Table of Content

20 September 2024, Volume 45 Issue 9

Previous Issue   

Cover Download

Article Download

Characteristics of Soil Carbon and Nitrogen Leaching and Transport on Slopes under Hydraulic Erosion

ZHOU Huai-zhou, WU Bi-qiong, CHEN Peng-yu, JIAN Yi, DENG Jie, SONG Chun-lin, YANG Yu, WANG Gen-xu, SUN Shou-qin

2024, 45(9):  943-952.  doi:10.3969/j.issn.1000-6362.2024.09.001

Asbtract ( 110 )   PDF (1737KB) ( 123 )  

Related Articles | Metrics

Carbon and nitrogen leaching and seepage with subsurface flow is one of the main paths of soil carbon and nitrogen loss. However, as great challenges lie in direct observation, soil carbon and nitrogen leaching related research is relatively lagging behind. Especially due to the complexity of the soil subsurface flow, some problems have not been fully elucidated. In this paper, the characteristics and observation methodologies of soil carbon and nitrogen leaching under the influence of soil, environment and subsurface flow were first summarized, and numerous complicated factors demonstrated to affect carbon and nitrogen leaching, where the influence of rainfall and soil properties were pre-dominant. Field measurement could reflect the natural process of leaching, the indoor experiment could effectively quantify the influential factors on leaching, and the mathematical model could help understand the physical mechanism of carbon and nitrogen leaching by simulating water flow and matter transport in porous media. Future research could combine these methods to achieve a better research effect. The aim of this paper was to provide a certain understanding of the role of carbon and nitrogen in the process of matter cycling and nutrient transfer, and to provide a scientific basis for better regulation of the dynamics of carbon and nitrogen mineralisation, sequestration, leaching and plant uptake in future ecosystems.

Estimating High Spatial and Temporal Resolution MODIS Remote Sensing Evapotranspiration Data in Southern Red Soil Region Based on ESTARFM Model

FENG Jing-yi, JING Yuan-shu, RAN Chu-yu, Sachini Kaushalya Dissanayake S. D

2024, 45(9):  953-967.  doi:10.3969/j.issn.1000-6362.2024.09.002

Asbtract ( 93 )   PDF (8218KB) ( 59 )  

Related Articles | Metrics

Evapotranspiration is an important component of the water cycle, influencing crop growth and grain production. Obtaining long-term, high-resolution evapotranspiration data can help optimize regional water resource allocation. Using Landsat and MODIS remote sensing data, along with ground observation flux data and meteorological data, the SEBS model and ESTARFM model are utilized to derive high spatiotemporal resolution evapotranspiration estimates for the southern red soil area from April to October 2019. The spatiotemporal variation characteristics and influencing factors of the high resolution evapotranspiration are analyzed. The results show that high spatial resolution remote sensing images can achieve higher accuracy in simulating remote sensing-based evapotranspiration. The simulation performance of the SEBS model driven by Landsat remote sensing data is better than that of the SEBS model based on MODIS data. Comparison between the high spatiotemporal resolution evapotranspiration obtained by the ESTARFM model and the measured evapotranspiration using a large aperture scintillometer shows a RMSE of 0.68mm·d⁻¹ and R² of 0.87. The simulated high spatiotemporal resolution daily evapotranspiration from April to October 2019 is spatially correlated with land use types, with evapotranspiration rates as follows: forest > paddy field > other agricultural land. Temporally, evapotranspiration showed an increasing trend from April to August, followed by a gradual decrease from August to October. Temperature is found to be the main meteorological factor affecting evapotranspiration in the study area. The high spatiotemporal resolution evapotranspiration obtained using the SEBS and ESTARFM models shows good agreement with ground measurements. The combination of the SEBS and ESTARFM models can serve as an effective tool for estimating evapotranspiration in southern red soil regions.

Comprehensive Evaluation of Heat Tolerance for Japonica Rice in Liaohe Plain Rice-growing Region

SONG Xiao-wen, LIU Chun-xi, CHEN Qian, WANG Guo-jiao, SUN Bei, YANG Xiao-jin, YIN Hong

2024, 45(9):  968-983.  doi:10.3969/j.issn.1000-6362.2024.09.003

Asbtract ( 79 )   PDF (986KB) ( 69 )  

Related Articles | Metrics

To analyze the heat tolerance of japonica rice in Liaohe plain rice-growing region, and establish an effective evaluation system of heat tolerance, 20 main varieties of japonica rice suitable for cultivating over this region were selected and planted under simulated temperature increase using the free air temperature increase system. The heat tolerance of different japonica rice varieties was comprehensively evaluated by multivariate statistical analysis methods based on the heat tolerance coefficients of 10 rice trait indicators, the effective panicle number (X₁), grain number per spike (X₂), seed setting rate (X₃), 1000-grain weight (X₄), yield (X₅), brown rice rate (X₆), milled rice rate (X₇), protein content (X₈), amylose content (X₉), and taste value (X₁₀) after harvesting and air-drying the rice. The results showed that the trait indicators except taste value were significantly or extremely reduced under the warming. Correlation analysis based on heat tolerance coefficients revealed different degrees of association between rice trait indicators. Principal component analysis condensed the 10 individual indicators into 4 mutually independent comprehensive indicators. The 4 comprehensive indicators had contribution rates of 34.816%, 19.265%, 15.636%, and 10.605%, respectively, giving a cumulative contribution rate of 80.322%. The comprehensive evaluation values of heat tolerance (D_i value) were calculated using the membership function method. The higher the D_i value, the stronger the heat tolerance. The heat tolerance of 'Yanjing 456' was the strongest and that of 'Shennong 016' was the worst. The 20 japonica rice varieties were classified into three categories based on their heat tolerance: the first category comprised 7 heat-tolerant varieties, the second category included 6 moderately heat-tolerant varieties, and the third category consisted of 7 heat-sensitive varieties.

Evaluating of Spring Potato Late Blight Climate Risk Based on MaxEnt and CARAH Model in Chongqing

LUO Zi-zi, CHEN Dong-dong, WANG Ru-lin, CHEN Huan, HAN Xu, TANG Yu-xue, YANG Yuan-yan, ZHU Yu-han, ZHANG Yue

2024, 45(9):  984-997.  doi:10.3969/j.issn.1000-6362.2024.09.004

Asbtract ( 107 )   PDF (4949KB) ( 75 )  

Related Articles | Metrics

The hourly average temperature and average relative humidity data of 260 meteorological stations in Chongqing from February to June in 2019−2023 were used to simulate the geographic distribution of spring potato late blight infection risk using the CARAH late blight model. The accuracy of the simulation was tested by using the late blight infection data of 26 monitoring stations in Wuxi county, Chongqing in 2022. Based on the geographic distribution of spring potato late blight infection risk simulated, the maximum entropy model was constructed using the climate grid data of the monthly average temperature, maximum temperature, minimum temperature, water vapor pressure and precipitation from February to June in 1970−2000 to analyze the climate impact factors of spring potato late blight in Chongqing, and to evaluate the climate risk of spring potato late blight, providing a reference for the prediction and scientific prevention of the disease. The results showed that simulations of late blight infection based on hourly weather data had high accuracy, with a false positive rate of 12.5%, false negative rate of 18.5% and TS score of 0.73. The mean area under curve (AUC) of the receiver operating characteristic (ROC) was above 0.9, indicating higher accuracy of the simulation results. Precipitation was the dominant climate factor affecting the risk distribution of late blight of spring potato in Chongqing, while relative humidity and temperature were important climate factors. Climate variables at the seedling stage and bud flowering stage had a great impact on the distribution of late blight risk. The low risk area of late blight of each maturity(early/late) and susceptibility (resistant/susceptible) combination of spring potato was less than or close to 10000km², with an average area proportion of 10.2%. The medium risk area and high risk area were both more than 30000km², with an average area proportion of 43.7% and 46.1%, respectively. The climate risk of spring potato late blight showed a spatial distribution characteristic of "high in the middle and low in the periphery" in Chongqing. The high risk area was concentrated in the parallel valley area in eastern Sichuan, the medium risk area was mainly distributed in Daba mountain area in northeast Chongqing, Wuling mountain area in southeast Chongqing, and the hilly area in central Sichuan in west Chongqing, and the low risk area was scattered in the fringe of Chongqing. Spring potato production in Chongqing area faces a high climate risk of late blight, with significant spatial differentiation characteristics. It should be addressed through reasonable production layout and improved cultivation techniques.

Effects of Cropping Patterns and Fruit Nodes on Photosynthetic Characteristics, Yield and Quality of Muskmelon in Greenhouse

LV Xue-mei, AI Xin, ZHANG Lei, ZHAGN Ji-bo

2024, 45(9):  998-1011.  doi:10.3969/j.issn.1000-6362.2024.09.005

Asbtract ( 93 )   PDF (1319KB) ( 98 )  

Related Articles | Metrics

A two-factor split-plot experiment was carried out in the greenhouse of Linyi agricultural meteorological experimental site using muskmelon 'Xizhoumi 25' as the experimental material. The main area was two planting patterns of single row on ridge (D1) and double row on ridge (D2), and the sub-area was three kinds of melon nodes of 11−12 nodes (R1), 13−14 nodes (R2) and 15−16 nodes (R3), a total of 6 treatments, The plant leaf area and photosynthetic parameters, yield components and fruit quality were measured to explore the effects of different planting patterns and melon retention nodes on photosynthetic characteristics, yield and quality of muskmelon in greenhouse. The results showed that the photosynthetic efficiency of single row planting mode was higher than that of double row planting mode. The fruit diameter, single fruit weight and soluble sugar content of single row planting mode were significantly increased by 2.4%, 11.1% and 9.5%, respectively, compared with double row planting mode (P<0.05). The effect of node location on vegetative growth and reproductive growth of the plant was obvious. The photosynthetic characteristics, fruit length, fruit diameter and single fruit weight of R2 treatment were the best, and the single fruit weight was 9.41% and 14.73% higher than that of R1 and R3, respectively (P<0.05). The effect on fruit quality was extremely significant (P<0.01). The R1 treatment had the highest soluble sugar content, the R2 treatment had the highest sugar-acid ratio, and the R3 treatment had the highest VC and titratable acid content. The interaction between planting pattern and node position had significant effects on leaf area, SPAD, Pn, Ci, Gs and Tr of muskmelon (P<0.05), and the SPAD, Pn, Gs and Tr values of D1R2 combination leaves were the largest before maturity stage. Interaction effection had a significant effect on fruit length (P<0.05), and had a very significant effect on fruit diameter, fruit shape index and single fruit weight (P<0.01). Among them, the fruit diameter value of D1R1 combination was the largest, and the fruit shape index of D1R3 was the highest. The fruit length and single fruit weight of the D1R2 combination were the largest, and the average single fruit weight was 28.7% higher than that of other combinations. The interaction had a significant effect on VC content (P<0.05), and had a very significant effect on titratable acid, soluble sugar content and sugar-acid ratio (P<0.01). Of these, the D2R3 combination had the highest VC content, D1R3 had the highest titratable acid content, D1R1 had the highest soluble sugar content and D1R2 had the highest sugar-acid ratio. Under the same planting density and number of functional leaves (22500 plants·ha⁻¹, 25 nodes topping), the three evaluation indexes of yield flavor and taste and VC content of single row planting on ridge and 13−14 nodes interaction (D1R2) mode were the highest, and the comprehensive evaluation of its commodity value was the best. It is recommended that this mode of planting should be applied and generalized in the vertical cultivation of the early spring melons in greenhouses in the north of China.




Comparison of Single Leaf Weight Models for Tobacco in the Central and Eastern Regions of Guizhou Province Based on Meteorological Factors

LI Xiang, XIA Xiao-ling, LIU Yan-xia, LIU Tao, ZENG Li-ping, CHEN Li-ping, XU Jian, WANG Jun-fei, WU Zhou, WANG Ke-min

2024, 45(9):  1012-1026.  doi:10.3969/j.issn.1000-6362.2024.09.006

Asbtract ( 103 )   PDF (4231KB) ( 101 )  

Related Articles | Metrics

The meteorological and tobacco leaf weight data from 53 districts (counties) in the tobacco-growing region of central and eastern Guizhou from 2010 to 2021 were selected to analyze the influence of meteorological factors on the leaf weight of flue-cured tobacco. Four linear and non-linear models were established using four meteorological factors on an annual scale (four-factor model) and multiple meteorological factors on a ten-day scale (multi-factor model), respectively. The accuracy and stability of the models were validated to explore the advantages and disadvantages of different models in the central and eastern regions of Guizhou. The results indicated: (1) the average single leaf weight of the lower, cutters, and upper leaves in the tobacco-growing areas of central and eastern Guizhou in the past 12 years were 5.7g, 9.2g, and 10.8g, respectively. The variation range in single leaf weight was smaller in the lower leaves than in the cutters and upper leaves. (2) Based on the modeling with four factors, the BP neural network algorithm model for the single leaf weight of lower leaves showed the best simulation effect, with R² of 0.26 and RMSE of 0.84g. The simulation effect of the random forest algorithm model exhibited the best simulation performance, with R² of 0.33 and RMSE of 1.08g for single leaf weight in the cutters leaf, and R² of 0.16 and RMSE of 1.59g for single leaf weight in the upper leaf. When model with multiple factors, the random forest algorithm model for the single leaf weight of the lower and upper leaves performed best, with R² of 0.22 and RMSE of 0.85g for the lower leaves and R² of 0.16 and RMSE of 1.57g for the upper leaves. For the cutters leaves, the BP neural network algorithm model demonstrated the optimal simulation effect, with R² of 0.18 and RMSE of 1.14g. (3) The accuracy of the multiple-factor stepwise regression algorithm for predicting the single-leaf weight of the lower leaves was 34.86%, while the simulation accuracy of other algorithms for the same leaf position was generally above 70%. The multi-factor BP neural network model predicted the highest accuracy of 86.24% in the lower single leaf weights, while the random forest model predicted the highest accuracy of 89.91% in the cutters single leaf weights under four-factor. The random forest algorithm with multifactor had the highest prediction accuracy of 84.4% for the upper leaf weight. (4) Based on the four-factor algorithm model for predicting the single-leaf weight in the central and eastern regions of Guizhou in 2021, the accuracy of the four algorithmic models for predicting the single-leaf weight of the cutters and lower leaves showed little difference. The accuracy of the random forest algorithm for predicting the single-leaf weight of the upper leaves was the highest, reaching 91%, which was significantly higher than the other three algorithmic models. Under the multi-factor condition, the average simulated accuracy of the various models for the lower, middle, and upper parts was 81%, 74%, and 85% respectively. (5) At the city-level dimension, under the condition of four factors, the average accuracy of the BP neural network model for simulating the single-leaf weight of the lower leaves was 84%. There was no significant difference in the simulation results of the four algorithmic models for the cutters leaves. The upper single leaf weight simulation resulted in the highest average accuracy of 92% with the random forest algorithm. Under the condition of multiple factors, the average accuracy of the linear regression model for simulating the single leaf weight of the lower leaves was 91%, while for the upper leaves, the linear regression model also had the highest average accuracy of 88%. In summary, for the average single-leaf weight of tobacco leaves in the central and eastern regions of Guizhou, using the four-factor BP neural network and random forest algorithm can better simulate the impact of meteorological factors on the single-leaf weight of tobacco leaves, providing scientific basis for the regulation of tobacco leaf production.

Base the Multi-model Forecasting Products Compared Simulation Capability of Heat Damage on Early Rice in the Middle and Lower Reaches of Yangtze River

LIN Zhi-jian, YAO Jun-meng, LI Chun-hui, ZHANG Ying, CAI Zhe

2024, 45(9):  1027-1040.  doi:10.3969/j.issn.1000-6362.2024.09.007

Asbtract ( 73 )   PDF (17152KB) ( 58 )  

Related Articles | Metrics

Using CLDAS products, the European Centre for Medium-Range Weather Forecasts high resolution model (ECMWF_HR), Global/Regional assimilation and prediction system-Global Forecast System (GRAPES_ GFS) and the data of the national meteorological center forecast (SCMOC) were analyzed for identifying the occurrence and intensity of heat damage under the condition of 1-3d, 1-5d and 1-7d in advance during the early rice booting maturity period in the middle and lower reaches of the Yangtze river from 2021 to 2023. The results showed that: (1) for the identification effect on the occurrence of heat damage, SCMOC products had better identification effect, with the probability of detection (POD) greater than 0.6 for each prediction period. The TS score between 0.49 to 0.59. The POD of GRAPES_GFS product was also greater than 0.6, but the false alarm rate (FAR) was greater than 0.3, with the TS score of 0.45 to 0.52. The ECWMF_HR product had the worst discriminative effect, with POD less than 0.4 and TS score less than 0.3. (2) For the identification effect of heat damage intensity, the value of accumulated heat damage of SCMOC products and CLDAS products were the closest, with the correlation coefficient (Cor) greater than 0.6. The root mean square error (RMSE) increased the forecast period, with values of 1.57℃·d, 2.57℃·d, and 3.43℃·d, respectively. GRAPES_GFS product had a strong forecast for most of Hubei and northern Jiangxi, while the forecast for central and southern Hunan was significantly weak. The Cor of GRAPES_GFS product with the CLDAS product was about 0.06 lower than that of the SCMOC product, and the RMSE was about 0.4℃·d higher. The Cor of ECMWF_HR products and CLDAS products was less than 0.4 in each forecast period, and the RMSE was 0.5℃·d to 1.0℃·d higher than that of the SCMOC products. (3) For the heat damage in different years, SCMOC and GRAPES_GFS products had good identification effect in 2022 and 2023, but the former had better identification effect in 2021. The ECMWF_HR product showed a significant weak forecast for the occurrence and intensity of heat damage in the study area from 2021 to 2023. In summary, the SCMOC prediction product has better identification effect on the heat damage of early rice in the middle and lower reaches of the Yangtze River, which can provide reference to carry out disaster prevention and reduction work on the heat damage of early rice.

Identifying the Freezing Damages of Winter Wheat in Spring and Simulating Their Decadal Changes in Henan Province Based on Deep Learning Model

HUANG Rui-xi, ZHAO Jun-fang, YANG Jia-qi, PENG Hui-wen, QIN Xi

2024, 45(9):  1041-1052.  doi:10.3969/j.issn.1000-6362.2024.09.008

Asbtract ( 111 )   PDF (16126KB) ( 83 )  

Related Articles | Metrics

 Freezing damage in spring is one of the serious agricultural meteorological disasters for winter wheat production in the Huang-Huai-Hai plain. In order to effectively identify freezing damages of winter wheat in spring, this paper used a deep learning long short-term memory model LSTM to identify the occurrences of freezing damages of winter wheat in spring in Henan province, which was an important winter wheat planting area in China. The spatiotemporal distributions and decadal changes of freezing damages of winter wheat in spring based on the multi-source meteorological, crop, and disaster data from 99 meteorological stations from 1981 to 2020 were explored. The results indicated that: (1) the optimized LSTM model effectively identified freezing damages of winter wheat in spring. The daily minimum temperature simulated by LSTM model from 2017 to 2020 showed an average absolute percentage error (MAPE) of 8.73% and a goodness of fit (R²) with 0.90 compared to the actual minimum temperature. Based on the disaster data and freezing damage index for winter wheat in spring, the actual disaster situations from 2017 to 2020 were found to be consistent with the simulated results by the optimized LSTM model. (2) From 1981 to 2020, the harm of mild freezing damage to winter wheat in spring in Henan province had gradually weakened, and the high frequency area of mild freezing damage moved from the Northeast to the East. The overall distribution of mild freezing damage of winter wheat in spring in Henan province between 1981 and 2020 was higher in the northeastern Henan, and lower in the southwestern and southeastern Henan, with an average frequency of 0−1.75 times·10y⁻¹ during the past 40 years. The frequency of mild freezing damage of winter wheat in spring decreased per decade, gradually decreasing from 0.843 times per decade to 0.157 times per decade. The high frequency area of mild freezing damage of winter wheat in spring moved from the Northeast to the East. (3) From 1981 to 2020, the frequency of severe freezing damage of winter wheat in spring in Henan province showed a trend of first increasing and then decreasing. The high frequency area of severe freezing damage of winter wheat in spring moved from the Northeast to the East. The overall distribution of severe freezing damage during the 40a period was higher in the East than in the West, and higher in the North than in the South. The average frequency of severe freezing damage in the 40a period was 0−2.75times·10y⁻¹. The frequency of severe freezing damage of winter wheat in spring increased from 0.508 times per decade to 0.857 times per decade and then decreased to 0.289 times per decade. The high frequency area of severe freezing damage moved from the Northeast to the East. The overall trend of severe freezing damage of winter wheat in spring in Henan Province was decreasing, but the frequency of extreme weather events under climate warming was increasing. Strengthening the researches on the impacts of major agricultural meteorological disasters on agricultural production in various regions in China under climate change was still one of the future research focuses. Various accuracy evaluation indicators of the deep learning LSTM model for identifying freezing damage of winter wheat in spring proposed in this study were improved, and the simulation results of freezing damage by LSTM model were basically consistent with the actual occurrences. Our results can provide ideas and methods for large-scale freezing damage identification of winter wheat under global climate change, and also have certain reference values for other agricultural meteorological disaster identification.

The Rice Vulnerability to Sterile-type Chilling Disaster in China Based on Crop Model and Machine Learning

ZHANG Jing, ZHANG Zhao, ZHANG Liang-liang, CAO Juan, LUO Yu-chuan, HAN Ji-chong, TAO Fu-lu

2024, 45(9):  1053-1066.  doi:10.3969/j.issn.1000-6362.2024.09.009

Asbtract ( 93 )   PDF (10162KB) ( 38 )  

Related Articles | Metrics

Using the case study of a sterile-type chilling disaster during the head-flowering phase of rice, this study presents a novel approach to constructing vulnerability curves that can overcome data limitations while also taking into account crop growth mechanisms. Meteorological data during 1990-2010 was used to generate sterile-type chilling scenarios at county scale, estimated rice yield losses through combining one crop model (MCWLA) and machine learning (XGBoost) method, finally developed sterile-type chilling vulnerability curves for each main rice-planting zone in China and estimated long-term historical (1961-2010) yield loss caused by sterile-type chilling disasters. The results showed that: (1) Machine learning could effectively reproduce the estimation ability of crop model (RRMSE<6%, R²>0.93). (2) The sterile-type vulnerability decreased with decreasing latitude, and was weaker in growing seasons for late rice than that in early rice. (3) The historical yield loss was higher for single rice (1224kg·ha⁻¹) than for double rice (early rice: 868kg·ha⁻¹; late rice: 807kg·ha⁻¹). 

Spatio-temporal Variation of Guangxi Drought Based on the SPEI_PM and Its Correlation with ENSO

TANG Jin-li, HU Bao-qing, YU Bi-yun, ZOU Yi, SU Hong-xin

2024, 45(9):  1067-1078.  doi:10.3969/j.issn.1000-6362.2024.09.010

Asbtract ( 86 )   PDF (16254KB) ( 55 )  

Related Articles | Metrics

Using monthly data from 19 meteorological stations in Guangxi Zhuang Autonomous region from 1961 to 2020, this study calculated the standardized precipitation evapotranspiration index (SPEI) based on the Penman-Monteith evapotranspiration model (SPEI_PM). The temporal and spatial variation of meteorological drought were analyzed via linear tendency estimation, and Mann-Kendall trend test. The correlation between El Niño-Southern Oscillation (ENSO) and meteorological drought in Guangxi was also explored by cross-correlation function, cross wavelet transform and wavelet coherence. The values for summer, autumn, and annual SPEI_PM from 1961 to 2020 exhibited a significant linear trend of increasing wetness over time. Annual and seasonal droughts throughout the region were predominately mild and moderate, with less frequent severe and extreme droughts. Spring droughts were concentrated in the central part of the region, summer droughts in the southwest, autumn droughts in the southeast and northeast, and winter droughts spread throughout the region. Annual droughts were concentrated in the northeast. The rate of drought occurrence as measured by the stations in Guangxi exhibited a distinctly chronological character. Large-scale droughts (especially severe and extreme droughts) across the entire region occurred most often in the 1960s, 1970s, and 2000s. The impact of ENSO on the meteorological drought in Guangxi was characterized by spatial heterogeneity. Correlation between the ocean Niño index (ONI) and meteorological drought was highest in the northeast and southeast, as well as the northwest. There was no significant correlation between drought and ONI in coastal areas and most southwestern parts of the region. As a typical example from meteorological stations in northeastern Guangxi, SPEI-3 and ONI at the Guilin Meteorological station showed interannual oscillation cycles of 16−48 months in the period 1962−1976, and 12−64 months in the period 1990−2019. These results can serve as a useful reference for future drought forecasting and assessment in Guangxi.

Bibliometric-based Analysis on International Hotspots for Cimate-smart Agriculture

DENG Ming-jun, JIANG Bing-huan, CAO Xiao-tian, LUO Wen-bing

2024, 45(9):  1079-1093.  doi:10.3969/j.issn.1000-6362.2024.09.011

Asbtract ( 121 )   PDF (1569KB) ( 138 )  

Related Articles | Metrics

Climate-smart agriculture (CSA) has become a central element in driving the greening agenda in agriculture. An in-depth exploration of global progress of CSA research globally can help to improve the level of understanding and assessment of the field by both academics and practitioners alike. Based on the Web of Science database resources, this paper analyzed the international CSA research hotspots using a bibliometric approach, based on 814 papers published on CSA topics between 2014-01-01 and 2023-08-11. The main findings were as follows: (1) since 2018, there had been a notable surge in the publication of CSA-related papers and subsequent citations. The core of CSA research predominantly revolved around environmental science, sustainability studies, agronomy, and interdisciplinary fields; (2) Keyword hotspots mainly included climate-smart agriculture, climate change, adoption, conservation agriculture, management, food security, mitigation, and farmers, etc.; (3) The research hotspots mainly included conservation agriculture, the impact and quantification of CSA on soil organic carbon, the effects of CSA application in different regions, the key factors affecting the adoption of CSA by farmers, and CSA-related decision support, which revealed that the effects of CSA implementation vary in different countries and regions, and that the key factors for the adoption of CSA by farmers were diversified, which suggested that policymakers must consider diversified factors comprehensively when designing and implementing CSA strategies to ensure the effective promotion and localized application of CSA measures across the globe. This suggested that policymakers should consider the diversity of factors when designing and implementing CSA strategies to ensure the effective promotion and localized application of CSA measures in all parts of the world; (4) The 25 references with a surge in citations from 2014−2023 were mainly focused on the core topics of conservation agriculture, the application and challenges of CSA, as well as the impacts of climate change on agriculture and coping strategies.