Agricultural and Forest Meteorology最新文献

{"title":"Crop productivity under heat stress: a structural analysis of light use efficiency models","authors":"Peiyu Lai,&nbsp;Michael Marshall,&nbsp;Roshanak Darvishzadeh,&nbsp;Andrew Nelson","doi":"10.1016/j.agrformet.2024.110376","DOIUrl":"10.1016/j.agrformet.2024.110376","url":null,"abstract":"<div><div>The increasing frequency and intensity of extreme heat events necessitate reliable global estimates of crop productivity under heat stress. Light use efficiency (LUE) models are commonly used for macroscale crop productivity estimation but exhibit uncertainties under high-temperature extremes related to the representation of model components and their interactions. They also struggle to isolate heat stress effects from other factors. This study reduced LUE model uncertainty for crop productivity estimation under heat stress by systematically assessing the representations of three essential components: the fraction of photosynthetically active radiation absorbed by the canopy (F_PAR), the temperature constraint (F_T), and the moisture constraint (F_M), and the synergy among them under heat-stressed and normal conditions. Model optimizations used data from 75 heat periods (HP) across 18 cropland flux sites worldwide for gross primary production (GPP) estimation, where crops were solely stressed by high temperatures, independent of low soil moisture and unfavorable light. By testing 200 LUE configurations in HP conditions, combing five F_PAR and F_T representations, and four F_M representations, we identified the best-performing model, which combined the Enhanced Vegetation Index (EVI)-based F_PAR, the evaporative fraction (EF)-based F_M, and an inverse double exponential F_T. This model notably improved GPP estimation under heat stress, comparable to three existing models under normal conditions, further enhancing aboveground biomass estimation across general conditions. Additionally, this study highlighted the limitations of five air temperature-based F_Ts, while emphasizing the critical contributions of EVI-based F_PAR and EF-based F_M under heat stress. These findings emphasize the importance of considering interactions among model components, such as the evapotranspiration effect on F_T and F_M, to reduce LUE model uncertainty under extreme conditions. Our findings offer valuable insights for improving crop productivity estimation under heat stress and developing adaptation strategies to mitigate heat stress impacts, thereby ensuring food security in the warming future.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110376"},"PeriodicalIF":5.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of the dynamics of sap-flow timeseries of a beech forest using a machine learning approach","authors":"J.P. Kabala ,&nbsp;C. Massari ,&nbsp;F. Niccoli ,&nbsp;M. Natali ,&nbsp;F. Avanzi ,&nbsp;G. Battipaglia","doi":"10.1016/j.agrformet.2024.110379","DOIUrl":"10.1016/j.agrformet.2024.110379","url":null,"abstract":"<div><div>Transpiration is a key biogeochemical process, accounting for more than half of the evaporative water fluxes from land to the atmosphere; however, its quantification is still a hot topic. Sap-flux is a commonly used technique to measure the transpiration of individual plants or trees at a high temporal resolution but limited in time and space to the measurement campaigns. The quantification of hydro-meteorological parameters, (e.g. air temperature, incoming radiation, soil moisture etc.) that drive the transpiration process, is way simpler. The condition of vegetation, which influences transpiration by modulating the stomatal resistance, is extensively monitored by several remote sensing satellite missions.</div><div>Three different Machine Learning (ML) algorithms (Regression Tree, Random Forest and XGBoost) are tested on the 2021 and 2022 timeseries of sap-flux based transpiration measured in a <em>Fagus sylvatica</em> forest located in Southern Italy, to evaluate the usefulness of different vegetation indices (namely NDVI, EVI2 from Sentinel-2 and Cross-polarization Ratio (CR) from Sentinel-1) in increasing the prediction accuracy. As meteorological predictors Radiation, Air Temperature, Vapour Pressure Deficit, and Soil Moisture were selected. ML was chosen due to its effectivity in extracting the complex and non-linear interplays between predictors and the response variable.</div><div>The results showed that the inclusion of vegetation indices in the predictors always improved the prediction accuracy. EVI2 was the most effective vegetation index, and this is the first study to show that the Sentinel-1 CR is a valuable predictor of vegetation transpiration. With respect to algorithm performance Random Forest and XGBoost outperformed the Regression Tree and showed comparable accuracies between them. The added value of Cross-Ratio is that, being sensed in the Radar wavelength, it is not affected by the atmospheric conditions, and thus might be helpful in areas that experience significant cloud cover. Our findings show different suitable approaches for upscaling sap-flux timeseries, depending on the context of application and useful to reconstruct forest transpiration at local and regional scale.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110379"},"PeriodicalIF":5.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of snow on vegetation green-up dynamics on the Tibetan Plateau: Integration of survival analysis and remote sensing data","authors":"Jingyi Xu ,&nbsp;Yao Tang ,&nbsp;Jiahui Xu ,&nbsp;Jin Chen ,&nbsp;Song Shu ,&nbsp;Jingwen Ni ,&nbsp;Xiaoqi Zhou ,&nbsp;Bailang Yu ,&nbsp;Jianping Wu ,&nbsp;Yan Huang","doi":"10.1016/j.agrformet.2024.110377","DOIUrl":"10.1016/j.agrformet.2024.110377","url":null,"abstract":"<div><div>Snow cover variation significantly impacts alpine vegetation dynamics on the Tibetan Plateau (TP), yet this effect under climate change remains underexplored. This study uses a survival analysis model to assess the influence of snow on vegetation green-up dynamics, while controlling for key temperature and water availability factors. This analysis integrates multi-source data, including satellite-derived vegetation green-up dates (GUDs), snow depth, accumulated growing degree days (AGDD), downward shortwave radiation (SRAD), precipitation, and soil moisture. Our survival analysis model effectively simulated GUD on the TP, achieving an <em>R</em> of 0.62 (<em>p</em> &lt; 0.01), a root mean square error (RMSE) of 11.20 days, and a bias of −1.41 days for 2020 GUD predictions. It outperformed both the model excluding snow depth and a linear regression model. By isolating snow's impact, we found it exerts a stronger influence on vegetation GUD than precipitation in snow-covered areas of the TP. Furthermore, snow depth effects varied seasonally: a 1-cm increase in preseason snow depth reduced green-up rates by 8.48% before 156^th day but increased them by 4.74% afterward. This indicates that deeper preseason snow cover delays GUD before June, but advances it from June onward, rather than having a uniform effect. These findings highlight the critical role of snow and underscore the need to incorporate its distinct effects into vegetation phenology models in alpine regions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110377"},"PeriodicalIF":5.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901843","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":"The biophysical effects of phenological shifts impact land surface temperature for corn expansion in Northeastern China","authors":"Yuyang Ma ,&nbsp;Jie Li ,&nbsp;Jianxi Huang ,&nbsp;Anne Gobin ,&nbsp;Xuecao Li ,&nbsp;Wenqi Liu ,&nbsp;Haixiang Guan ,&nbsp;Nadezhda N. Voropay ,&nbsp;Chuli Hu","doi":"10.1016/j.agrformet.2024.110373","DOIUrl":"10.1016/j.agrformet.2024.110373","url":null,"abstract":"<div><div>In the last two decades, rapid corn expansion has significantly impacted local and regional climates in Northeastern China. However, its climatic effects and underlying biophysical mechanisms have rarely been investigated, particularly in accurately describing the changes in surface physiological structure throughout different phenological stages. This study utilized remote sensing observations and the pair-wise comparison approach to examine land surface temperature (LST) change associated with corn expansion at various phenological stages and whole growth seasons, respectively. We then employed the temperature response model to decompose and quantify the LST changes into radiative processes (albedo) and non-radiative processes (i.e., evapotranspiration and turbulent heat exchange). This study indicated that, except for soybean, the mean LST changes (ΔMean_LST) induced by corn expansion initially decreased and subsequently increased with the phenology shifts. Specifically, the potential warming effect was pronounced during three-leaves (EMV3) to seven-leaves stage (V7) and V7 to jointing date (JD), with the largest warming in Mean_LST occurring when corns were converted into trees (1.24±0.43 K) (mean ± 95 % confidence level) (0.93±0.29 K), followed by grass (0.47±0.37 K) (0.43±0.31 K), rice (0.46±0.23 K) (0.31±0.22 K), wetlands (0.16±0.21 K) (0.15±0.34), respectively. EMV3 to JD dominated the ΔMean_LST for the whole growth season, potentially warming the Mean_LST when trees, grass, rice, and wetlands converted to corn, while cooling the Mean_LST when soybeans converted to corn. Furthermore, The effect of phenological stages on LST varies with latitude. For example, during V7 to JD and Milky date (MID) to Maturity date (MD), the non-radiative warming effect of wetland conversion surpassed that of rice conversion as latitude increased (44°N-47°N). This indicates that the wetland conversion causes intensified warming at high latitudes in these stages. Additionally, non-radiative processes, characterized by varying signs and magnitudes, dominated the LST response to corn expansion. Overall, this study comprehensively investigated the ΔLST of corn expansion at various phenological stages and latitudes through the biophysical mechanism, which could be beneficial in developing adaptive and mitigative agricultural management strategies for climate warming in Northeast China.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110373"},"PeriodicalIF":5.6,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888604","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":"Decadal isotopic and functional trait evidence reveals water and nitrogen constrains on productivity of three subtropical conifers","authors":"Jing Wang ,&nbsp;Xuefa Wen","doi":"10.1016/j.agrformet.2024.110375","DOIUrl":"10.1016/j.agrformet.2024.110375","url":null,"abstract":"<div><div>Increasing evidence indicates that plant productivity is constrained by water and nutrient availability under natural conditions of the stimulatory effects of elevated CO₂ concentration (eCO₂). However, it remains unclear how plant traits related to water and nitrogen acquisition and utilization acclimate to the soil water and nitrogen limitations on productivity. To address this, we investigated isotopic and functional traits and net primary productivity (NPP) of three dominant species of <em>Pinus elliottii, Cunninghamia lanceolata</em>, and <em>Pinus massoniana</em> in a subtropical coniferous plantation from 2011 to 2022 along with environmental parameters. Faced with increasing soil water and nitrogen stress, stomatal conductance (gs, 1/leaf δ¹⁸O enrichment) decreased with eCO₂ in all species. Stomatal closure enhanced intrinsic water use efficiency (iWUE, derived from leaf δ¹³C using photosynthetic discrimination model) in <em>P. elliottii</em> and <em>P. massoniana</em> but not in <em>C. lanceolata</em>. Although eCO₂ compensate for productivity losses resulting from drought-induced decreases in gs, increased NPP was observed only in <em>P. elliottii</em>, reflecting differences in the species' abilities to acclimate and overcome resource limitations. All species showed increased mycorrhizal dependency (the difference in δ¹⁵N between leaves and soil, |△¹⁵N|), high leaf nitrogen content, but reduced nitrogen use efficiency, leaf water content and specific leaf area. This suggested that plants increased nitrogen investment through biological adaption to mitigate productivity limitations caused by water and nutrient stress. The increased NPP in <em>P. elliottii</em> was due to high nitrogen uptake and low leaf nitrogen demand, compensating for water limitations. Conversely, reductions in NPP in <em>C. lanceolata</em> and <em>P. massoniana</em> were attributed to the relatively low nitrogen uptake and high leaf nitrogen demand, which failed to offset water limitations. This implies that the magnitude and direction of vegetation productivity responses to eCO₂ are determined by species-specific differences in plant adaptations to water and nutrient limitations.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110375"},"PeriodicalIF":5.6,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888603","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 the impact of extreme climate events on European gross primary production","authors":"Huihui Zhang ,&nbsp;Hugo A Loaiciga ,&nbsp;Akpona Okujeni ,&nbsp;Ji Liu ,&nbsp;Min Tan ,&nbsp;Tobias Sauter","doi":"10.1016/j.agrformet.2024.110374","DOIUrl":"10.1016/j.agrformet.2024.110374","url":null,"abstract":"<div><div>Climate warming and the associated intensification of extreme climate events (such as droughts, heavy precipitation, and heatwaves) present challenges to plant growth. Plant growth is influenced by a number of factors such as soil moisture, water demand by plants, temperature sensitivity, growth stage, and by irrigation practices in the case of crops. The response of plant growth to extreme climate events across a range of growing periods, climate regions, and agricultural land types under different irrigation strategies remains unclear. This study utilizes ten extreme climate indices and six drought indices to predict plant growth outcomes, as indicated by the end-of-growing season Gross Primary Production (GPP), across different growing seasons in Europe from 2003 to 2020. This work examines the impact of extreme climate events on plant growth with a novel explainable LightGBM model. This model elucidates the contribution of such events to plant growth, and helps to identify their tipping points. This paper's results demonstrate that early-season soil moisture and extreme absolute temperatures are key predictors in forecasting the end-of-growing season GPP, indicating potential drought memory. Plant growth correlates highly with extreme climate events in arid, cold, and temperate climates. In arid climates the extreme precipitation amounts are the predominant predictor of end-of-growing season GPP. Agricultural drought plays a leading role in the model prediction results in cold climates. Extreme climate events have a more pronounced effect on plant growth yield in rainfed cropland and grasslands compared to irrigated croplands. The implementation of irrigation strategies involving human intervention would help mitigate the impact of extreme climate events on plant growth outcomes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110374"},"PeriodicalIF":5.6,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wetter, but not wet enough—Limited greenhouse gas mitigation effects of subsurface irrigation and blocked ditches in an intensively cultivated grassland on fen peat","authors":"Sebastian Heller ,&nbsp;Bärbel Tiemeyer ,&nbsp;Willi Oehmke ,&nbsp;Peter Gatersleben ,&nbsp;Ullrich Dettmann","doi":"10.1016/j.agrformet.2024.110367","DOIUrl":"10.1016/j.agrformet.2024.110367","url":null,"abstract":"<div><div>High-intensity grassland farming on peatlands is a profitable land use option in Western and Central Europe. This highly productive land use requires extensive drainage measures and regular grassland renewal. The drainage practice in particular substantially increases peat mineralisation, resulting in high emissions of the greenhouse gases (GHG) carbon dioxide (CO₂) and nitrous oxide (N₂O). Against this, a controlled raising of the water level (WL) by subsurface irrigation (SI) or ditch blocking (DB) has been proposed as a compromise between reducing the GHG emissions and maintaining grassland use on peatlands. We tested this assumption by measuring the full set of GHGs over four years for three water management systems (SI, DB, ditch drainage as control) in combination with three grassland renewal treatments (direct sowing, shallow ploughing, original sward as control) on an intensively used fen grassland in Northwest Germany.</div><div>The mean annual WL was successfully raised by SI to −0.25 m below the soil surface, while the DB unit remained at a similar level (−0.37 m) as the control (−0.38 m). However, CO₂ emissions were only marginally reduced by SI due to high variability between sites and years. Partially higher CO₂ emissions may have been caused by a higher temperature sensitivity of the heterotrophic respiration at intermediate WLs. Partially lower CO₂ emissions may reflect increased carbon uptake by root growth (<em>Juncus effuses</em>) rather than reduced peat mineralisation. The GHG mitigation potential of the SI system remained negligible in this study, as the small CO₂ reduction was offset by increased CH₄ and N₂O emissions. The average emissions of the DB system were similar to those of the control unit. Both renewal treatments increased N₂O emissions for approximately two years. Overall, our study results do not support the use of SI as a GHG mitigation measure for intensively used fen grasslands.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110367"},"PeriodicalIF":5.6,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Species distribution models built with local species data perform better for current time, but suffer from niche truncation","authors":"Nicolò Anselmetto ,&nbsp;Donato Morresi ,&nbsp;Simona Barbarino ,&nbsp;Nicola Loglisci ,&nbsp;Matthew G. Betts ,&nbsp;Matteo Garbarino","doi":"10.1016/j.agrformet.2024.110361","DOIUrl":"10.1016/j.agrformet.2024.110361","url":null,"abstract":"<div><div>To cope with climate change-induced alterations, forest ecosystems’ conservation and restoration require models that are both capable to incorporate current local-scale dynamics but also to anticipate future changes. These requirements may be fulfilled by robust assessments of response (i.e., species data such as forest inventories) and predictor (e.g., climate) variables. The aim of this study is to predict current and future probability of occurrence for 22 tree species comparing inventory and climate data at different spatial scales and test for model performance, reliability, and niche truncation.</div><div>We built species distribution models (SDMs) for 22 tree species of Piedmont, an Alpine administrative region of north-western Italy. We compared (i) a fine-scale model calibrated with a local forest inventory with a 250-m spatial resolution at the extent of Piedmont and a regional climate model calibrated on the Italian extent versus (ii) coarse-scale model calibrated with a pan-European forest inventory (EU-Forest) at 1-km resolution and a global climate dataset (CHELSA v1.2). Moreover, (iii) we developed a data pooling method by combining the species data and using CHELSA. We evaluated models using spatial-block cross-validation and external validation through several metrics. We predicted the probability of occurrence for current and future under RCP4.5 and RCP8.5 climate scenarios.</div><div>Models built with local species data performed better for the future than those incorporating broad species data and their current predictions reflected the realized distribution of species but they suffered from niche truncation while extrapolated to the future. Indeed, models calibrated at the local scale predicted greater magnitude of changes for future scenarios compared to coarse-scale models. Integrating species data at different extents and resolutions is a valid approach when both are available.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110361"},"PeriodicalIF":5.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation and simulation of terrestrial latent heat flux globally: A collaborative effort utilizing CMIP6 climate models and eddy covariance observations","authors":"Xinling Tian,&nbsp;Zhenhua Di,&nbsp;Yunjun Yao,&nbsp;Zhenwei Liu,&nbsp;Hao Meng,&nbsp;Huiying Sun,&nbsp;Xueyan Wang,&nbsp;Wenjuan Zhang","doi":"10.1016/j.agrformet.2024.110371","DOIUrl":"10.1016/j.agrformet.2024.110371","url":null,"abstract":"<div><div>Exchanging latent heat flux (LE) through evapotranspiration impacts the atmospheric thermodynamics and water cycle. The Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase6 (CMIP6) are vital to reproduce improved LE variations globally, albeit with significant uncertainties. Meanwhile, the rational attribution to regions of LE simulations is essential for informed water resources administration and climate control. This paper presents the first comprehensive evaluation of the ability of 49 ESMs to model global terrestrial LE during the 2000–2014 period based on 205 eddy covariance (EC) observations. Utilizing the Bayesian Model Averaging (BMA) method, we analyzed the spatial-temporal variation and attribution to regions of global LE during 1980–2014 by combining the top six models with EC observations. The results showed that most ESMs overestimated LE, with an average <em>BIAS</em> of 7.57 W‧<em>m</em>⁻², covering −7–17 W‧<em>m</em>⁻². Among them, the MIROC6 model evinced the highest predictive skill at various land cover types. Moreover, the BMA-based global average terrestrial LE showed low LE values in dry and cold regions of temperate and cold zones of middle and high latitudes but evidenced high LE values in hot and humid regions of low-latitude tropical zones. The inter-annual variations of the BMA-based global annual LE exhibited a significant linear increasing trend with a 0.027 W‧<em>m</em>⁻² slope (<em>P</em>-value &lt;0.05). Further attribution to regions analyses were concluded, considering that LE trends were the same as the temperature trends in the Northern Hemisphere, especially in the middle and high latitudes. These conditions are comparable to radiation in the equatorial regions, correlating substantially with precipitation in dry and semi-dry regions across Asia, Europe, and Africa. In addition, the advantages of CMIP6 ESMs on LE simulations mainly including bias, and interannual variability characteristics were also concluded compared with CMIP5 ESMs.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110371"},"PeriodicalIF":5.6,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867136","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":"Reductions in nitrous oxide emissions in diverse crop rotations linked to changes in prokaryotic community structure","authors":"Mingming Zong ,&nbsp;Xiaolin Yang ,&nbsp;Alberto Sanz-Cobena ,&nbsp;Uffe Jørgensen ,&nbsp;Klaus Butterbach-Bahl ,&nbsp;Diego Abalos","doi":"10.1016/j.agrformet.2024.110370","DOIUrl":"10.1016/j.agrformet.2024.110370","url":null,"abstract":"<div><div>Diverse crop rotations are increasingly recognized as key to address the global food crisis and improve environmental sustainability, including reducing nitrous oxide (N₂O) emissions. However, the specific effects on N₂O emissions of different crops in these rotations and the underlying incidence on microbial processes remain underexplored. In a six-year field study, we compared N₂O emissions from traditional wheat-maize rotation with diverse rotations, including legumes (peanut, soybean), ryegrass, sorghum, and sweet potato. We also examined the microbial functions associated with nitrogen cycling based on functional annotation of prokaryotic taxa (FAPROTAX) analysis. Our study showed that diversified crop rotations with reduced synthetic fertilization and irrigation can reduce N₂O emissions by 23 %-49 % compared to conventional rotations. These reductions were supported by increases in soil organic carbon, soil carbon/nitrogen ratio and decreases in the relative abundance of denitrifying microorganisms, particularly observed in rotations with soybean and sweet potato. However, the spring maize and peanut-based rotation had higher emission factors than traditional wheat-maize rotation due to lower initial crop nitrogen uptake and lower nitrogen use efficiency, respectively. Changes in the microbial community structures of nitrification and denitrification processes, including increased activity of ammonia-oxidizing bacteria <em>MND1</em> and archaea <em>Candidatus Nitrososphaera</em> in legume and sweet potato rotations, and a shift in denitrifying microbes of diverse rotations (a decrease in <em>Rhodoplanes</em> and an increase in <em>Paracoccus</em>), significantly contributed to the overall reductions in emissions in all other investigated rotation systems. Understanding the microbial mechanisms that control N₂O emissions from agricultural soils will enable the development of more effective and crop-specific strategies to further reduce greenhouse gas emissions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110370"},"PeriodicalIF":5.6,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857559","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}