European Journal of Agronomy最新文献

{"title":"Assessing tea foliar quality by coupling image segmentation and spectral information of multispectral imagery","authors":"Xue Kong ,&nbsp;Bo Xu ,&nbsp;Yang Meng ,&nbsp;Qinhong Liao ,&nbsp;Yu Wang ,&nbsp;Zhenhai Li ,&nbsp;Guijun Yang ,&nbsp;Ze Xu ,&nbsp;Haibin Yang","doi":"10.1016/j.eja.2024.127491","DOIUrl":"10.1016/j.eja.2024.127491","url":null,"abstract":"<div><div>In-situ rapid detection of biophysical parameters in tea leaves using spectral data is essential for enhancing the quality and yield of tea. However, a major challenge with the current application of spectral technology is its inability to completely distinguish between old leaves and picked leaves within the field of view, which affects the accurate correspondence of biochemical elements. Therefore, this study achieved precise matching of biophysical parameters with spectral information by focusing on the spectra of picked leaves. By combining the Excess Green minus Excess Red (ExGR) with the image segmentation methods of Otsu and P75, the spectral features of picked leaves were effectively identified from complex backgrounds. Additionally, the vegetation indices (VIs) closely associated with the biophysical parameters of tea were selected, and a partial least squares regression (PLSR) model was applied for parameter inversion. Results demonstrated that the VIs calculated using Otsu (VI_OtsuPix) and P75 (VI_P75Pix) exhibited significantly improved correlations with the biophysical parameters of tea compared with those calculated using ExGR &gt; 0 (GreenPix). The PLSR model based on VI_OtsuPix performed well in estimating the total polyphenols (TPP), achieving a coefficient of determination (R²) of 0.39 and a root mean square error (RMSE) of 32.24 mg g⁻¹. In predicting free amino acids (FAA), VI_P75Pix demonstrated the best inversion accuracy (R² = 0.53, RMSE = 3.41 mg g⁻¹). These findings not only confirmed the potential of integrated image technology in the non-destructive assessment of biophysical components in picked leaves but also provide the tea production and processing industry with a fast and cost-effective method for quality monitoring.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127491"},"PeriodicalIF":4.5,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935443","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":"Crop straw converted to biochar increases soil organic carbon but reduces available carbon","authors":"Li Li ,&nbsp;Jinkang Yang ,&nbsp;Yalin Yu ,&nbsp;Awais Shakoor ,&nbsp;Ahmad Latif Virk ,&nbsp;Feng-Min Li ,&nbsp;Haishui Yang ,&nbsp;Zheng-Rong Kan","doi":"10.1016/j.eja.2024.127499","DOIUrl":"10.1016/j.eja.2024.127499","url":null,"abstract":"<div><div>Biochar can potentially be used to sequester soil organic carbon (SOC). However, a comprehensive assessment of SOC and its fractions in response to biochar produced by crop straw is still lacking compared to straw return. Here, a global meta-analysis with 58 publications was conducted to quantify the impacts of straw biochar on SOC contents. The results showed that straw biochar (BC) addition increased SOC content by 49.4 % and 20.1 % compared to straw removal (S0) and straw return (ST), respectively. Random Forest model suggested that soil initial total N, mean annual precipitation (MAP), bulk density (BD), mean annual temperature (MAT), initial SOC, and biochar pyrolysis temperature were the critical factors affecting SOC contents under BC than that under S0 (<em>P</em> &lt; 0.05). Compared to ST, experimental duration, soil initial total N, initial SOC, cropping system, soil pH, and land use were the main factors driving the response of SOC to BC (<em>P</em> &lt; 0.05). Specifically, with significant variations among subgroups, the biochar-amended soil had higher relative changes in SOC content under experimental duration of 2–4 years (23.0 %), soil initial total N ≤ 0.9 g kg⁻¹ (28.0 %), initial SOC &lt; 9 g kg⁻¹ (26.0 %), double cropping system (23.8 %), soil initial pH &gt; 6.4 (22.6 %), paddy-upland (19.8 %) when compared to ST. Straw biochar had a higher microbial biomass carbon (MBC), humic acid carbon (HAC), and dissolved organic carbon (DOC) compared with S0. Whereas compared to ST, BC significantly decreased the concentrations of MBC, mineral-associated organic carbon (MAOC), fulvic acid carbon (FAC), and DOC, indicating that biochar produced by crop straw is not conductive to microbial utilization and growth. Overall, straw biochar application enhances SOC accumulation while it is difficult to be used by microorganisms. It is recommended that the co-application of crop straw and biochar from straw may benefit both SOC sequestration and the microbially mediated carbon cycle.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127499"},"PeriodicalIF":4.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935469","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":"Rice straw nitrogen can be utilized by rice more efficiently when co-incorporating with milk vetch","authors":"Qianyu Fan ,&nbsp;Jiancheng Xie ,&nbsp;Jintao Du ,&nbsp;Huanyu Ge ,&nbsp;Cuilan Wei ,&nbsp;Hao Qian ,&nbsp;Hai Liang ,&nbsp;Jun Nie ,&nbsp;Feng Hu ,&nbsp;Songjuan Gao ,&nbsp;Weidong Cao","doi":"10.1016/j.eja.2024.127495","DOIUrl":"10.1016/j.eja.2024.127495","url":null,"abstract":"<div><div>The co-incorporation of milk vetch (MV) and rice straw (RS) in paddy field can promote nitrogen (N) uptake of rice, but the mechanisms of increased N utilization and contributions of milk vetch N (N_MV) or rice straw N (N_RS) to rice N uptake are still unclear. Two long-term field experiments and a ¹⁵N dual-label pot experiment were established to explore the effects of co-incorporation of milk vetch and rice straw on the fate and utilization of milk vetch N and rice straw N in the rice cropping system. Results of the field experiments showed that co-incorporation of MV and RS increased the rice N uptake by 45.0 % at two sites on average, compared to single RS return. The ¹⁵N dual-label pot experiment indicated that compared to single RS, co-incorporation of MV and RS increased the N_RS uptake and N_RS recovery of rice by 53.2 % and 53.4 %, respectively, and the N_RS recovery in soil was increased by 55.4 %. This study concluded that co-incorporation of MV and RS facilitated the efficient utilization of N_RS by increasing N_RS uptake of rice and recovery in soil.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127495"},"PeriodicalIF":4.5,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935464","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":"Precision nitrogen fertilization strategies for durum wheat: a sustainability evaluation of NNI and NDVI map-based approaches","authors":"Carolina Fabbri ,&nbsp;Antonio Delgado ,&nbsp;Lorenzo Guerrini ,&nbsp;Marco Napoli","doi":"10.1016/j.eja.2024.127502","DOIUrl":"10.1016/j.eja.2024.127502","url":null,"abstract":"<div><div>Durum wheat, one of the most important staple crops, faces increasing use of fertilizers, particularly nitrogen (N), to meet growing food demand. However, inefficient nitrogen management to meet crop demand can contribute to harms ecosystems. This study focuses on the application of precision fertilization technologies, particularly through variable-rate fertilization based on satellite imagery, to enhance N use efficiency in durum wheat cultivation. To this end, an experiment was conducted during four consecutive growing seasons, from October 2018 to July 2022, in Asciano, Siena, Italy. A total of four N fertilization approaches were evaluated: a uniform N rate, calculated conventionally, and three variable rates based on Sentinel-2 L2A spectral bands. These variable rate approaches include one using the Nitrogen Nutrition Index (NNI), a proportional NDVI-based estimate (NDVIH), and a compensative NDVI-based estimate (NDVIL). Results indicate that the NNI approach, based on satellite imagery, lead to significant N savings without compromising grain yield or quality. This approach also optimizes protein partitioning and dough technical properties, essential factors in various end-use applications. The NNI approach consistently outperforms the other approaches in terms of N fertilizer use efficiency (NfUE). Furthermore, the NNI approach proves to be economically advantageous, with lower social costs and higher rates of return compared to other N fertilization approaches. This emphasizes the economic and environmental sustainability of precision fertilization techniques, specifically NNI, in durum wheat cultivation. This research provides valuable insights for the practical implementation of satellite-based N fertilization strategies, in particular NNI, which offer long-term benefits for sustainable agriculture.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127502"},"PeriodicalIF":4.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905653","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":"Optimizing maize production in the Guanzhong Region: An evaluation of density tolerance, yield, and mechanical harvesting characteristics in different maize varieties","authors":"Xiaoyue Wang ,&nbsp;Xiaopeng Wu ,&nbsp;Yongzhi Hua ,&nbsp;Yuqing Li ,&nbsp;Liangchuan Ma ,&nbsp;Yihuang Gong ,&nbsp;Wanchao Zhu ,&nbsp;Shutu Xu ,&nbsp;Jiquan Xue ,&nbsp;Xiaoliang Qin ,&nbsp;Kadambot H.M. Siddique","doi":"10.1016/j.eja.2024.127500","DOIUrl":"10.1016/j.eja.2024.127500","url":null,"abstract":"<div><div>The rising demand for maize and increasing labor costs necessitate the selection of appropriate varieties to enhance maize production. This study evaluated the performance of three maize varieties—SD650, ZD958, and SD8806—at four planting densities: low (4.5 ×10⁴ plants/hm²), regular (6 ×10⁴ plants/hm²), medium (7.5 ×10⁴ plants/hm²), and high (9 ×10⁴ plants/hm²) over two years (2020 and 2021). The results demonstrated that SD650 consistently outperformed the other varieties, offering higher yield, superior lodging resistance, and better adaptation to high-density planting. These advantages were attributed to SD650’s optimized plant architecture and ability to maintain a higher kernel number per ear under dense planting conditions. Moreover, SD650 had a faster kernel dehydration rate during late growth stages and lower kernel water content at maturity, making it more suitable for mechanical harvesting. In conclusion, maize varieties like SD650, characterized by shorter growth periods, high-density tolerance, high yields, and compatibility with mechanized harvesting, are ideal for cultivation in summer-sown regions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127500"},"PeriodicalIF":4.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905648","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":"Influence of agronomic parameters and storage parameters on the frying color of French fry potatoes (Solanum tuberosum L.)","authors":"Michaela Pia Laumer ,&nbsp;Adolf Kellermann ,&nbsp;Franz-Xaver Maidl ,&nbsp;Kurt-Jürgen Hülsbergen ,&nbsp;Thomas Ebertseder","doi":"10.1016/j.eja.2024.127493","DOIUrl":"10.1016/j.eja.2024.127493","url":null,"abstract":"<div><div>Farmers and French fry producers have stated that each year, various factors impact the frying quality of potato tubers. As a result, a trial was designed to study the frying color development of the cultivar Innovator grown under different conditions (location, nitrogen fertilization, and harvest date) and stored at 6.5°C and 7.5°C. The samples were evaluated monthly from December to March. A multiple regression model was created using all the samples from the two trial years, explaining &gt; 85 % of the differences in frying color. Additionally, models for both the years and every sampling month were calculated. These multiple regression models helped measure the impact of the variables and their consistency. The results revealed a significant impact of the climatic water balance in the latter part of June, which explained differences between years and locations. Other factors determining frying color were the harvest date, storage duration, and storage temperature. No effect of the location or the tested nitrogen fertilization rates could be found.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127493"},"PeriodicalIF":4.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905649","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":"Rainfall and maximum temperature are dominant climatic factors influencing APSIM-Maize cultivar parameters sensitivity in semiarid regions","authors":"Xuening Yang ,&nbsp;Xuanze Zhang ,&nbsp;Zhigan Zhao ,&nbsp;Ning Ma ,&nbsp;Jing Tian ,&nbsp;Zhenwu Xu ,&nbsp;Junmei Zhang ,&nbsp;Yongqiang Zhang","doi":"10.1016/j.eja.2024.127494","DOIUrl":"10.1016/j.eja.2024.127494","url":null,"abstract":"<div><div>Sensitivity analysis is crucial for identifying key crop model parameters to improve parameterization efficiency, but climate conditions can affect sensitivity, leading to inaccurate calibration if different climate conditions are not considered. This study uses the extended Fourier amplitude sensitivity test to identify sensitive cultivar parameters in the Agricultural Production System Simulator (APSIM-Maize), focusing on maize yield in a semiarid region. Regression analysis shows that rainfall and maximum temperature significantly impact the sensitivity of maize yield to the transpiration efficiency coefficient (<em>transp_eff_cf</em>) (r = -0.66 and 0.63, p = 0.001 and 0.003, respectively) and grain growth rate (<em>grin_gth_rate</em>) (r = 0.74 and −0.70, p = 0.0002 and 0.0005, respectively). The sensitivity of maize yield to the thermal time from emergency to the end of juvenile (<em>tt_emerg_to_endjuv</em>) shows varying sensitivity across years (<em>ST</em>_<em>i</em> = 0.03–0.26), influenced by maximum temperature. Our results demonstrated that <em>transp_eff_cf</em> and <em>grain_gth_rate</em> should be adjusted cautiously, especially in drier or warmer conditions. The implications of our study extend to providing valuable support for the calibration of APSIM-Maize cultivar parameters in response to climate variability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127494"},"PeriodicalIF":4.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905677","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":"Climate normals shape regional disparities of cotton yield failures compared to dominant impacts from climate extremes","authors":"Shengli Liu ,&nbsp;Tongtong Shi ,&nbsp;Tong Li ,&nbsp;Xinru You ,&nbsp;Shuai Dai ,&nbsp;Wenkui Wang ,&nbsp;Zhanbiao Wang ,&nbsp;Xiongfeng Ma","doi":"10.1016/j.eja.2024.127490","DOIUrl":"10.1016/j.eja.2024.127490","url":null,"abstract":"<div><div>Spatial co-occurring crop yield failures strongly affect crop productivity, resulting in profound socioeconomic consequences. Cotton (<em>Gossypium hirsutum L.</em>) cultivation in Xinjiang accounts for 91 % of China’s national cotton production, and play a critical role in the global textile industry. However, given the wide range of climatic niches for cotton cultivation in Xinjiang, the lack of a comprehensive understanding of the spatial links in cotton yield failures and their relations with climate attributes impedes the design of effective regional strategies to enhance cotton productivity synchronously. To address this, we quantified the spatial dependence of cotton yield failures and assessed how climate variations affect cotton yield anomalies across regions in Xinjiang by conducting a case study focused on cotton cultivation in North Xinjiang (NXJ) and South Xinjiang (SXJ). We employed statistical analysis combining copula theory and multiple linear regression to untangle the regional cotton yield failure and their anomalies attributed to climate normal and extremes. Our results demonstrated a significant spatial connection between cotton yield anomalies in these regions, with a recurring pattern of yield failures emerging approximately every 15 years. Moreover, yearly variations in climate attributes explained over 40 % of the observed cotton yield anomalies. Climate extremes exerted a fourfold greater impact on cotton yield anomalies compared to the weaker signals from climate normals. Nevertheless, the cumulative climate normals significantly contributed to regional disparities in cotton yield anomalies. These findings highlight the multifaceted contributions of climatic drivers to spatially compounded cotton yield failures. Measures aimed at accelerating breeding cycle against both normal and extreme climate changes, as well as implementing targeted field management practices, are essential for synchronously enhancing cotton productivity in China and addressing critical challenges within the cotton industry.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127490"},"PeriodicalIF":4.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151456","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":"Using remotely sensed vegetation indices and multi-stream deep learning improves county-level corn yield predictions","authors":"Shahid Nawaz Khan ,&nbsp;Javed Iqbal ,&nbsp;Mobushir Riaz Khan ,&nbsp;Naeem Abbas Malik ,&nbsp;Faiq Ahmad Khan ,&nbsp;Kashif Khan ,&nbsp;Abid Nawaz Khan ,&nbsp;Amna Wahab","doi":"10.1016/j.eja.2024.127496","DOIUrl":"10.1016/j.eja.2024.127496","url":null,"abstract":"<div><div>The increasing global population and climate change pose significant threats to global food security. Machine learning models that use crop growth variables retrieved from remote sensing data offer the potential for early estimates of county-level crop yields, aiding informed decision-making for grain policy, insurance, and food security planning. While vegetation indices and other metrics derived from remote sensing data have been utilized in machine learning models for county-level crop yield estimation, multi-stream data fusion through deep learning remains underexplored. In this study, we trained random forest regression (RFR), support vector regression (SVR), deep neural network (DNN) and a multi-stream deep neural network (MSDNN) using vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate county-level corn yield in Iowa state, within U.S. corn belt. For MSDNN, NDVI and EVI were used as two separate data streams initially to train the model. Additionally, we evaluated the effectiveness of early-season remote sensing data by dividing the growing season into four intervals: end of May (EoM), end of July (EoJ), and end of September (EoS). As compared to SVR, RFR and DNN, MSDNN model performed better with coefficient of determination (R²) = 0.86. For early season yield estimation, the EoS model yielded the highest accuracy (R² = 0.86), followed by EoJ (R² = 0.66) and EoM (R² = 0.35). These findings indicate that county-level corn yield can be reasonably estimated before harvest, offering valuable insights for informed grain policy decisions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127496"},"PeriodicalIF":4.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152137","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":"Simulating phosphorus dynamics between the soil and the crop with the STICS model: Formalization and multi-site evaluation on maize in temperate area","authors":"Mounir Seghouani ,&nbsp;Matthieu N. Bravin ,&nbsp;Patrice Lecharpentier ,&nbsp;Alain Mollier","doi":"10.1016/j.eja.2024.127475","DOIUrl":"10.1016/j.eja.2024.127475","url":null,"abstract":"<div><div>Soil-crop models are pertinent tools to study and manage phosphorus (P) in agroecosystems. However, P modeling is suffering a delay as compared to nitrogen and carbon. In this study, we extended the STICS model to simulate the P uptake and P feed-back by coupling it with a soil-plant P model. The paper aims at describing the P model and present the results showing the ability of the model to simulate contrasting P uptake and growth response pattern of maize submitted to different level of P inputs. in temperate area. The model simulates the soil P availability and the crop P demand, uptake, and partitioning. A major originality of this work is that it relies on soil solution P concentration and P sorption curves to simulate soil P availability, and critical P dilution curves to simulate crop P demand. We evaluated the model against a dataset coming from four field fertilization trials located at different site in mainland France. The trials consisted of fertilizing maize with a mineral fertilizer at three application rates (P0, P1, P2) which induced contrasted crop responses to P shortage. The model has shown great ability in predicting P uptake both dynamically and at the end of the cropping season for the entire dataset (EF &gt;0.75). The model has satisfactory predictions of crop biomass accumulation (EF &gt;0.5) and leaf area index. Considering each fertilization level separately, the evaluation has shown that the model had predicted the final P uptake of P1 and P2 treatments better than that of P0 treatment (EF of 0.74, 0.73 and 0.62 for P2, P1, and P0, respectively). The predictions made for the P0 treatment remained nonetheless satisfactory for both P uptake and plant growth. The good performance of the model is promising as it shows that the model is sufficiently robust to simulate maize P uptake across a range of soil P availability and P fertilization under contrasting temperate climatic conditions. Further validation on other crop species and soil and climatic conditions is discussed.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127475"},"PeriodicalIF":4.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884209","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}