogen cycles in farmland. Therefore, further research is needed to understand the response of wheat yield and nitrogen losses to climate change during cultivation. In this study, we investigate the wheat-producing region in the middle and lower reaches of the Yangtze River in China, one of the leading wheat-producing areas, by employing a random forest model using wheat yield records from agricultural meteorological observation stations and spatial data on wheat yield, nitrogen application rate, and nitrogen losses. The model predicts winter wheat yield and nitrogen losses in the middle and lower reaches of the Yangtze River based on CMIP6 meteorological data and related environmental variables, under SSP126 and SSP585 emission scenarios. The results show that future climate change (temperature and precipitation changes) will decrease winter wheat yield by 2~4% and reduce total nitrogen losses by 0~5%, but in other areas, the total nitrogen losses will increase by 0~5% and the N leaching losses per unit of yield will increase by 0~10%. The results of this study can provide a theoretical basis and reference for optimizing nitrogen application rates, increasing yield, and reducing nitrogen losses in wheat cultivation under climate change conditions.

An Increase in Solar Radiation in the Late Growth Period of Maize Alleviates the Adverse Effects of Climate Warming on the Growth and Development of Maize

Against the background of long-term climate change, quantifying the response of maize growth and development to climate change during critical growth stages will contribute to future decision-making in maize production. However, there have been few reports on the impact of climate change on maize dry matter accumulation and yield formation using long-term field trial data. In this study, field trial data from 13 agricultural meteorological stations in the Beijing–Tianjin–Hebei region from 1981 to 2017 were analyzed using partial correlation analysis and multiple regression models to investigate the effects of climate change on maize growth and yield. The results showed that the average temperature (Tavg) and accumulated effective precipitation (EP) during the maize growing season increased while the accumulated solar radiation (SRD) decreased from 1981 to 2017. During the seedling stage (GS1, VE-V8) and ear development stage (GS2, V8-R1), Tavg increased by 0.34 °C and 0.36 °C/decade, respectively, and EP increased by 1.83 mm and 3.35 mm/decade, respectively. The significant increase in Tavg during GS1 was the main reason for the inhibitory effect of climate change on maize growth, development, and biomass accumulation. However, the increase in SRD during the grain formation stage (GS3, R1–R3) and grain filling stage (GS4, R3–R6) was favorable for yield formation, increasing the grain number per ear (GN) and grain weight (GW) by 5.00% and 2.84%, respectively. SRD significantly increased after the silk stage, partially offsetting the adverse effects of temperature on maize yield formation, but the final result was a 0.18% and 0.94% reduction in maize plant dry weight (TDW) and grain yield (GY), respectively, due to the combined effects of the three climate factors. Spatially, climate change mainly had a negative impact on maize in the eastern and western parts of the central region of Beijing–Tianjin–Hebei, with a maximum GY reduction of up to 34.06%. The results of this study can provide a scientific basis for future decision-making in maize production against the background of climate change.

A 30 m annual maize phenology dataset from 1985 to 2020 in China

Abstract. Crop phenology indicators provide essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference in dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets are consistent with in situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding the spatial patterns and temporal dynamics. Furthermore, we observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the changes in temperature and precipitation, which further altered the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the future agroecosystem response to global warming. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021).

A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation

As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha−1. Compared to county-level statistics during 2007–2015, the ranges of R2, RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha−1, and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.

Spatio-Temporal Analysis of Changes in Winter Wheat Cold Damage with Meteorological Elements in Huang-Huai-Hai Plain from 2011 to 2020

The Huang-Huai-Hai Plain is one of the typical agri-ecosystems in China, which suffers from cold damage frequently resulting in substantial economic losses. In order to monitor the changes in the occurrence of cold damage in an effective and large-scale manner, and to determine their meteorological causes, this paper collected low temperature data from the agricultural meteorological stations and remote sensing data of MODIS from 2005 to 2015, and constructed a monitoring model of cold damage to winter wheat in Huang-Huai-Hai Plain based on the Logistic regression model. This model was used to analyze the spatio-temporal changes of cold damage of winter wheat in Huang-Huai-Hai Plain from 2011 to 2020, and correlation analysis was performed with the spatio-temporal changes of meteorological factors to ascertain how they affect cold damage. The results show that the harm from cold damage in winter wheat has been gradually decreasing from 2011 to 2020, and the cold damage areas with high probability and high frequency are moving from north to south. The meteorological elements with the greatest impacts on the degree of cold damage from stronger to weaker are heat, precipitation and sunshine duration, whose influence has spatial variability.

Effect of Snow Cover on Spring Soil Moisture Content in Key Agricultural Areas of Northeast China

As an important source of soil moisture content during spring in high-latitude areas, snow cover affects the occurrence of spring drought and crop yield and quality. There has not been sufficient research on the effect of winter snow cover on spring soil moisture content. This paper focuses on the main agricultural areas of Northeast China—the Songnen Plain and the Sanjiang Plain. Using meteorological data of both spring soil moisture content and snow cover at 19 agricultural meteorological stations from 1983 to 2019, the effect of snow cover on spring soil moisture content in the Sanjiang Plain and Songnen Plain is studied by variance analysis, spatial analysis, and correlation analysis. The results show that: (1) Compared to the Sanjiang Plain, the Songnen Plain has a significantly lower content of soil moisture at the surface (0–10 cm) and deep layer (10–20 cm, 20–30 cm) during the entire spring and every month of spring (p < 0.05), and a greater interannual variation of soil moisture. (2) Snow cover has a significant effect on spring soil moisture in the Songnen Plain, but not as much as one in the Sanjiang Plain. For the Songnen Plain, snow-cover duration and the snow-cover onset date has a lasting influence on spring soil moisture until May, which can extend to as deep as 20–30 cm. As months go by, its influence on shallow-layer soil gradually wears off. Maximum snow depth and the snow-cover end date only influence the April surface soil. (3) Snow cover has a strong effect on soil moisture conservation in more arid areas. Delayed snow-cover onset date, earlier snow-cover end date, and significantly shortened snow-cover duration all contribute to a spring drought soil condition in the Songnen Plain.

Assessing impact of weather variability and changing climate on oil-palm yield in major growing regions of southern Thailand


 Oil palm yield is very responsive to weather fluctuations in the growing season. The purpose of this study was to investigate the relationship between yield variation and climate trends in the major oil palm-growing regions, especially in Southern Thailand (Chumphon; CP, Ranong; RN, Krabi; KB, Trang; TR, Satun; ST, Phang-Nga; PN, SuratThani; SR and Nakhon Si Thammarat; NS) where oil palm has been grown in a large plantation. Monthly weather variables from 16 agricultural meteorological stations were analyzed by linear and non-linear regressions over 28 years in each major oil palm-producing region. To evaluate the trends of changes in weather parameters and yield, a statistical model was developed for estimating oil palm yield based on climatic trends during 1994-2017. The results showed that warming trends were observed at all major oil palm-growing regions. There were pieces of evidence of significant correlation in temperature trends which had the strongest values in KB (Tmax, R2=0.534**) and PN (Tmin, R2=0.670**). The highest trends of ET and RH were also markedly increased in SR (R2=0.618**). Whereas precipitation trend had slightly increasing changes in CP (R2=0.220**) and PN (R2=0.233**). In addition, the annual trends in the values of Heliothermal Index, Dryness Index and Cool Night Index were markedly increased in NS, RN and KB, respectively. Comparing climate variables and yield variations over 19 years, the study indicated that the relationships between observed yield and estimated yield had highly significant differences in CP (R2=0.468**), SR (R2=0.735***) and NS (R2=0.579***), but there was lower value in KB (R2=0.098*) than those of the other regions. Therefore, this study indicates that recent climate trends have had an implicit effect on oil palm yield in the major producing regions in Southern Thailand. This study could be a guideline to further planning for oil palm management. 

Using Artificial Neural Networks Models for predicting wheat yield

Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period (1986-2012) were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013-2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range ConclusionsThe research assessment in concerning MLP, GRNN and RBNN in the field of wheat yield forecast. The forecast was worked utilizing the climate variables namely; Rain (R), maximum temperature (Tmax), mean temperature (Taver) ,minimum temperature (Tmin), potential evapotranspiration (PET), dew point data (DP), wind speed (WS) and irrigation requirement (IR) for wheat. Data of historical 31 years (1986 to 2012) were collected from standard agricultural meteorological stations of the Agricultural Research Center in the tests station of Sakha Province, Kafer el Shikh Governorate, Egypt, Data of 27 years for the period (1986-2012) were retained to develop the models and the data of four years (2013 and 2016) were expended to evaluate the models, to compare their outputs with the data measured these data did not affect in the process of building neural networks models. Results discovered that the ideal conformation for the FFNN model involved of one layer (8-40-1). The hidden layers had 40 nodes in the hidden layer for the ANN model. Hyperbolic tangent transfer function was engaged in hidden and output layers of the ANN display. The learning rate and the momentum parameter were 0.005 and 0.9 resp. for the ANN model. Iterations were 1000 epochs during training process for the ANN model. The outcome represented that GRNN extant well forecast outcomes as competed to FFNN and RBNN.

ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products

Abstract. Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need high-resolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530; Luo et al., 2019).

Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

Identification of complete drivers for phenology changes is crucial for developing prediction models of plant phenology. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital role in feeding human beings. In this study, we investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP) using both field and satellite data. Considering the large discrepancies between the existing satellite estimation and field measurements of winter wheat phenology, we first improved the MODIS-based estimation of green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD) with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation with pre-season temperature. These results suggest that both the climatic factors and phenological interactions should be included in the future development of winter wheat phenology models to improve the prediction accuracies.

Trends in maize (Zea mays L.) phenology and sensitivity to climate factors in China from 1981 to 2010.

Changes in crop phenology may reflect crop responses and adaptation to climate change. In this study, we used observational data (1981-2010) of maize (Zea mays L.) phenology from agricultural meteorological stations in the major maize-growing regions of China to examine spatiotemporal changes in the phenologies and growth periods and associated sensitivities to changes in major climatic factors. The results showed that, during the study period, sowing, tasseling, and maturity dates for maize were delayed in most maize growth regions. The lengths of vegetative growth period (VGP, from emergence to tasseling) were increased in spring and spring-summer maize growth regions and decreased in summer maize growth regions; the lengths of the maize reproductive growth period (RGP, from tasseling to maturity) and whole growth period (WGP, from emergence to maturity) were mostly extended (except NWMR_SU). Overall, sensitivity of maize VGP, RGP, and WGP was negatively related to average temperature (P < 0.01) and positively related to precipitation and sunshine hours (P < 0.01); there were variations in sensitivity among regions and data station locations. Precipitation was a driver of growth period length in the northwest inland maize region, whereas mean temperature and sunshine hours were drivers in the southwest hilly region.

Spatiotemporal changes of rice phenology in China under climate change from 1981 to 2010

An examination of multiple spatiotemporal changes in crop phenology is critical for enabling regional agricultural activities to adapt to climate change. This study utilizes observed phenological data on early, single, and late rice collected from 39 agricultural meteorological stations in China over the 1981–2010 period to investigate changes in 10 phenological dates and three growing periods. Trends in air temperature, precipitation, sunshine hours, and growing degree days over the study period were also recorded. It was found that, on average, the dates of sowing (0.03 d a−1), emergence (0.01 d a−1), booting (0.00 d a−1), heading (0.05 d a−1), milk ripening (0.22 d a−1), and maturity (0.08 d a−1) were delayed across all stations, whereas the dates of trefoil (0.07 d a−1), transplanting (0.07 d a−1), regreening (0.11 d a−1), and tillering (0.11 d a−1) were advanced. There were different trends among different growing periods. Across all stations, on average, a slightly reduced trend for vegetative (0.00 d a−1) and reproductive growing periods (0.01 d a−1) was observed, while an extended trend for whole growing periods (0.03 d a−1) was found. Sensitivity analyses of rice growing periods and climatic factors indicated that increases in temperature and sunshine hours tended to reduce lengths of the growing periods at most stations, whereas precipitation has tended to extend the lengths of the reproductive growing periods over the past decades.

How Much Are Planting Dates for Maize Affected by the Climate Trend? Lessons for Scenario Analysis Using Land Surface Models

Process-based land surface models are important tools to study the historical and future effects of climate change and land use change. The planting date has a considerable effect on crop growth and consequently on dynamic parameters used in land surface models, for example albedo and actual evapotranspiration. If planting dates can be related to climate, scenarios can use this relation to estimate planting dates. Such a relation is expected to differ according to agro-ecological zone. In this study, spring and summer maize planting date observations at 188 agricultural meteorological experiment stations of China, as well as monthly weather records, over the years 1992–2010 were used as the data source. In order to quantify the relation between planting dates and climate parameters, growing season monthly average minimum temperature (Tmin), mean temperature (T), and precipitation (P) were used. The time trend analysis of planting dates and weather data, principal component analysis (PCA) of weather data, and multivariate regression of planting dates as affected by weather data were used. Both Tmin and T increased during this period in most zones, whereas precipitation showed no trend. In southwest and northwest China, maize planting dates advanced significantly for both spring and summer maize. However, in the north China plain (summer maize) and northeast China (spring maize), the planting date was significantly delayed. Ordinary least squares multivariate regression models were able to explain 33% and 59% of the variance of planting dates in the southwest China (i.e., the humid subtropics zone) for spring and summer maize, respectively. However, only 3% could be explained in the Loess Plateau. Thus, adjusting planting dates in scenario analysis using land surface models is indicated for some zones, but not others, where socioeconomic factors are dominant.

Relative soil moisture in China’s farmland

Based on the data of relative soil moisture in 653 agricultural meteorological stations during the period of 1993‒2013 in China, the characteristics and regularity of spatial and temporal variation of relative soil moisture in China’s farmland were analyzed and discussed using geostatistical methods. The results showed that the relative soil moisture of China’s farmland has shown a fluctuant increasing trend since 1993. The relative soil moisture of China’s farmland is more than 60% in general, its distribution area has been expanded northward and westward with the summer monsoon since mid-April and began to shrink eastward and southward in late October. The value of relative soil moisture increases with the increase of soil depth. On an interannual scale, the relative soil moisture of farmland increased fastest in summer and autumn, and its variation range decreased with the increase of soil depth. The relative soil moisture was positively correlated with precipitation, and negatively correlated with potential evaporation and temperature. The correlation between relative soil moisture and various meteorological factors weakened as soil depth increased. The meteorological factors have a great influence on relative soil moisture of dry land in spring, summer and autumn and they also have a greater impact on relative soil moisture of paddy fields in winter.

The Design of a Drought Weather Index Insurance System for Summer Maize in Anhui Province, China

We analyzed precipitation anomalies of summer maize in order to establish a drought weather index model that is based on tassel appearance and maturity stage as well as drought yield reduction rate. We utilized daily precipitation data encompassing the period between 1971 and 2010 from 15 agricultural meteorological observation stations within a summer maize planting area in Anhui Province, China. A compensation standard for insurance claims resulting from summer maize drought is proposed in this paper, and revisions are presented to the drought weather index by incorporating relative humidity from the initial insurance period. The results of this study enabled the generation of a summer maize drought weather index insurance product which was then tested within the city of Huaibei in Suixi County, Anhui Province. Results also demonstrate that application of a weather index insurance product can rapidly and objectively enable the provision of economic compensation in the aftermath of agricultural disasters.

Assessing the combined effects of climatic factors on spring wheat phenophase and grain yield in Inner Mongolia, China.

Understanding the regional relationships between climate change and crop production will benefit strategic decisions for future agricultural adaptation in China. In this study, the combined effects of climatic factors on spring wheat phenophase and grain yield over the past three decades in Inner Mongolia, China, were explored based on the daily climate variables from 1981–2014 and detailed observed data of spring wheat from 1981–2014. Inner Mongolia was divided into three different climate type regions, the eastern, central and western regions. The data were gathered from 10 representative agricultural meteorological experimental stations in Inner Mongolia and analysed with the Agricultural Production Systems Simulator (APSIM) model. First, the performance of the APSIM model in the spring wheat planting areas of Inner Mongolia was tested. Then, the key climatic factors limiting the phenophases and yield of spring wheat were identified. Finally, the responses of spring wheat phenophases and yield to climate change were further explored regionally. Our results revealed a general yield reduction of spring wheat in response to the pronounced climate warming from 1981 to 2014, with an average of 3564 kg·ha-1. The regional differences in yields were significant. The maximum potential yield of spring wheat was found in the western region. However, the minimum potential yield was found in the middle region. The air temperature and soil surface temperature were the optimum climatic factors that affected the key phenophases of spring wheat in Inner Mongolia. The influence of the average maximum temperature on the key phenophases of spring wheat was greater than the average minimum temperature, followed by the relative humidity and solar radiation. The most insensitive climatic factors were precipitation, wind speed and reference crop evapotranspiration. As for the yield of spring wheat, temperature, solar radiation and air relative humidity were major meteorological factors that affected in the eastern and western Inner Mongolia. Furthermore, the effect of the average minimum temperature on yield was greater than that of the average maximum temperature. The increase of temperature in the western and middle regions would reduce the spring wheat yield, while in the eastern region due to the rising temperature, the spring wheat yield increased. The increase of solar radiation in the eastern and central regions would increase the yield of spring wheat. The increased air relative humidity would make the western spring wheat yield increased and the eastern spring wheat yield decreased. Finally, the models describing combined effects of these dominant climatic factors on the maturity and yield in different regions of Inner Mongolia were used to establish geographical differences. Our findings have important implications for improving climate change impact studies and for local agricultural production to cope with ongoing climate change.