Chinese Journal of Agrometeorology

Climate-smart Water-nitrogen Managements for Main Patterns of Double-cropping System in North China Plain

GUAN Kai-xin, GUO Er-jing, GAO Ji-qing, ZHANG Wen-meng, ZHANG Zhen-tao, ZHOU Li-tao, GUO Shi-bo, YANG Xiao-guang

2023, 44(06): 453-468. doi:10.3969/j.issn.1000-6362.2023.06.001

Asbtract ( 163 )

PDF (12001KB) ( 127 )

Related Articles | Metrics

This study aimed to (1) quantify the impact of different cropping systems on greenhouse gas emission amount and intensity in North China Plain under climate change, (2) clarify the spatial variations of annual yields, resource use efficiencies and greenhouse gas emissions in North China Plain under different water and nitrogen managements, (3) provide a scientific basis for the smart water and nitrogen managements of main cropping systems in North China Plain to adapt to climate change. Based on the meteorological data, soil data and crop data of 44 meteorological stations in North China Plain from 1981 to 2020, the agricultural production systems sIMulator (APSIM) model was used to simulate the yield and greenhouse gas emissions of spring maize single-cropping and wheat-maize double-cropping systems in North China Plain. The changes of greenhouse gas emissions with the increase of annual yield in wheat-maize double-cropping system compared with spring maize single-cropping system were analyzed. On this basis, APSIM model was used to simulate the annual yields and greenhouse gas emissions of wheat-maize double-cropping system in North China Plain under different water and nitrogen managements, and their agronomic efficiencies of applied N and water productivities were also calculated. Besides, the normalization method was adopted to clarify whether each management achieve the multi-objective synergistic effects of high annual yield, high resource use efficiency and low greenhouse gas emission. In addition, the smart water and nitrogen managements for main cropping systems to adapt to climate change in the study area were proposed. The results showed that：(1) from 1981 to 2020, the annual greenhouse gas emission amount of spring maize single-cropping system in the study area was 0.48×104−1.65×104kg CO2-eqha−1, while the annual greenhouse gas emission amount of wheat-maize double-cropping system was 2.36×104−4.11×104kg CO2-eqha−1. The greenhouse gas emission amount of wheat-maize double-cropping system increased by 406.7% compared with that of spring maize single-cropping system in the study area. (2) From 1981 to 2020, the greenhouse gas emission intensity of spring maize single-cropping system in the study area was 0.08−0.35kg CO2-eq kg−1, while the greenhouse gas emission intensity of wheat-maize double-cropping system was 0.19−0.47kg CO2-eqkg−1. The greenhouse gas emission intensity of wheat-maize double-cropping system increased by 153.8% compared with that of spring maize single-cropping system in the study area. (3) With the increase of irrigation amount of winter wheat, annual yields and greenhouse gas emissions of wheat-maize double-cropping system increased. However, irrigation stage of winter wheat had no significant effect on annual yields and greenhouse gas emissions. (4) The annual yields and greenhouse gas emissions increased significantly with the increase of nitrogen application when the total nitrogen application of each crop was 0−225kgha−1 for wheat-maize double-cropping system. However, once the total nitrogen application of each crop reached 225kgha−1 for wheat-maize double-cropping system in North China Plain, the annual yields had no significant change but the greenhouse gas emissions still significant increased with the increase of nitrogen application of each crop. The results indicate that the adoption of climate-smart water-nitrogen managements can achieve both high annual yield and relatively low greenhouse gas emissions in wheat-maize double-cropping system in North China Plain.

Spatial and Temporal Variation Characteristics of Blue-green Water Resources in Wujiang River Basin Based on SWAT Model

KANG Wen-dong, NI Fu-quan, DENG Yu, XIANG Jun

2023, 44(06): 469-478. doi:10.3969/j.issn.1000-6362.2023.06.002

Asbtract ( 152 )

PDF (8415KB) ( 72 )

Related Articles | Metrics

Blue-green water evaluation can provide scientific suggestion for the comprehensive management of water resources in the basin. Here Wujiang river basin was as an example, based on the SWAT (Soil and Water Assessment Tool) hydrological model, the annual runoff anomaly percentage method was used to determine different precipitation patterns. The Mann-Kendall trend method(M-K) and the linear regression trend analysis method were applied to evaluate the temporal and spatial characteristics of blue-green water resources from 1992 to 2019 and the differences in the distribution and spatial distribution of blue-green water in different precipitation years. The results showed that: (1) the simulation of the SWAT model was better, which describe the water cycle process in Wujiang river basin. (2) The annual average precipitation, blue water resources and green water resources in the basin were 1126mm, 549mm and 589mm, respectively. The precipitation and blue water resources generally showed a decrease trend, but the green water resources exhibited an increase trend. (3) The green water coefficient were 46%, 52% and 58% in the wet year, normal year and dry year, respectively. Thus, the amount of green water resources has changed and plays an important role in ecosystem maintenance. (4) From the upstream to the downstream of the basin, the precipitation and blue water resources increased first and then decreased, while the green water resources presented a trend of first increasing, then decreasing and finally increasing. (5) The temporal and spatial variation of blue water resources was mainly impacted by the change of precipitation, while that of green water resources was affected by the change of precipitation, temperature, and land use cover.

Effects of Deep Rotary Tillage Combined with Organic Fertilizer on Bacterial Community Structure and Function of Maize Rhizosphere Soil in Saline Alkali Land

MA Zhong-hua, LIU Ji-li, WU Na, YANG Yong-sen, HU Yong-qi, ZHE Yong-qing

2023, 44(06): 479-491. doi:10.3969/j.issn.1000-6362.2023.06.003

Asbtract ( 128 )

PDF (1858KB) ( 54 )

Related Articles | Metrics

The 16 SrRNA gene high-throughput sequencing technology was used to analyze the effects of different tillage methods (A1 traditional tillage, A2 deep rotary tillage) and organic fertilizer levels (B1: 0kg·ha−1, B2: 7500 kg·ha−1, B3: 15000kg·ha−1, B4: 22500kg·ha−1) on the bacterial community structure of maize rhizosphere saline alkali soil, and predict their functions. The results showed that: (1) the interaction effect of deep rotary tillage and organic fertilizer application significantly increased the observed index, Chao1 index, ACE index and aromatic index(P<0.05). (2)The bacterial community in maize rhizosphere was mainly composed of 31 phyla and 497 genera, including Proteobacteria(37.4%−54.4%), Acidobacteria(8.6%−2.9%), Actinobacteria(5.2%−13.3%). The analysis of bacterial community showed that the bacterial community structure was similar among different tillage methods, and the abundance of bacteria was different among organic fertilizer application levels. (3)The correlation analysis showed that the soil pH value was significantly negatively correlated with ACE index, Observed index and Chao1 index(r=−0.56, P=0.004); The activity of invertase (IA) was positively correlated with ACE index, Observed index and Chao1 index(r=0.52, P=0.01). Redundancy analysis showed that alkali hydrolyzed nitrogen, available phosphorus, available potassium, organic matter, alkaline phosphatase, sucrase and pH value were the key environmental factors affecting the bacterial community structure of corn farmland soil. (4)Deep rotary tillage combined with organic fertilizer significantly increased the gene abundance of bacterial signal transduction function in the rhizosphere soil of maize fields, such as carbohydrate metabolism, amino acid metabolism, other amino acid metabolism, carbohydrate biosynthesis and metabolism, cofactor and vitamin metabolism. The functional gene abundance of soil bacteria was the highest under A2B3 treatment. In this study, the treatment of deep tillage and organic fertilizer application rate of 15000kg·ha−1 was the most beneficial ways to improve the bacterial diversity and function of corn farmland.

Progress of DSSAT-CSM Model Application in Maize Planting Research

WANG Yu-ling, XU Chun-xia, BI Ya-qi, FAN Jun, GUO Rui-jia, WANG Jing, FAN Xing-ming

2023, 44(06): 492-501. doi:10.3969/j.issn.1000-6362.2023.06.004

Asbtract ( 203 )

PDF (448KB) ( 135 )

Related Articles | Metrics

Crop models play an important role in the simulation, evaluation and prediction of maize production. Through literature review, the authors systematically summarized the development and application of DSSAT-CSM model in China; the composition, development and shortcomings of DSSAT-CSM model; and the process and results of using crop model to simulate the key factors affecting maize growth. It provided reference and technical support for crop model to optimize maize growth and yield by adjusting crop variety parameters, temperature variation, nitrogen fertilizer measures, irrigation system and key soil factors. Uncertainty and deficiencies of current crop models were the key factors that limited simulation accuracy and efficiency. Therefore, standardizing data collection, coupling multiple types of crop models, optimizing dynamic management processes, and modifying and optimizing models are the future trends of crop models.

Effect Comparison of County-scale Model of Longan Yield in Guangdong Based on Two Datasets

YIN Mei-xiang, LUO Rui-ting, ZHU Ping, ZENG Qin-Wen, ZHAO Wei-Wen

2023, 44(06): 502-512. doi:10.3969/j.issn.1000-6362.2023.06.005

Asbtract ( 131 )

PDF (435KB) ( 70 )

Related Articles | Metrics

In order to construct a county-scale dynamic refined simulation model for longan yield, authors analyze the influence of meteorological factors on longan yield using the longan production data of Maoming from 1990 to 2020 and the daily meteorological data of the national meteorological observatory, establish and carry out the comparative analysis on the dynamic simulation model of longan yield in Huazhou, Gaozhou and Xinyi based on the random forest regression method and stepwise regression method with different data schemes. The results showed that the longan yield in Maoming is closely related to meteorological factors, and the minimum temperature and the relative humidity during the growth period have the greatest influence on the longan yield in Maoming, with 15 and 14 meteorological factors selected respectively, and their maximum correlation coefficients being −0.31 and 0.43, respectively. Compared with the multiple stepwise regression method, the accuracy of longan yield simulation model constructed by the random forest regression method is higher. The model determination coefficient (R2) is 0.97, which increases by 7%, the mean absolute error (MAE) is 210.16kg·ha−1, which decreases by 52%, and the root mean square error (RMSE) is 289.62kg·ha−1, which decreases by 46%. When the data of similar climate characteristic areas outside the simulation target region is introduced, the simulation result of the random forest regression model is significantly improved, with R2 increases by 3%, MAE decreases by 32%, and RMSE decreases by 31%, while the simulation result of the stepwise regression model has no significant change. The longan yield simulation model based on the random forest regression method is reliable, which can meet the demand for refined meteorological service of longan.

Product Design and Pricing of Kiwifruit High Temperature and Drought Composite Weather Index Insurance

WANG Wen, CHEN Yan, WANG Hong-mei

2023, 44(06): 513-522. doi:10.3969/j.issn.1000-6362.2023.06.006

Asbtract ( 178 )

PDF (490KB) ( 156 )

Related Articles | Metrics

Weather index insurance with the advantages of transparent information, convenient claim and settlement strong secondary market liquidity is an effective method to diversify agricultural risks, compared with traditional agricultural insurance products. However, the disaster mechanism of crops that are often affected by multiple hazards together during their growth process is very complex. Accurately constructing the correlation relationship model among multiple weather indices and between weather index and yield per unit area of agricultural products, expanding the underwriting liability of weather index insurance are important to reduce the basis risk, reasonably design weather index insurance and transfer the risk of agricultural weather disasters. In this study, based on the precipitation and temperature data of 24 years of day-by-day from 1995 to 2018 from the national meteorological network, a three-dimensional nested Copula model and a conditional mixed three-dimensional Copula model among precipitation, temperature and yield per unit area had been constructed for kiwifruit in Meixian county, Shaanxi province. The relationship between the three variables was simulated using the conditional mixed three-dimensional Copula model with higher simulation accuracy by comparing the error and the pure rate of composite weather index insurance was determined. The results showed that the pure insurance rate was 10.07% at 70% coverage level for a payout under the conditions of cumulative precipitation below 423.2mm and average daily maximum temperature above 26.40℃ from May to September. This study explored the cross-influence of high temperature and drought weather on the unit yield loss of kiwifruit and better clarified the correlation among high temperature, drought and kiwifruit yields, which could reduce the basis risk to a certain extent, improve the agricultural weather index insurance system, provide a new idea and method for the design of composite weather index insurance products, and contribute to the promotion and application of weather index insurance.

Evaluation of Rice Affected by Heat Damage in the Sichuan Basin in 2022 Based on Satellite and In-situ Observation

WANG Xin, YANG De-sheng, WANG Rui-ting, ZHAO Yi, WANG Ming-tian

2023, 44(06): 523-534. doi:10.3969/j.issn.1000-6362.2023.06.007

Asbtract ( 184 )

PDF (8606KB) ( 109 )

Related Articles | Metrics

In order to fully understand the disaster situation of rice high temperature heat damage in the Sichuan basin (SCB) in 2022, this study explores the monitoring and evaluation technology of rice high temperature heat damage suitable for Sichuan by using MODIS data, meteorological data, geographic auxiliary data and agricultural production data. Based on the remote sensing technology, the estimation of daily mean temperature and daily maximum temperature, the extraction of rice area, the identification of rice heading−flowering stage, and the estimation and grade evaluation of high temperature heat damage area during rice heading−flowering stage were studied in the SCB. The evaluation results were verified by high temperature heat damage measured by national meteorological station. The results showed that the mean temperature and maximum temperature can be obtained by merging satellite-retrieved temperature and in-situ observed temperature from dense automatic weather stations with high accuracy. Considering the characteristics of the growth period, the planting area and the key growth period of rice in the SCB could be accurately identified. The high temperature heat damage grade of rice at heading−flowering stage based on satellite-ground fusion data inversion was in good agreement with the measured heat damage grade at the station except for the mountainous region around the basin. The proposed methods can not only rapidly monitor the high temperature heat damage of rice in heading-flowering stage at any time, but also evaluate the distribution of heat damage, frequency distribution and disaster area of rice in heading−flowering stage in the annual study area. It can be applied to operational applications and progressively improved in services.

Impacts Report of Winter Meteorological Conditions on Agricultural Production in 2022/2023

LI Yi-jun, WANG Chun-zhi, TAN Fang-ying, HAN Li-juan, QIAN Yong-lan, ZHAO Xiu-lan

2023, 44(06): 535-537. doi:10.3969/j.issn.1000-6362.2023.06.008

Asbtract ( 159 )

PDF (463KB) ( 104 )

Related Articles | Metrics

In the winter of 2022/2023, the national average temperature was -2.9℃, which was higher than the same period of the perennial value (1991-2020). The average precipitation in winter was 31.3mm, 20.4% less than that in the same period. The national average sunshine was close to perennial value, and the eastern part of Southwest China was more than the same period. Most regions of the winter wheat areas in the north have abundant light and heat conditions, within many large range of rain and snow weather, most of the moisture is suitable to wheat safe winter and turn green. Most regions of the southern is enough warm and sunshine hours for the growth and development of rape, wheat, winter crops and economic forest fruits. However, the phase of drought in Guizhou, southern of Sichuan and Yunnan and the phase of low temperature in regions south of the Yangtze River were not good to the steady growth of summer crops.

Table of Content