Table of Content

20 June 2025, Volume 46 Issue 6

Previous Issue   

Cover Download

Article Download

Characteristics of Climatic Seasonal Variation in Northeast China under the New Standard

SHAO Qi-duo, FENG Xi-yuan, REN Hang, TU Gang, LI Shang-feng, LIU Gang, YANG Xu, WU Di

2025, 46(6):  741-752.  doi:10.3969/j.issn.1000-6362.2025.06.001

Asbtract ( 6 )   PDF (16028KB) ( 6 )  

Related Articles | Metrics

According to the national standard of Climate Seasonal Division (GB\T 42074−2022), the characteristics of seasonal variation in Northeast China (NEC) for the period 1961–2020 were analyzed using CN05.1 gridded data, and the changes caused by the shift of the standards and climatological baselines were investigated. The results showed that the climatic season of NEC was divided into regions with four seasons and non−summer zone regions, and the non−summer regions were mainly located in the northern part of the NEC, high−altitude regions and their surroundings. Spring and summer started from the south to the northeast, from the central plains to the high altitude mountains, and vice versa in autumn and winter. Compared with the 1981–2010 baseline period, parts of the Sanjiang plain and Hulun lake changed from non−summer regions to four−season regions. The starting dates showed a significant advance of 1d·10y⁻¹ in spring over most of the NEC region, and a significant advance of 2−3d·10y⁻¹ in summer over the central and western parts of the Northeastern plains. The starting dates were significantly delayed in autumn over the four-season regions, and in winter over the non−summer regions and the central of Northeastern plains. The summer and winter duration were significantly prolonged and delayed, respectively. Compared to the original standard, there were more areas with significant changes in spring and summer starting dates and summer and winter duration under the new standard. The areas up to the summer standard showed a significant upward trend of 3.9PP·10y⁻¹ and had a significant positive correlation with the area−mean June−July−August NEC temperature. The rating of starting date of seasons obeys the normal distribution law, with a slight advance in summer.

Potential Impacts of 1.5℃ Warming on Soil Organic Carbon of Grasslands in Northern China

YANG Xin-yue, LI Hui-rong, ZHENG Hao-jun, JIANG Wen-fang, ZHANG Wen, YU Yong-qiang, WANG Guo-cheng

2025, 46(6):  753-767.  doi:10.3969/j.issn.1000-6362.2025.06.002

Asbtract ( 5 )   PDF (13405KB) ( 5 )  

Related Articles | Metrics

Grasslands are the largest terrestrial ecosystem globally, with approximately 90% of their carbon stored as soil organic carbon (SOC), making them highly sensitive to climate change. Despite this, large−scale and high−resolution quantifications of SOC changes across the entire soil profile (0–100cm) under warming scenarios remain limited. To address this, this study employed a data−model fusion approach to quantify potential changes in SOC across the entire soil profile of natural grasslands in Northern China under a 1.5℃ warming scenario at a 1km spatial resolution. Results showed that warming could lead to an average decrease of 3.63%–4.22% in SOC density across the soil profile, equivalent to a soil carbon stock loss of 0.78 to 1.52Pg C (1Pg=10¹⁵g). However, these estimated carry substantial uncertainty, primarily due to limitations in input datasets and model representation of grassland productivity dynamics. Notably, projections varied significantly among different soil datasets, with SoilGrids250m projecting the largest SOC stock reduction (1.52Pg C, 95% confidence interval: 1.17–1.91Pg C), followed by WISE30sec (0.82Pg C, 95% confidence interval: 0.62–1.04Pg C) and GSDE (0.78Pg C, 95% confidence interval: 0.57–1.04Pg C). These findings underscored the potential negative impacts of global warming on grassland soil carbon storage, emphasizing the necessity of enhancing grassland ecosystem conservation and restoration efforts to ensure the sustainable use and development of these vital ecosystems.




Effects of Planting Structure Adjustment from Perspective of Water Footprint of Grain Production in Yulin City

HE Lin-sen, ZHANG Yi-kun, WANG Yong-sheng, YIN Fang

2025, 46(6):  768-780.  doi:10.3969/j.issn.1000-6362.2025.06.003

Asbtract ( 5 )   PDF (3247KB) ( 39 )  

Related Articles | Metrics

The water footprint of grain production is an effective method for assessing the regional agricultural water resource situation. As a typical ecologically fragile area in the northwest region of China, Yulin city had been facing severe competition for water use among industry, agriculture, and residents, with intensified production, living, and ecology water resource pressures. Analyzing the effects of crop structure adjustment in Yulin city from the perspective of the water footprint of grain production is crucial for achieving efficient integration of regional water resources and agricultural production. In this study, the authors employed the food security index and crop water footprint theory to quantify the crop structure and water use in Yulin city from 2000 to 2020 with the perspective of the production water footprint. Additionally, an agricultural benefit evaluation system was developed using the entropy weight method to analyze the regional grain yield and water-saving benefits. The results indicated that (1) from 2000 to 2020, the planting structure of grain crops in Yulin city underwent significant changes, with an increase in the sowing area of corn and tubers becoming the dominant crops. The total output of major crops’ significantly increased from 55.89×10⁴t to 212.01×10⁴t, and the grain self-sufficiency rate also increased from 4.29% to 13.74%. (2) The blue-water footprint slightly increased during the grain crop planting structure adjustment. During the study period, the water footprint per unit mass of major grain crops had decreased, with the lowest blue water footprint per unit mass of corn at 0.21m³·kg⁻¹. At the county level, the blue-to-green water ratio was predominantly characterized by blue water being no more than green water. (3) During the study period, the grain yield, water-saving, and comprehensive benefits of agriculture had significantly increased. The comprehensive agricultural benefits had improved, with the northern six counties generally experiencing higher benefits than the southern six counties. The authors found that the planting structure adjustment had effectively ensured regional food security, with a small increase in the blue water footprint, sustaining the security of regional groundwater and surface water resources. This adjustment optimized regional water resource utilization while enhancing the benefits of grain yield and water savings. To ensure the coupling and sustainable use of cultivated land and water resources in the future, it is essential to improve cultivated land quality by focusing on “land use structure, water use structure, and planting structure”. Additionally, it is crucial to promote drip irrigation, sprinkler irrigation, and cultivating drought-resistant crops in the northern six counties, while developing “four-in-one” rainwater replenishment irrigation in the southern six counties, combined with biological coverage measures to enhance the soil's water retention and moisture retention capacity. 

Comparative Simulation of Six Machine Learning Algorithms on Soil Profile Temperature in Naqu, Xizang

XU Jun-jie, YU Yi-lei, YANG Li-hu, LI Wen-yan, LV Cui-cui, WEI Xin

2025, 46(6):  781-791.  doi:10.3969/j.issn.1000-6362.2025.06.004

Asbtract ( 4 )   PDF (3376KB) ( 1 )  

Related Articles | Metrics

 Research on soil temperature prediction mainly focuses on the surface layer, while relatively few studies on deep soil temperature, especially in high−altitude areas where relevant meteorological data are difficult to obtain. Authors employed six machine learning algorithms, which included the traditional Random Forest (RF), Particle Swarm Optimization - based Random Forest (PSO-RF), Ant Colony Optimization - based Random Forest (Ant-RF), as well as three neural network methods [Radial Basis Function (RBF), Backpropagation (BP), and Extreme Learning Machine (ELM)] to predict soil temperature at seven depths (0cm, 10cm, 20cm, 30cm, 40cm, 50cm and 60cm). Meanwhile, comparison of different machine learning algorithms in predicting soil temperatures at different depths were performed. The application of these algorithms in predicting soil temperature at different depths was compared using a dataset of soil temperature and meteorological data from Naqu city (Xizang) from 2017 to 2019. Then, these data were used as input variables for the models, which included temperature, humidity, cumulative solar radiation, precipitation and atmospheric pressure. At the same time, Taylor diagrams were used for model evaluation. The results showed that soil temperatures in the shallow layers changed dramatically, directly influenced by atmospheric temperature and solar radiation, while deep soil temperatures changed more steadily, with certain thermal insulation properties and obvious seasonal fluctuation characteristics. Comparing different models, it was found that in the prediction of soil temperatures at multiple depths, the RBF (radial basis function) neural network model demonstrated higher accuracy in prediction precision, stability and generalization ability, with R² ranging from 0.9016 to 0.9904 and MSE (mean square error) between 0.2501 and 2.7725 °C, achieving the highest accuracy at a depth of 50cm, with R² reaching 0.9904. The model performed best at a 50cm depth, where R² reached 0.9904. Afterwards, the RF model followed, with R² ranging from 0.8861 to 0.9381. Therefore, the RBF model could more accurately capture the complex relationships between soil temperature and various influencing factors, including meteorological conditions and soil depth. This study provides a more reliable and accurate tool for predicting soil temperatures at different depths, thereby offering important scientific basis for fields such as agricultural management, environmental protection, and climate change research.

Estimation of Water Conservation and Analysis of Influencing Factors in Shiyang River Basin Based on FLUS-InVEST Model

HOU Hui-min, REN Zhi-wei, WANG Wan-zhen

2025, 46(6):  796-807.  doi:10.3969/j.issn.1000-6362.2025.06.005

Asbtract ( 5 )   PDF (8737KB) ( 1 )  

Related Articles | Metrics

Based on multi−source data such as land use data, meteorological data and soil type space, this study used the FLUS model to estimate the spatial pattern change of land use in the Shiyang river basin under the scenarios of natural development, ecological protection and cultivated land protection in 2035, and coupled with the InVEST model to simulate the water conservation under the three scenarios. Compared with the water conservation in the Shiyang river basin in 2020, the spatial and temporal distribution and influencing factors of water conservation in the Shiyang river basin under the three scenarios were deeply explored, in order to solve the problems of non−standard water resources management. The results showed that: (1) the projected water conservation Shiyang river basin under the scenarios of natural development, ecological protection, and cultivated land protection in 2035 would be 5.25×10⁸m³, 5.28×10⁸m³ and 5.33×10⁸m³, respectively. (2) According to the analysis of different county−level administrative regions, the water conservation capacity of Sunan county in 2035 would increase the most under the scenarios of natural development, ecological protection and cultivated land protection, with an increase of 24.53%, 27.03% and 24.55%, respectively. Gulang county's water capacity would be reduced by 30.74%, 15.38% and 29.72% compared with 2020. According to the analysis of different land use types, the total water conservation in the Shiyang river basin under the scenarios of natural development, ecological protection and cultivated land protection in 2035 would be as follows: grassland>unused land>forest>farmland>shrub>construction land. (3) The average temperature in 2020 was the leading factor that affects the water sources of the Shiyang river basin. The average temperature had the strongest interpretation of the interaction with the NDVI, reaching 0.764. Social factor total population, GDP and natural factor (average temperature, digital high, precipitation and slope) interaction were enhanced by dual−factor.

Numerical Simulation Model of Vertical Water Temperature Distribution in Shallow Water Pond

CHENG Lu, MU Hai-zhen, ZHANG Jia-ting, ZHU Yin-qi

2025, 46(6):  808-815.  doi:10.3969/j.issn.1000-6362.2025.06.006

Asbtract ( 3 )   PDF (2322KB) ( 1 )  

Related Articles | Metrics

As one of the most important factors affecting aquaculture activities, a numerical model for water temperature simulation has been developed to accurately describe the pattern of temperature variation in shallow water bodies. The model was based on the one-dimensional heat diffusion equation, which considered factors such as water surface heat exchange, shortwave radiation absorption within the water body, and thermal convection. The boundary conditions were based on the assumption of surface heat balance and no heat exchange at water body bottom. Meteorological factors such as temperature, relative humidity, wind speed, cloud cover and solar radiation were used as input parameters. The simulation model was solved using explicit integration scheme. A simulation experiment was conducted from June 21 to 29, 2023, after calibrating the model parameters with observed water temperature in the fish pond of the Shanghai Jufu Aquaculture Professional Cooperative in Qingpu district, Shanghai. This model demonstrated strong capacity for water temperature simulation in shallow water ponds by addressing the trends and magnitude of water temperature variations at all levels. Through comparison with observation data, the simulated water temperatures were significantly correlated with the observed values, with correlation coefficients exceeding 0.8 at all levels and root mean square errors 0.8℃ to 1.1℃. The model is capable of generating water temperature simulation outputs for specified periods and depths, tailored to user requirements. Such outputs hold promising prospects for broader application, offering detailed guidance that can significantly enhance aquaculture activities.

Input Threshold of Manure Cadmium and the Cumulative Danger of Cadmium in Greenhouse Vegetable Soil in the Beijing-Tianjin-Hebei Region

BU Yan-ting, ZHANG Nan, LI Jian-zhong, WEN Jiong, LI Zuo-lin, LIU Wei, HE Chao, ZENG Xi-bai, SU Shi-ming

2025, 46(6):  816-826.  doi:10.3969/j.issn.1000-6362.2025.06.007

Asbtract ( 6 )   PDF (680KB) ( 1 )  

Related Articles | Metrics

The accumulation of heavy metals in soil from vegetable growing greenhouses significantly affects the ecological environment and the safety of agricultural products. In order to clarify the input threshold for curbing the excessive accumulation of heavy metals in the soil of greenhouse agriculture, through field survey sampling and literature data collection, the present study analyzed the content status of six heavy metals in greenhouse vegetable soils and manure in Beijing−Tianjin−Hebei region in 2023: chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb). This study attempted to set an input threshold for Cd for the common cultivation of leafy fruit and vegetables fertilized with manure to reduce heavy metal accumulation during greenhouse vegetable production. Results showed that : (1) the average values for Cr, Ni, Cu, Zn, Cd and Pb heavy metals in the greenhouse vegetable soil in Beijing−Tianjin−Hebei region were 74.4, 26.6, 26.8, 97.7, 0.3 and 21.3mg·kg⁻¹, respectively, all below the background value stipulated by national standards (GB 15618−1995). Heavy Metal Soil Accumulation Index indicates that if the index value was more than 1, 61.4% of the sample points were measured as exceeding the predetermined background value for Cd. When the index value was more than 2, then 10.0% of the sample points used in data collection exceeded the background Cd value, which was significantly higher than that for the other heavy metals. (2) The Cd content did not exceed the corresponding standard value, while the heavy metal contents of Cr, Ni, Cu, Zn, and Pb in the manure of soil used for greenhouse vegetable planting were above the heavy metals standards of the agricultural industry standard (NY 525−2021). (3) From 2005 to 2025, the study for the fertilizer application of Cd for greenhouse vegetables in the Beijing-Tianjin-Hebei region showed that the Cd level decreased from 8.2mg·kg⁻¹ to 0.5mg·kg⁻¹with the increasing number of planting years, indicating that there was reasonable control of Cd content in the manure. When applying 15000kg·hm⁻² of manure, the thresholds of heavy metals Cd in the manure for control purposes were found to be 0.240 and 0.378mg·kg⁻¹, respectively, through input and output of heavy metals in leafy vegetables for Chinese cabbage and sesame leaf in the Beijing−Tianjin−Hebei region. The threshold for input of Cd heavy metals for fruit vegetables, represented by cucumbers, based on environmental capacity control, varied with manure application. The standards for input Cd based on soil pollution levels were set at 0.60, 0.40, and 0.24mg·kg⁻¹ for 20y, 30y and 50y of manure application, respectively, after 15000kg·ha⁻¹ of manure. These findings provide critical reference values to regulate sources of heavy metals in soils for greenhouse vegetables and would also assist in developing effective strategies for pollution prevention in the Beijing−Tianjin−Hebei region.

Spatial-temporal Characteristics of Northeast Cold Vortex-type Chilling Injury and Its Impact on Rice Yield in Heilongjiang Province

PANG Yun-chao, WANG Qiu-jing, FANG Ming-ming, CHU Chun-yan, SHI Mu-zhen, LI Xiu-fen, ZHU Hai-xia, JIANG Li-xia

2025, 46(6):  827-838.  doi:10.3969/j.issn.1000-6362.2025.06.008

Asbtract ( 4 )   PDF (4307KB) ( 1 )  

Related Articles | Metrics

 Clarifying the spatiotemporal characteristics of cold vortex−induced chilling injury during the booting stage of cold−region rice and the resulting yield losses, as well as analyzing the influence of cold vortex−type weather on rice yield formation, can provide meteorological references for the high−quality and safe production of cold−region rice. Using daily meteorological data and rice yield data from 65 meteorological stations in the rice−growing area of Heilongjiang province from 1964 to 2021, and based on the national standards of the People's Republic of China, authors identified obstacle−type low−temperature cold damage (referred to as cold vortex−type cold damage) caused by persistent northeast cold vortex activity during the rice booting stage. Using mathematical statistical methods, the occurrence patterns and spatiotemporal evolution characteristics of cold vortex−type chilling injury were analyzed and constructed a model to assess yield losses of rice due to cold vortex-type chilling injury. The results showed that a total of 223 occurrences of cold vortex-type chilling injury were recorded in the study area, with peak periods in the 1980s and 2000s. Spatially, there were significant regional differences, with more occurrences in the north and east compared to the south and west. At stations experiencing mild chilling injury, rice yield losses ranged from 3.4% to 9.2%, while moderate and severe chilling injury led to yield losses of 7.9% to 16.0% and 21.1% to 27.6%, respectively. This demonstrates that the greater the severity of cold vortex−induced chilling damage, the higher the yield reduction. The negative accumulated temperature (the accumulated temperature sums during the booting stage of rice, when the daily average temperature fell below the critical growth temperature) during the cold−vortex−induced chilling injury period was significantly correlated with the relative meteorological yield of rice (P<0.01). Within a specific range of negative accumulated temperature (−7.3℃·d to −0.6℃·d), with each 1℃·d decrease in negative accumulated temperature reducing relative meteorological yield by approximately 2.0, 4.4 and 3.5 percentage points in the western, central, and eastern regions, respectively. Overall, the greater the accumulation of negative temperature sums caused by cold vortex−type chilling injury, the lower the relative meteorological yield of rice.

Prediction of Tea Picking Date in Different Main Production Areas of Hubei Province

JU Ying-qin, CHEN Zheng-hong, MA De-li, HUANG Zhi-yong, CHEN Xiao-xiao, WANG Ying-qiong, LUO Jiang-mei, MENG Fang, ZHAO Ya-jing

2025, 46(6):  839-851.  doi:10.3969/j.issn.1000-6362.2025.06.009

Asbtract ( 4 )   PDF (959KB) ( 1 )  

Related Articles | Metrics

Based on tea picking date and meteorological data from three main tea producing areas in Hubei province from 2010 to 2022, significance analysis and correlation analysis methods was used to explore the impact of meteorological conditions on tea picking date. A prediction model of tea picking date for first round tea, second round tea, and third round tea was established, basing on meteorological factors. The results showed that: (1) the first, second and third round of tea in Hubei province entered the pluck starting date on March 30^th, May 17^th and June 27^th, respectively. There was a delayed trend with interannual variation. (2) The average temperature during the growing period of the first round of tea was Xian'an＞Yingshan＞Yiling. In mid−March, the average temperature remained stable at 10℃, and the tea tree's nutrient buds went through winter dormancy, accumulating a lot of nutrients. The spring shoots sprouted, reaching the conditions for the first round of tea picking. The average temperature during the growth period of the second round of tea was around 20℃. The average temperature during the growth period of third round of tea was around 25℃. The precipitation was almost above 100mm per month. The relative humidity was between 70%−85%. The sunshine hours were within a certain range. These conditions met the growth and development of tea trees. The first round of tea picking in Yiling was easily affected by spring frost. The picking date of Yingshan and Xian'an second and third round tea was greatly affected by heavy rainfall during the plum rain period. (3) The multiple linear regression model was used to predict the picking date of tea, and the historical data were used for verification. The results showed that the model had a prediction error of about 4 days for the first round of tea picking date, which was better than that of the second and third round of tea. At the same time, the accuracy of the model's forecasting time increased as the picking date approaches. Due to the impact of rare high temperatures and droughts in 2022, the model's predictive performance in the second and third round of tea picking date was poor. 

Identification of Heat Tolerance and Adaptability for Cold Region Early Japonica Rice under Natural Extreme High Temperature in South

LIU Jin, HU Jia-xiao, TU Hang, MENG Bing-xin, ZHOU Hui-ying, LI Mao-mao, YU Li-qin

2025, 46(6):  852-861.  doi:10.3969/j.issn.1000-6362.2025.06.010

Asbtract ( 3 )   PDF (1997KB) ( 1 )  

Related Articles | Metrics

A total of 556 cold−region early Japonica rice varieties from Heilongjiang, Jilin, Liaoning province and Japan were introduced, the field experiments with two sowing dates were conducted in Jiangxi province, where heat tolerance and adaptability were assessed under extreme natural high−temperature conditions, which comprehensively evaluated the high temperature resistance, thereby screening out heat tolerant Japonica rice and provided excellent germplasm resources for Japonica breeding in high−temperature areas. Research indicated that under high−temperature conditions, grain number, filled grain number, spikelet fertility, heat tolerance class, adaptability and high temperature resistance grade of cold−region early Japonica rice had a variation change, among these varieties from different geographical regions, those from Liaoning and Heilongjiang Japonica rice stood out with notably higher grian number and spikelet fertilit compared to others, meanwhile, Liaoning Japonica rice showed the strongest heat adaptability and comprehensive heat tolerance. A total of 30 excellent heat tolerance and adaptability Japonica rice varieties were selected, 13, 2, 11 and 4 varieties were from Heilongjiang, Jilin, Liaoning province and Japan, accounting for 5.26%, 2.94%, 8.53% and 3.60% of the tested material from every geographical origin, respectively. Among these, Tongyu267, Songjing4, Longgrain line, Yanjing34 and Yanfeng47 exhibited the strongest heat tolerance and adaptability, which can be used as the key parents of Japonica rice breeding in high−temperature regions. 

Construction of Gannan Navel Orange Yield Simulation Model Based on Meteorological Factors

LI Ying-chun, LI Xiang-xiang, XIE Yuan-yu, YANG Jun

2025, 46(6):  862-871.  doi:10.3969/j.issn.1000-6362.2025.06.011

Asbtract ( 4 )   PDF (593KB) ( 1 )  

Related Articles | Metrics

In this study, the navel orange yield and the meteorological data from Ganzhou were collected for the period from 2000 to 2022, and the corresponding meteorological yield was separated. The key meteorological factors affecting meteorological yield were identified by fitting relationships between meteorological yield and meteorological factors at five growth stages. Finally, the relative meteorological yield model based on key meteorological factors was constructed using multiple linear regression method, and the model was validated to determine its reliability, stability and accuracy. The results indicated that: (1) the exponential smoothing method with a given weight of 0.8 was more reasonable for separating the meteorological production of navel oranges in Ganzhou from 2000 to 2022. (2) The key meteorological factors affecting the yield of navel oranges included precipitation during the overwintering period, average temperature during the budding and flowering period, average temperature during the young fruit growth period, precipitation during the fruit swelling period, and sunshine hours during the coloring and ripening period. (3) Two yield simulation models were constructed based on the key meteorology factors from December 1 to September 30 of the following year (i.e., overwintering to the end of fruit swelling) and December 1 to November 30 of the following year (i.e., overwintering to coloring and ripening), respectively. Both the model passed the 0.01 level of significance, with the relative error of 5.52% and 5.31%, and the root mean squared error of 604.85kg·ha⁻¹ and 614.86kg·ha⁻¹, respectively. The model validation accuracy from 2018 to 2022 was 97.88% and 97.84%, respectively. Overall, the two simulation models are suitable for simulating navel oranges yield in southern Jiangxi.

Spatiotemporal Variation Characteristics of Climate Potential Production in Guizhou Province

REN Qing-feng, WENG Ling, GU Kun, WU Xin-hao, XIE Qiang, LUO Hai-shun

2025, 46(6):  872-882.  doi:10.3969/j.issn.1000-6362.2025.06.012

Asbtract ( 4 )   PDF (6268KB) ( 3 )  

Related Articles | Metrics

This study investigated the temporal and spatial variability characteristics of climate potential production in Guizhou province by utilizing data from 79 national meteorological stations (1961−2023) combined with the Miami and Thornthwaite Memorial models. The findings provided valuable references for optimizing climate resource utilization and guiding scientific agricultural production planning. The results indicated that: (1) from 1961 to 2023, Guizhou province experienced significant abrupt changes in temperature, precipitation and standard climate potential production, with substantial spatial distribution disparities. The temperature increased at a rate of 0.15°C·10y⁻¹ (P < 0.01), with 91.1% of the stations showed an increasing trend (P<0.05). After an abrupt change in 2005, the mean temperature rose by 0.6℃. Precipitation exhibited no significant overall trend, 8.9% of the stations demonstrated a decreasing tendency (P<0.05). After an abrupt change in 2022, the precipitation reduction 207.1mm. The standard climate potential production also showed no significant overall trend, but 12.7% of the stations displayed an increasing trend (P<0.05), while 1.3% exhibited a decreasing trend (P<0.05). After an abrupt change in 2012, the average standard climate potential production increased by 280.8kg·ha⁻¹. (2) From 1961 to 2023, the water heat ratio of climate potential production across Guizhou province remained below 1, while 1.3% of the stations showed an increasing trend (P<0.05), 34.2% exhibited a decreasing trend (P<0.05). Projections suggested an increased imbalance in the water−to−heat ratio of future climate potential production. (3) From 1961 to 2023, the sensitivity coefficients of standard climate potential production to temperature and precipitation changes in Guizhou were 422.0kg·ha⁻¹·°C⁻¹ and 4.0kg·ha⁻¹·mm⁻¹. Precipitation had a more dominant effect on the production of the standard climate potential production than temperature. Given the projected significant warming trend in Guizhou, enhanced attention should be paid to the impact of precipitation variability. 




Hierarchical Model Predictive Control of Greenhouse Environment Based on Energy Consumption and Cost Optimization

REN Zhi-ling, JIANG Qing, DONG Yun

2025, 46(6):  883-894.  doi:10.3969/j.issn.1000-6362.2025.06.013

Asbtract ( 5 )   PDF (5225KB) ( 1 )  

Related Articles | Metrics

In order to improve the energy efficiency and reduce the production cost of Venlo greenhouse, a Hierarchical model predictive control (HMPC) method including optimization layer and control layer was proposed. The optimization layer aimed to minimize the energy consumption and the total operating cost at the time of using price respectively, and construct two strategies to increase the energy utilization rate and reduce the greenhouse cost. Sensitivity analysis was carried out to study the impact of electricity price, CO₂ price and the range of environmental condition constraint on cost optimization. In the control layer, Model predictive control (MPC) was used to solve the model object mismatch and suppress the system disturbance. The relative average deviation and maximum relative deviation were used to compare and analyze the tracking performance of MPC and open−loop control under 2%, 6% and 12% perturbations, to obtain a more stable and reliable greenhouse control system. The results showed that the constraints on temperature and average relative humidity had a significant impact on reducing the operating cost of the greenhouse. The total energy consumption in the lowest energy consumption scenario was 79.92% of that in the lowest cost scenario. The total cost of the lowest cost scenario was 83.61% of the lowest energy consumption scenario. The MPC had a good tracking performance under different systematic perturbation. Greenhouse hierarchical model predictive control can effectively improve greenhouse control accuracy, increase greenhouse energy efficiency and reduce production costs. 

Research Progress on the Extraction Methods of Agricultural Irrigation Information Based on Optical Remote Sensing Data

LI Dong-yu, WANG Pei-juan, LI Yang, WANG Qi, MA Yu-ping

2025, 46(6):  895-906.  doi:10.3969/j.issn.1000-6362.2025.06.014

Asbtract ( 5 )   PDF (360KB) ( 1 )  

Related Articles | Metrics

 Irrigation plays an important role in farmland management and timely and accurate access to irrigation information is important for modern agricultural production. With the continuous development of remote sensing technologies, optical remote sensing has surpassed traditional field measurement methods, which are known for their low quality and efficiency, and has found wide application. In this paper, the principles of inversing agricultural irrigation information using the vegetation indeices, soil moisture, and evapotranspiration methods based on optical remote sensing data were outlined, and the advantages and disadvantages of each method and their development trends were summarized. The results indicated that as research expands from small irrigation districts to larger provincial and national regions, the types of irrigation information required become more diverse, researchers were continuously optimizing existing methods to address their shortcomings, leading to the maturation of research techniques. Integration of multi-parameter inversion methodologies and machine learning algorithms could effectively improve the precision of agricultural irrigation information extraction, which was also the leading trend in optical remote sensing data extraction. This approach representd two major trends in the extraction of agricultural irrigation information based on optical remote sensing data. However, challenges such as time lags and limited penetration still remain. Future research should focus on developing models suitable for different spatial and temporal scales by utilizing the integration of multi-parameter inversion methodologies and machine learning algorithms, and should aim to deepen the understanding of underlying mechanisms and continuously improve the precision of agricultural irrigation information extraction from optical remote sensing data.