Agricultural and Forest Meteorology最新文献

{"title":"Genotype-specific calibration and uncertainty quantification in oil palm modeling: developing a probabilistic framework for tropical American conditions","authors":"Carlos Ricardo Bojacá, Cristihian Bayona, Iván Ayala-Díaz, Alexandre Cooman","doi":"10.1016/j.agrformet.2026.111160","DOIUrl":"https://doi.org/10.1016/j.agrformet.2026.111160","url":null,"abstract":"","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"17 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147620307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Similarity of wind speed in the Roughness Sublayer above vegetation","authors":"Raoni Aquino Silva de Santana, Leandro dos Reis Biase Gomes, Marcelo Chamecki, Jose D. Fuentes, Flávio Augusto Farias D’Oliveira, Denisi Holanda Hall, Bruno Takeshi Tanaka Portela, Cléo Quaresma Dias-Júnior","doi":"10.1016/j.agrformet.2026.111155","DOIUrl":"https://doi.org/10.1016/j.agrformet.2026.111155","url":null,"abstract":"","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"1 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147620309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust estimation of daily photosynthesis from instantaneous observations through machine-learning integration of radiation and environmental drivers","authors":"Yanan Zhou,&nbsp;Xing Li,&nbsp;Jingyu Lin,&nbsp;Xi Liu","doi":"10.1016/j.agrformet.2026.111064","DOIUrl":"10.1016/j.agrformet.2026.111064","url":null,"abstract":"<div><div>Remote sensing (RS) facilitates large-scale estimation of vegetation carbon and water fluxes, yet temporal mismatches persist between its instantaneous observations and the daily flux mean or sum required for ecological modeling. Traditional upscaling methods typically convert instantaneous flux observations to daily values through assuming that diurnal flux patterns are mainly driven by solar radiation, failing to capture real dynamics induced by other environmental factors (e.g., temperature and moisture). This introduces substantial errors, particularly in daily carbon flux estimation. To address this issue, we focus on gross primary production (GPP) and develop a new conversion factor model that integrates solar radiation and other key environmental drivers, enabling robust upscaling from instantaneous to daily scales. Using the FLUXNET2015 dataset, the conversion factor <span><math><msub><mi>γ</mi><mrow><mi>e</mi><mi>n</mi><mi>v</mi></mrow></msub></math></span>, defined as the ratio of instantaneous to daily GPP, was modeled using random forest, with vapor pressure deficit, soil water content, air temperature, and shortwave radiation as predictors. SHapley Additive exPlanations (SHAP) analysis was used to evaluate predictors’ contribution and response mechanisms. Results show that the proposed model outperformed traditional upscaling methods in daily GPP estimation, improving R² by up to 39% and reducing RMSE by up to 82%. Validation across diverse ecosystems, environmental stress levels, and drought conditions further confirmed its superior generalizability over conventional methods. Critically, <span><math><msub><mi>γ</mi><mrow><mi>e</mi><mi>n</mi><mi>v</mi></mrow></msub></math></span> retained high accuracy when driven by ERA5-Land reanalysis data instead of site-level tower measurements and reliably upscaled satellite-based instantaneous GPP snapshots to daily estimates, demonstrating scalability for large-scale applications. Moreover, <span><math><msub><mi>γ</mi><mrow><mi>e</mi><mi>n</mi><mi>v</mi></mrow></msub></math></span> effectively captured complex diurnal dynamics of vegetation photosynthesis under environmental stress, and through SHAP, revealed the growing role of water or temperature-related drivers in regulating GPP diurnal patterns as stress intensified. Overall, this study presents a structurally simple yet ecologically grounded solution to the temporal mismatch in RS-based GPP estimation, and offers valuable insights for upscaling other ecosystem fluxes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"380 ","pages":"Article 111064"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling data assimilation and machine learning to improve land surface conditions and near-surface temperature and humidity forecasts","authors":"Xinlei He ,&nbsp;Shaomin Liu ,&nbsp;Tongren Xu ,&nbsp;Fei Chen ,&nbsp;Zhitao Wu ,&nbsp;Ziwei Xu ,&nbsp;Xiang Li ,&nbsp;Rui Liu","doi":"10.1016/j.agrformet.2026.111063","DOIUrl":"10.1016/j.agrformet.2026.111063","url":null,"abstract":"<div><div>Enhancing the representation of land surface conditions and improving the accuracy of near-surface weather forecasts remain critical challenges for numerical weather prediction (NWP). This study coupled a hybrid data assimilation-machine learning framework (DL) with the Weather Research and Forecasting (WRF) model to quantify the impacts of incorporating soil moisture (SM) and vegetation data on land surface initialization and near-surface weather forecast accuracy. This was achieved by integrating satellite-based leaf area index (LAI) and multi-source SM data into the WRF model in the Southern Great Plains (SGP) of the United States. The results indicate that optimizing LAI and SM significantly improves the simulation of water table depth, evapotranspiration (ET), air temperature and humidity in the WRF model. In addition to SM, LAI optimization provides additional benefits to the WRF model in dry years. A series of comparison experiments were conducted across both dry and wet years to evaluate the accuracy of air temperature and humidity forecasts. The optimized vegetation and SM conditions from the DL method were used as initial conditions for the early days of the forecast period. The results confirm that the DL method effectively refines the land surface initial conditions at the beginning of the forecast period. This effect improves the estimation of near-surface atmospheric conditions (e.g., air temperature and humidity) and alters precipitation patterns during the forecast period. In addition, the integration of LAI and SM is more effective in improving forecasts in wet/normal years than dry years. Analysis of the forecast results illustrates that the DL method can optimize initial conditions and improve near-surface weather forecasts over the next month.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"380 ","pages":"Article 111063"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Warm and wet spring compensated for the reduction in carbon sinks due to an extreme summer heatwave-drought event in 2022 in southern China","authors":"Yuanyuan Zhang ,&nbsp;Fei Jiang ,&nbsp;Yanlian Zhou ,&nbsp;Guanyu Dong ,&nbsp;Dongqiao Wu ,&nbsp;Wei He ,&nbsp;Jun Wang ,&nbsp;Mousong Wu ,&nbsp;Hengmao Wang ,&nbsp;Lingyu Zhang ,&nbsp;Mengwei Jia ,&nbsp;Weimin Ju ,&nbsp;Jing M. Chen","doi":"10.1016/j.agrformet.2026.111060","DOIUrl":"10.1016/j.agrformet.2026.111060","url":null,"abstract":"<div><div>During the July-September (JAS) of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). It caused an unprecedented decline in vegetation photosynthesis, however, its impact on the regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using the Global Carbon Assimilation System (GCAS v2) by assimilating OCO-2 XCO₂ retrievals. Our results indicate that, relative to 2015-2021, the MLYR region experienced a 45.8 TgC reduction in land sink during JAS, consistent with the TRENDYv13 simulations. Combining our inverse results with satellite proxies for GPP, we find that an unusually wet spring in 2022 boosted vegetation growth in the MLYR, increasing gross primary productivity (GPP) by 46.1 TgC and strengthening the land sink by 24.0 TgC, thereby substantially offsetting the carbon sink reductions observed during JAS. Outside the MLYR region in southern China, annual land sink increased by 49.9 TgC in remaining areas (RAS), also greatly mitigating the impact of the DH2022 on the regional carbon balance. Overall, the annual land sink in MLYR decreased by only 7.1 TgC, whereas in southern China, it increased by 42.8 TgC. During JAS, the decreased land sink in MLYR was primarily driven by a decline in GPP in forests and grass/shrub, coupled with an increase in total ecosystem respiration in croplands. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing our understanding of the impacts of climate extremes on the regional carbon cycle.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111060"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity of diaheliotropic leaf movement is enhanced in field-grown cotton under moderate water deficit","authors":"Yuan Shi,&nbsp;Fubin Liang,&nbsp;Shuhao Lv,&nbsp;Huijun Song,&nbsp;Jingshan Tian,&nbsp;Yali Zhang,&nbsp;Ling Gou,&nbsp;Wangfeng Zhang","doi":"10.1016/j.agrformet.2026.111028","DOIUrl":"10.1016/j.agrformet.2026.111028","url":null,"abstract":"<div><div>Diaheliotropic leaf movement is pronounced in cotton (<em>Gossypium hirsutum</em> L.) leaves, affecting the interception of photosynthetically active radiation and thus leaf photosynthetic capacity. The leaf movement state is related to soil water content. However, the relationship between diaheliotropic leaf movement characteristics and soil water content in cotton leaves, as well as its effect on leaf photosynthetic capacity is still unclear. In this study, cotton (<em>Gossypium hirsutum</em> L. cv. Xinluzao 45) was subjected to three water treatments: well-watered (control), moderate, and severe water deficit, with the relative soil water content in the 0–60 cm soil layer maintained at 75 ± 5 %, 55 ± 5 %, and 35 ± 5 % of the field capacity, respectively. The cotton leaves were categorized into two groups, free-moving and restrained leaves, to measure diurnal variations in midrib angle, incident photosynthetic photon flux density (PPFD), net photosynthetic rate (Pn), and sucrose and starch content under different water treatments. The results showed that the degree of diaheliotropic leaf movement reached its maximum in the morning (before 12:00). Under water deficit conditions, the time of peak variation in leaf midrib angle was advanced by 0.5–2 h compared to the control. Under moderate water deficit, the rate of midrib angle change in free-moving leaves was 27.9 %–44.3 % higher than that of the control. Accordingly, their incident PPFD was 26.7 %–31.4 % higher and Pn was 19.3 %–35.1 % higher than those in restrained leaves. Free-moving leaves exhibited synergistic changes in sucrose accumulation and water potential under moderate water deficit, and the vascular tissue at the junction of leaf and petiole changed less than that under severe water deficit. Therefore, the production and transport of photoassimilates were not affected under moderate water deficit. The stabilized accumulation of photoassimilates mitigated water stress and enhanced the sensitivity of diaheliotropic leaf movement through sucrose-dominated osmotic adjustment.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111028"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the algorithms for the estimation of wet surface evaporation on the Tibetan Plateau","authors":"Cunbo Zhang ,&nbsp;Xuelong Chen ,&nbsp;Huaiyong Shao ,&nbsp;Xin Xu ,&nbsp;Ling Yuan ,&nbsp;Yajing Liu ,&nbsp;Ying Xie ,&nbsp;Yaoming Ma","doi":"10.1016/j.agrformet.2026.111030","DOIUrl":"10.1016/j.agrformet.2026.111030","url":null,"abstract":"<div><div>Interception water accounts for 15–50% of precipitation, constituting a vital facet of the hydrological cycle. However, modeling of interception water evaporation over the wet surface of the Tibetan Plateau (TP) is frequently omitted in evapotranspiration models. In this study, a new calculation method for wet surface fraction (<em>F_wet</em>) was introduced to the MOD16-STM evapotranspiration model (Yuan et al. 2021) by reanalyzing the correlation between relative humidity and precipitation responses across the TP region. The new <em>F_wet</em> equation aids in more accurate categorizing wet and dry surface fractions for the TP region. The justification for recalibrating the wet soil resistance for evaporation was also provided. Compared with the MOD16-STM model, optimizations resulted in an increase of R² from 0.45 to 0.76, while RMSE was reduced from 40.1 to 27.1 W m^–2 and MB decreased from –26.2 to 2.3 W m^–2 under wet conditions. The integrated model with the revised wet surface evaporation algorithm exhibited significant performance enhancement‌, ‌particularly through mitigation of wet surface evaporation underestimation‌. The modified algorithm enables improved capture‌ of post-precipitation evapotranspiration variation.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111030"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved simulation of gross primary production and evapotranspiration in a drought-prone temperate deciduous forest with the BEPS-EcoHydro","authors":"Lu Hu ,&nbsp;Mousong Wu ,&nbsp;Weimin Ju ,&nbsp;Xiuli Xing ,&nbsp;Jing M. Chen ,&nbsp;Huajie Zhu","doi":"10.1016/j.agrformet.2026.111031","DOIUrl":"10.1016/j.agrformet.2026.111031","url":null,"abstract":"<div><div>Climate extremes, particularly drought, severely affect ecosystem functions. Most terrestrial biosphere models use empirical soil moisture stress factors to represent the impacts of drought on stomatal conductance and photosynthesis, which lack a mechanistic representation of water flow in the soil-plant-atmosphere continuum (SPAC) and result in uncertainties in simulated carbon and water fluxes. In this study, a plant hydraulics module was integrated into the process-based Biosphere-atmosphere Exchange Process Simulator, i.e., the BEPS-EcoHydro, and comprehensively evaluated in a drought-prone temperate deciduous forest in the central USA. BEPS-EcoHydro considers SPAC water flow driven by the soil-leaf water potential gradient, potential transpiration, and plant water storage. Building on these hydraulic processes, the effect of water stress on photosynthesis in BEPS-EcoHydro was quantified via a linkage to leaf water potential. The results showed that BEPS-EcoHydro effectively captured variations in predawn leaf water potential at the ecosystem scale with a coefficient of determination (R²) of 0.54 (<em>p</em> &lt; 0.01), and outperformed the original BEPS in simulating soil moisture with an improvement of R² by 34%. Additionally, evapotranspiration (ET) and gross primary production (GPP) simulation performance has been improved with BEPS-EcoHydro, especially at the hourly scale. Importantly, BEPS-EcoHydro captured drought impact better than the original BEPS and detected the hysteretic responses of GPP and ET to leaf water potential during drought intensification and recovery periods. These results suggest that consideration of plant hydraulics in process-based ecosystem models is necessary to better understand mechanisms in vegetation responses to climate extremes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111031"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing evapotranspiration in rainfed and irrigated Alfalfa in the U.S. southern great plains using eddy covariance measurements and OpenET products","authors":"Pradeep Wagle ,&nbsp;Afshin Shayeghi ,&nbsp;Nishan Bhattarai ,&nbsp;Brian K. Northup ,&nbsp;Corey Moffet ,&nbsp;Stacey A. Gunter ,&nbsp;Rudra Baral","doi":"10.1016/j.agrformet.2026.111032","DOIUrl":"10.1016/j.agrformet.2026.111032","url":null,"abstract":"<div><div>Understanding the annual dynamics of water use by rainfed and irrigated alfalfa (<em>Medicago sativa</em> L.) can support its sustainable management. Changes in evapotranspiration (ET) and plant growth patterns of alfalfa across years are scarce and are not well understood in the Southern Great Plains (SGP) of the United States (U.S.). The objectives of this study were to investigate the dynamics of eddy covariance (EC) measured ET (ET_EC) and its controlling factors in rainfed and irrigated alfalfa and to compare ET_EC dynamics with OpenET products that provide several established remote sensing-based ET model products (METRIC, PTJPL, SIMS, SSEBop, SEBAL, and DisALEXI) across the western U.S. The ET_EC showed notable seasonal and interannual dynamics driven by meteorological conditions, vegetation dynamics, and water availability. Warmer and wetter conditions in April 2019 promoted initial alfalfa growth. Alfalfa’s water use (ET) mirrored its growth pattern throughout the year. Daily ET_EC rates and cumulative ET_EC at annual and seasonal scales were substantially lower than those reported for highly productive irrigated alfalfa in past studies. Satellite-derived enhanced vegetation index (EVI) and solar radiation (SR) explained 75% and 88% of variations in ET_EC for all sites combined at 8-day and monthly scales, respectively. It indicates the potential of developing empirical models using readily available EVI and SR data to monitor alfalfa ET across large areas. When compared to ET_EC, the performance of OpenET models varied widely, depending on field scenarios and criteria applied to model evaluations. SIMS and SSEBop demonstrated consistency and reliability in estimating ET for rainfed and irrigated alfalfa. DisALEXI and SEBAL performed poorly in irrigated alfalfa. METRIC and PTJPL exhibited poor performances under rainfed and irrigated conditions. By examining water use dynamics by alfalfa and the reliability of OpenET products, this study provides crucial information for effective water management practices for alfalfa.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111032"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the MCWLA agroecosystem model to better simulate methane emissions from paddy rice fields","authors":"Hong Zhou ,&nbsp;Fulu Tao ,&nbsp;Yi Chen ,&nbsp;Lichang Yin ,&nbsp;Yibo Li","doi":"10.1016/j.agrformet.2026.111053","DOIUrl":"10.1016/j.agrformet.2026.111053","url":null,"abstract":"<div><div>Rice cultivation stands out as a major greenhouse gas source, emitting 10–20% of global CH₄ emissions. How to accurately estimate CH₄ emissions from paddy rice and their mitigation potential has been key concerns. Agroecosystem models have unique advantages in understanding CH₄ processes, simulating CH₄ emissions dynamics, optimizing management practices, and quantifying mitigation potentials. However, current agroecosystem models need to be substantially improved for these purposes. In this study, we develop a comprehensive agroecosystem model, MCWLA-Rice 2.0, to better depict the production, oxidation, and emission processes of CH₄ and improve the simulation of root exudates, the effect of nitrate fertilizer on CH₄ emissions, and the decomposition of external organic carbon. We calibrate and validate the model and demonstrate its performance in simulating the rice cultivation system under different fertilizer and irrigation treatments at seven sites across Asia. Elaborating on both aboveground and belowground carbon-nitrogen coupling processes, MCWLA-Rice 2.0 is a valuable tool for simulating rice productivity and CH₄ emissions under various environments and managements, effectively supporting the development of climate-smart agriculture.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111053"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}