Agricultural Regions Research Articles

Aquifer system deformation in the San Luis Valley: A new framework for modeling subsidence in agricultural regions

The San Luis Valley (SLV), Colorado, is challenged with implementing sustainable groundwater management in the face of increasing surface water scarcity due to climate change. Groundwater extraction in unconsolidated aquifers such as the SLV can cause cm-scale subsidence and rebound. This study utilizes Interferometric Synthetic Aperture Radar (InSAR) data, validated by Global Navigation Satellite System (GNSS) measurements, to measure subsidence and analyze the groundwater dynamics that cause it. Addressing the challenges of phase decorrelation and data gaps, notably from September 2018 to April 2019, we adopted a modified DS-interpolation algorithm, alongside a Singular Spectrum Analysis (SSA)-based gap filling technique. Furthermore, we enhanced the temporal resolution of groundwater level data through the Theis curve interpolation. These methodologies enabled the integration of observational well data with satellite measurements to calibrate a one-dimensional deformation model, capturing both the elastic and inelastic responses of the aquifer system. Our investigation, spanning 2015 to 2021, reveals both seasonal and long-term subsidence, with the confined aquifer section experiencing up to 1 cm/year of subsidence alongside notable seasonal fluctuations. The methodology presented here provides a path to model subsidence in regions with sparse groundwater level and noisy InSAR data. It also provides valuable insights for developing effective water management strategies in the SLV.

Associations between Pesticide Metabolites and Inflammatory Biomarkers in Adolescents and Children Living in an Agricultural Region

Associations between Pesticide Metabolites and Inflammatory Biomarkers in Adolescents and Children Living in an Agricultural Region

Photolysis of dinotefuran and nitenpyram in water and ice phase: Influence mechanism of temperature over photolysis

Neonicotinoids are widely used pesticides around the world, but the photolysis of neonicotinoids in cold agricultural region are still in blank. This paper aimed to study the influence of cold temperature over photolysis of neonicotinoids. To this end, the photolysis rates and photoproducts of dinotefuran and nitenpyram in water, ice and freeze-thawing condition were determined. Coupled with quantum chemistry calculation, the influence mechanisms of temperature and medium were investigated. The results showed the photolysis rates of neonicotinoids in water condition slightly declined with the lowered temperature due to the photolysis reactions were endothermic reactions. However, the photolysis rates increased by 89.8 %, 59.2 %, 49.4 % and 9.5 % for dinotefuran and nitenpyram in ice and thawing condition, respectively. This phenomenon was posed by the concentration-enhancing effect and change of photo-chemical properties of neonicotinoids in ice condition, which included lowered bond cleavage energy, lowered first excited singlet state energy and expanded light absorption range. The photolysis pathways of the two neonicotinoids did not change in different medium, but the concentration of carboxyl products was relatively higher than that of water condition due to the more amounts of reactive oxygen species in ice medium, which might increase the secondary pollution risk after ice-off in spring due to the higher ecotoxicity to nontarget organism of these photoproducts. The influence of cold temperature and medium change should be considered for the environmental fate and risk assessment of neonicotinoids in cold agricultural region.

Integrating machine learning and change detection for enhanced crop disease forecasting in rice farming: A multi-regional study

Crop diseases are increasingly causing devastating yield losses each year, posing a significant threat to global food security. Currently, fungicide treatment is one of the most effective measures to mitigate these disease-induced yield losses. Accurately predicting disease occurrence, onset timing, and development is critical for optimizing fungicide application schedules to achieve high efficacy. In this study, we compiled weekly disease observations, crop management, and weather data from 207 locations across China, India, and Japan. We compared six machine learning (ML) models for their performance in simulating disease severity. By mimicking disease development curves and trends at early stages, we developed a novel change detection (CD) model, named rolling linear regression (RLR), and combined it with the best-performing ML model to predict the occurrence, severity, and onset dates of four major rice diseases: leaf blast (Magnaporthe oryzae), panicle-neck blast (Magnaporthe oryzae), sheath blight (Rhizoctonia solani), and false smut (Ustilaginoidea virens). Our findings showed that the RandomForestRegressor demonstrated the highest performance in simulating disease severity, with mean absolute errors (MAE) below 0.5 % and root mean square errors (RMSE) below 2.5 %. The RLR model showed a distinct advantage over the other four widely used CD models based on five evaluation metrics. Additionally, the paired RandomForestRegressor+RLR model achieved the highest F1-score, ranging from 0.7 to 0.8, in predicting disease occurrence, outperforming 29 other ML+CD model pairs. Furthermore, the RandomForestRegressor+RLR model predicted onset dates with fewer than 6 error days and accuracies ranging from 74 % to 87 %. Combining ML with CD models not only shows robust generalization across diverse environmental conditions but also proves highly effective for large-scale disease risk forecasting in rice farming regions. The adaptability of ML techniques, when sufficient training data are available, particularly enhances decision support systems aimed at optimizing rice disease management practices for growers across various regions. Our hybrid model thus presents a compelling advancement in the precision agriculture domain, with significant implications for improving disease management strategies for crops beyond rice through timely intervention. This approach can contribute to safeguarding global food security by reducing crop losses due to disease damage.

Spatiotemporal variations and priority ranking of emerging contaminants in nanwan reservoir: A case study from the agricultural region in huaihe river basin in China

The presence of emerging contaminants (ECs) in drinking water sources is an increasing concern, yet limited data exists on their occurrence and risk in the upper Huaihe River Basin, an important agricultural region in Central China. This study investigated 70 ECs, including pesticide and antibiotics in surface water from drinking water source areas in Nanwan Reservoir along the upper reaches of the Huaihe River Basin to prioritize the ECs based on ecological risk and health risk assessment. A total of 66 ECs were detected in the surface water at least once at the selected 38 sampling sites, with concentrations ranging from 0.04 to 2508 ng/L. Ecological risk assessment using the risk quotient (RQ) method revealed high risks (RQ > 1) from 7 ECs in the dry season and 15 ECs in the wet season, with triazine pesticides as the main contributors. Non-carcinogenic risks were below negligible levels, but carcinogenic risks from neonicotinoid and carbamate pesticides and macrolide antibiotics were concerning for teenagers. Ciprofloxacin exhibited a high level of resistance risk during the wet season. A multi-indicator prioritization approach integrating occurrence, risk, and chemical property data ranked 6 pesticides and 3 antibiotics as priority pollutants. The results highlight EC contamination of drinking water sources in this agriculturally-intensive region and the need for targeted monitoring and management to protect water quality.

Soybean production in SEALBA: inoculation, co-inoculation, and starter nitrogen fertilization

The grain production area in the Brazilian agricultural region known as SEALBA, especially in Alagoas State, has seen significant growth in recent years. This study aims to contribute to sustainable regional development by evaluating the effects of inoculation, co-inoculation, and the application of starter mineral nitrogen (N), either individually or in combination, on soybean growth and yield. Eight field experiments were conducted from 2018 to 2021 in two municipalities within Alagoas, covering soybean cultivation areas ranging from the first to the fourth year. The experiments followed a randomized complete block design with four replications, employing a factorial arrangement of treatments. The first factor encompassed five levels related to inoculation technologies and N management: 1) Inoculation with Bradyrhizobium; 2) Inoculation with Bradyrhizobium combined with N basal fertilization; 3) Co-inoculation of Bradyrhizobium + Azospirillum; 4) Co-inoculation of Bradyrhizobium + Azospirillum + N fertilization at the base; and 5) Control treatment without microbiological inputs or nitrogen fertilizers. The second factor involved the evaluation of different soybean cultivars, with two materials assessed in 2018 and three cultivars in 2019, 2020, and 2021. Results demonstrated that seed inoculation with Bradyrhizobium led to increased soybean grain yield in first-year cultivation areas. However, N basal fertilization, commonly known as starter fertilization, did not result in yield improvements compared to the use of microbiological inputs alone. Soybean cultivars BRS 9383 IPRO and FTR 3191 IPRO exhibited greater responsiveness to seed inoculation with Bradyrhizobium.

Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China.

As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by -3.92% to -4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

Performance of Zero Tillage Safflower (Carthamus tinctorius L.) under Different Irrigation Schedules and Nutrient Levels in Northern Telangana Zone (NTZ)

An experiment was carried out at Regional Agricultural Research Station, Polasa, Jagtial in Northern Telangana Zone during rabi (winter season), 2023-2024 where the performance of zero tillage safflower (Carthamus tinctorius L.) was tested under different irrigation schedules and nutrient levels. The experiment was executed using a factorial randomized block design with 15 treatment combinations, with three replications. The treatments consisted of two factors i.e., factor-1 included irrigation schedules-; I1: Irrigation at rosette and flower initiation stages, I2: Irrigation at rosette and seed development stages, I3: Irrigation at branching and flower initiation stages, I4: Irrigation at branching and seed development stages and I5: No irrigation (residual moisture) and factor-2 included nutrient levels, S1: 75% Recommended dose of fertilizer (RDF), S2: 100% RDF and S3: 125% RDF. The results revealed that irrigation at branching and seed development stages(I4) reported significantly highest seed (1199 kg ha-1), stalk yield (4724 kg ha-1) and yield attributing traits viz.; number of heads plant-1(22.5), number of seeds head-1(23.4), seed weight plant-1(19.8 g), 100-seed weight (7 g), whereas the lowest yield were observed with no irrigation (residual moisture) (I5) treatment. Among different nutrient levels, application of 125% RDF (S3) resulted in the highest seed (1105 kg ha-1) and stalk yield (4429 kg ha-1) and yield attributing traits viz.; number of heads plant-1(21.4), number of seeds head-1(22.6), seed weight plant-1(19.5 g), 100-seed weight (6.7 g) over 75% RDF (S1). Among different treatments, irrigation at the branching and seed development stages combined with the application of 125% RDF gave the best results.

Digital mapping of soil quality and salt-affected soil indicators for sustainable agriculture in the Nile Delta region

The study addresses the challenge of sustainable land management, which is crucial for agricultural production and soil quality (SQ), in the face of land degradation that negatively impacts crop production and SQ. The goal of the current work is to assess SQ using digital soil mapping (DSM) in Kafr El-Sheikh province, Egypt, to develop a framework employing two methods for soil quality index (SQI) assessment: the total data set (SQI-TDS) and a selected minimum data set (SQI-MDS) to choose indicators, along with a weighted additive SQI (SQIw), and a Random Forest (RF) model to predict and map the SQI, as well as the salt-affected soil indicators (EC, pH, and ESP). This framework uses remote sensing data: time series of Sentinel-1 (S-1) and Sentinel-2 (S-2) greenest pixel composite. Additionally, we incorporated environmental covariates derived from S-1 and S-2 imagery to understand their influence on SQ, which in turn informs land management practices, land degradation assessment, and crop productivity. The findings reveal a clear negative impact of salinity and alkalinity on SQ. We demonstrate the importance of Variance Inflation Factor (VIF) and Sequential Feature Selection (SFS) techniques for improving the performance of the RF model used for prediction. Notably, the greenest pixel composite imagery proved promising for SQI assessment using DSM beneath vegetation cover, crop mapping, and land-use dynamics. The precise SQI obtained is essential for decision-makers to detect land degradation, develop sustainable agricultural management strategies, and assess their appropriateness for developing plans and strategies to increase agricultural productivity.

Shallow Groundwater Around Plateau Lakes： Spatiotemporal Distributions of Dissolved Carbon and Its Driving Factors

Dissolved carbon in groundwater plays an important role in carbon cycling and ecological function maintenance, and its concentration level affects the migration and transformation of pollutants in groundwater. To understand the spatiotemporal variation characteristics of dissolved carbon and its driving factors in shallow groundwater around plateau lakes, variations in the concentrations of dissolved organic carbon （DOC）, inorganic carbon （DIC）, and total carbon （DTC） and their driving factors in shallow groundwater （n = 404） around eight plateau lakes were analyzed. The results indicated that the average values of ρ（DOC）, ρ（DIC）, and ρ（DTC） in shallow groundwater around plateau lakes were 8.23, 49.01, and 57.84 mg·L-1, respectively, with the ρ（DOC） in 79.0% of shallow groundwater samples exceeding 5 mg·L-1. There were no significant differences in the DOC, DIC, and DTC concentrations between rainy and dry seasons, whereas the change in dissolved carbon concentrations in shallow groundwater were strongly affected by the intensity of agricultural intensification and the depth of groundwater table； the DOC, DIC, and DTC concentrations in shallow groundwater from facility agricultural regions （SFAR）, cropland fallow agricultural regions （CFAR）, and intensive agricultural regions with deeper groundwater tables （DIAR） were significantly reduced by 25.8% - 56.6%, 14.0% - 32.9%, and 16.6% - 36.7%, respectively, compared with those in intensive agricultural regions with shallower groundwater tables （SIAR）. Additionally, the dissolved carbon concentrations in shallow groundwater from DIAR were significantly lower than those of SFAR and CFAR. RDA revealed that physicochemical factors in water and soil significantly explained the changes in the dissolved carbon concentrations. Moreover, the dissolved carbon concentrations in shallow groundwater around Yilong Lake were significantly higher than those of other lakes, whereas that of Chenghai Lake was significantly lower than that of other lakes. Our study highlights that agricultural intensification intensity and groundwater table depth jointly drove the variations in dissolved carbon concentrations in shallow groundwater around plateau lakes. The study results are expected to provide a scientific basis for understanding the carbon cycle in plateau lake areas with underground runoff flowing into lakes and evaluating the attenuation of pollutants by dissolved carbon in shallow groundwater.

Do agricultural credit, wheat, and rice production impact environmental quality? Novel evidence from China’s mega agricultural regions

Do agricultural credit, wheat, and rice production impact environmental quality? Novel evidence from China’s mega agricultural regions

Enhancing Soil Conditions and Maize Yield Efficiency through Rational Conservation Tillage in Aeolian Semi-Arid Regions: A TOPSIS Analysis

Conservation tillage technology possesses substantial potential to enhance agricultural production efficiency and tackle issues such as wind erosion and land degradation in semi-arid regions. The integration of no-tillage and straw mulching technologies in the conventional aeolian semi-arid agricultural zones of western Liaoning, China, has led to notable improvements in crop yield and soil quality. However, a comprehensive assessment of the mechanisms and kinetics involved in soil nutrient variations is yet to be conducted. During a two-year study period, we assessed four tillage systems in the aeolian semi-arid regions of Northern China: no-tillage with full straw mulching (NTFS), no-tillage with half straw mulching (NTHS), no-tillage without straw mulching (NT), and conventional tillage (CT). The investigation focused on examining nutrient conditions, enhancing photosynthetic activity, and increasing maize yield while improving water use efficiency (WUE). Our findings emphasize the beneficial impact of combining no-tillage and straw mulching on enhancing soil water retention, resulting in a notable rise in soil moisture levels during the crucial growth phases of maize. This approach also positively influenced soil nutrient levels, particularly in the 0–20 cm layer, fostering an environment conducive to maize cultivation. In terms of ecological benefits, no-tillage with straw mulching curtailed soil sediment transport and wind erosion, notably at 30–40 cm heights, aiding in the ecological protection of the region. The yield and WUE were substantially higher under NTFS and NTHS than under CT, with NTHS demonstrating the most significant gains in yield (14.5% to 16.6%) and WUE (18.3% to 21.7%) throughout the study period. A TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analysis confirmed NTHS as the optimal treatment, achieving the highest scores for soil water, nutrient availability, wind erosion control, maize photosynthesis, yield, and WUE, thus emerging as the most effective conservation tillage strategy for sustainable agriculture in aeolian semi-arid regions.

ECONOMIC-STATISTICAL ANALYSIS OF THE DEVELOPMENT OF THE FRUIT AND VEGETABLE SECTOR IN UZBEKISTAN

This article describes the current state and dynamics of the agricultural network of our republic, an economic and statistical analysis of the production of agricultural products in economic entities and promising directions. The activities of farms operating in our country were also carefully analyzed, and with the help of statistical methods, the role and importance of this industry in the development of the industry was revealed. Dynamic changes in statistical indicators of network development over the years are shown. KEY WORDS: agriculture, agricultural products, gross domestic product, gross regional product, agriculture, livestock breeding, structural changes, investments, regional economy, economic and statistical analysis, agrarian reforms, socio-economic processes .

Assessment of Yield Losses due to Major Insect Pests in Sesame

Yield losses due to major insect pests viz., leaf webber and capsule borer Antigastra catalaunalis (Duponchel) and gall fly Asphondylia sesami (Felt) in promising sesame varieties was carried out at the Regional Agricultural Research Station, Jagtial, during summer, 2019 and 2020 under protected and unprotected conditions. The highest % avoidable yield losses was recorded in Hima variety (35.45) followed by Swetha, Sharada, GT 10, Madhavi, Gouri, VRI 1 and Rajeswari with 34.27, 33.17, 32.48, 31.05, 30.53, 30.01 and 29.75, respectively. Regarding cost benefit ratio, highest was recorded in Hima variety (1:2.23) followed by Swetha (1:2.05) and Sharada (1:1.75).

Fertility and ecotoxicological condition of light gray forest soils of the Ivanovo region

The results oflong-term monitoring of light gray forest soil for agricultural purposesin theIvanovo region are presented, which was carried outto establish the level of fertility according to the mainagrochemical indicators, the content of mobile forms of trace elementsand sulfur, the ecotoxicological state according to the content ofgross and mobile forms of heavy metals, arsenic, caesium-137 andstrontium-90.The average values of the indicators, their levels ofchange and trends in changes in the metabolic and hydrolyticacidity of the soil, availability of organic matter, mobile formsof nitrogen, phosphorus, potassium, exchangeable calcium, magnesium and other bases,mobile forms of boron, copper, zinc, molybdenum, cobalt, manganese andsulfur have been established.According to the content of traceelements in the soil, the needs for the use ofmicronutrients are determined.Changes, concentrations and trends in the mobileforms of cadmium, lead, copper, zinc, nickel, chromium, mercury andarsenic in the soil have been determined.The study establishedbackground values, changes and trends in the specific activities ofcaesium-137 and strontium-90, the density of their contamination of thesoil of the site and the power of the exposuredose of gamma radiation.According to thePearson–Spearman correlation coefficients,the peculiarities of the influence of the content of organicmatter, the level of acidity and the capacity of cationexchange on the content of mobile forms of trace elements,sulfur, gross and mobile forms of metals and radionuclides wereestablished.

Shallow groundwater table fluctuations weaken nitrogen accumulation in the thin layer vadose zone of cropland around plateau lakes, Southwest China

Excessive accumulation of nitrogen (N) in the soil profile in the intensive agricultural region will seriously threaten groundwater quality and safety. However, the impact of shallow groundwater table (SGWT) fluctuations driven by seasonal variations on the N accumulation characterizations in the soil profiles has not been well quantified, particularly in the regions with thin layer vadose zone. Through in-situ monitoring and simulation experiments, the changes in the SGWT and N accumulation of soil profile in intensive cropland around 7 plateau lakes in Yunnan were studied during the rainy season (RS) and dry season (DS), and the N loss in soil profile of cropland driven by SGWT fluctuations was estimated. The results showed that the SGWT and N accumulation in soil profile of cropland around the plateau lakes had obvious seasonal variation characteristics. The proportion of N storage in different forms in 60–100 cm soil layer in the RS was greater than that in the DS, particularly the proportion of NH4+-N storage was as high as 55 %, while N accumulation in surface soil was obvious in the DS. Compared with the DS, due to the rising SGWT in the RS, the maximum storages of TN and NO3−-N in the 0–100 cm soil layer decreased by17% and 36 %, respectively. The TN loss intensities from the 0–100 cm soil profiles of cropland around Fuxian Lake, Yilong Lake, Qilu Lake, Dianchi Lake, Yangzong Lake, Erhai Lake, and Xingyun Lake were 74, 54, 127, 105, 93, 72 and 207 kg/ha, respectively. Moreover, if the SGWT was <30 cm, the average TN loss intensity and amount could reach 177 kg/ha and 1250 t, respectively. Therefore, the SGWT regulation was one of the key measures to reducing soil N loss from the thin layer vadose zone of cropland around plateau lakes and improving groundwater quality.

A Review of Drip Irrigation’s Effect on Water, Carbon Fluxes, and Crop Growth in Farmland

The substantial depletion of freshwater reserves in many pivotal agricultural regions, attributable to the dual pressures of global climate change and the excessive extraction of water resources, has sparked considerable apprehension regarding the sustainability of future food and water security. Drip irrigation, as an efficient and precise irrigation method, reduces water loss caused by deep percolation, soil evaporation, and runoff by controlling the irrigation dosage and frequency, thus improving the efficiency of water resource utilization. Studies have shown that compared with traditional irrigation methods, drip irrigation can significantly decrease water consumption, optimize the water–energy relationship by reducing soil evaporation, increase the leaf area index, and promote crop growth, thereby enhancing plant transpiration. Although more wet and dry soil cycles from drip irrigation may increase soil CO2 emissions, it also enhances crop photosynthesis and improves crop net ecosystem productivity (NEP) by creating more favorable soil moisture conditions, indicating greater carbon sequestration potential. The advantages of drip irrigation, such as a short irrigation cycle, moderate soil moisture, and obvious dry and wet interfaces, can improve a crop’s leaf area index and biomass accumulation, improve root dynamics, promote the distribution of photosynthetic products to the aboveground parts, and thus enhance crop yields. This study highlights the potential for the application of drip irrigation in arid regions where resource optimization is sought, providing strong technical support for the achievement of sustainable agricultural development. Future research needs to consider specific agricultural practices, soil types, and environmental conditions to further optimize the implementation and effectiveness of drip irrigation.

Проблемы современного состояния сельского хозяйства Пермского края

The modern world food market is showing stable trends due to increasing demand for agricultural products. The article examines the state of agriculture in the Perm region, analyzes the structure of the livestock complex, the dynamics of sown areas and agricultural products in the context of livestock and crop production sectors.

Derivation of ecotoxicologically acceptable Cu concentrations in soil from different land uses in South Korea

This study aimed to establish ecotoxicologically acceptable Cu concentrations for soil-residing species by integrating the biotic ligand model and the species sensitivity distribution. Statistical analyses were performed on 35 soil solution samples collected from four distinct land use sites: residential, agricultural, forested, and industrial regions. The environmental parameters of these samples, including pH, dissolved organic carbon (DOC), Ca2⁺, Mg2⁺, K⁺, and Na⁺ concentrations, exhibited wide variations across the four regions. Specifically, pH and the concentrations of Mg2⁺, K⁺, and Na⁺ showed significant variability. Additionally, a strong correlation was observed between pH and Ca2⁺, as well as between the DOC concentration and Mg2⁺ and Na⁺. Using the biotic ligand model, we derived the half-maximal effective activities of Cu (EC50{Cu2+}) for 10 soil organisms based on the chemical compositions of the soil solution samples. Additionally, a species sensitivity distribution approach was employed to determine the 5% hazardous concentration (HC5) for soil biota, which was closely associated with DOC and Na⁺ concentrations, with Mg2⁺ playing a secondary role. We attributed these relationships to the formation of DOC complexes that mitigate Cu toxicity, along with competitive interactions with cations. Notably, HC5 values did not differ significantly across sampling sites (p = 0.523). Clustering based on environmental factors grouped the samples into four clusters, each containing soils from different land use types. However, the third cluster included an outlier from agricultural soil due to its unusually high pH and DOC levels. These findings suggest that it is crucial to consider site-specific soil characteristics when determining ecotoxicologically acceptable Cu concentrations, and soil solution characteristics do not always align with specific land use patterns.

Balancing photovoltaic development and cropland protection: Assessing agrivoltaic potential in China

The expansion of utility-scale photovoltaic (PV) installations has precipitated a growing conflict for land resources between energy generation and agricultural production. Agrivoltaics, which integrate PV systems with crop production, have emerged as promising solutions to alleviate land-use conflicts. This research integrates spatial data on PV installations with agricultural productivity figures to assess the impact of China's PV expansion on croplands and estimate the yield potential for six main crops under agrivoltaics. The results disclose a substantial incursion of PV plants into croplands, totaling 911 km2 by the year 2020. Compared to PV installations causing these croplands to be completely abandoned, agrivoltaics in a full-density PV system scenario could preserve up to 139 km2 of cropland with a corresponding crop yield of 7.1 × 104 tons, which is 9 % of the crop yield in a no-PV scenario. In a half-density PV system scenario, agrivoltaics could conserve 585 km2 of cropland with a corresponding crop yield of 4.6 × 105 tons, which is 55 % of the crop yield in a no-PV scenario. A regional distinction is observed, with northern agricultural regions demonstrating a more favorable agrivoltaic yield potential than the south. This study provides valuable insights for developing policies and best practices related to implementing agrivoltaics and PV spatial planning, thus steering a more sustainable coexistence of China's energy production and agricultural yield imperatives.