Pub Date : 2025-01-06DOI: 10.1016/j.eja.2024.127503
Xiaoyang Li , Yifan Wu , Chen Huang , Md. Abiar Rahman , Eli Argaman , Yan Xiao
Arbuscular mycorrhizal fungi (AMF) are widely distributed and can establish symbiotic relationships with various plants. AMF plays a critical role as a biological fertilizer in promoting sustainable agriculture. However, comprehensive studies on the effects of AMF inoculation under field conditions are still lacking. This study conducted a global synthesis of 117 peer-reviewed publications with 1633 field observations to assess the effects of different AMF inoculation treatments on plant colonization rate and crop growth performance in field experiments. The overall effect of AMF inoculation on plant colonization rate, nitrogen (N) uptake, phosphorus (P) uptake, yield and plant height demonstrated a positive impact. In crop studies, AMF inoculation was more beneficial for Leguminosae than Gramineae. Single inoculation produced greater effects than mixed inoculation. Claroideoglomus stood out in its ability to significantly boost colonization rates. However, its role in enhancing crop yields was less pronounced when compared to the contributions of Rhizophagus and Funneliformis. In tree inoculation studies, mixed inoculation outperformed single inoculation, with similar effects across fungal genera as observed in crops. AMF inoculation was more beneficial for crop P uptake rather than N uptake. Yield positively correlated with colonization and was closely associated with nutrient uptake. Soil environmental factors mainly affected plant colonization rate, while climate factors influenced crop yield. AMF inoculation positively impacts plant growth and development, but species differences, climate and soil conditions influence its effects. Therefore, this study offers valuable insights into sustainable agricultural production management and the application of AMF inoculants.
{"title":"Inoculation with arbuscular mycorrhizal fungi in the field promotes plant colonization rate and yield","authors":"Xiaoyang Li , Yifan Wu , Chen Huang , Md. Abiar Rahman , Eli Argaman , Yan Xiao","doi":"10.1016/j.eja.2024.127503","DOIUrl":"10.1016/j.eja.2024.127503","url":null,"abstract":"<div><div>Arbuscular mycorrhizal fungi (AMF) are widely distributed and can establish symbiotic relationships with various plants. AMF plays a critical role as a biological fertilizer in promoting sustainable agriculture. However, comprehensive studies on the effects of AMF inoculation under field conditions are still lacking. This study conducted a global synthesis of 117 peer-reviewed publications with 1633 field observations to assess the effects of different AMF inoculation treatments on plant colonization rate and crop growth performance in field experiments. The overall effect of AMF inoculation on plant colonization rate, nitrogen (N) uptake, phosphorus (P) uptake, yield and plant height demonstrated a positive impact. In crop studies, AMF inoculation was more beneficial for Leguminosae than Gramineae. Single inoculation produced greater effects than mixed inoculation. <em>Claroideoglomus</em> stood out in its ability to significantly boost colonization rates. However, its role in enhancing crop yields was less pronounced when compared to the contributions of <em>Rhizophagus</em> and <em>Funneliformis</em>. In tree inoculation studies, mixed inoculation outperformed single inoculation, with similar effects across fungal genera as observed in crops. AMF inoculation was more beneficial for crop P uptake rather than N uptake. Yield positively correlated with colonization and was closely associated with nutrient uptake. Soil environmental factors mainly affected plant colonization rate, while climate factors influenced crop yield. AMF inoculation positively impacts plant growth and development, but species differences, climate and soil conditions influence its effects. Therefore, this study offers valuable insights into sustainable agricultural production management and the application of AMF inoculants.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127503"},"PeriodicalIF":4.5,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935465","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}
Pub Date : 2025-01-04DOI: 10.1016/j.eja.2024.127497
Jing Zhang , Guijun Yang , Junhua Kang , Dongli Wu , Zhenhong Li , Weinan Chen , Meiling Gao , Yue Yang , Aohua Tang , Yang Meng , Zhihui Wang
Accurate and timely crop yield prediction is essential for effective agricultural management and food security. Soil moisture (SM) is a major factor that directly influences crop growth and yield, especially in arid regions. Hydrological models are often used to determine SM, which can be incorporated into crop growth models to estimate crop yield in large-scale areas. However, in existing studies on the coupling of hydrological models and crop models, there is little integration of remote sensing observation indicators into the coupled models, and few studies focus on selecting the most effective depth of SM and the number of SM layers. In this study, we developed a framework for integrating the Variable Infiltration Capacity (VIC) model and the WOrld FOod STudies (WOFOST) model to estimate winter wheat yield in the Yellow River Basin (YRB). The framework first selected the optimal SM layer from three layers and then jointly assimilated this SM as well as the leaf area index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) model into the WOFOST model using a genetic algorithm (GA). Results showed that the VIC model had a high performance in the validation period across the four subregions, with the Nash Sutcliffe Efficiency (NSE) of the simulated daily runoff and the observed runoff ranging from 0.31 to 0.73 and the corresponding Root Mean Square Error (RMSE) ranging from 256.55 to 467.21 m³ /s. The first SM layer (SM1), with a depth of 0–10 cm in the Longmen-Toudaoguai subregion and 0–26 cm in the Huayuankou-Longmen subregion, was found to be optimal, and jointly assimilating SM1 and LAI resulted in the best performance at the point scale (coefficient of determination (R²) = 0.85 and 0.87 in 2015 and 2018, respectively). The R2 improved by 0.11 and 0.06 in 2015 and 2018, respectively, compared to assimilating LAI alone, and the R2 improved by 0.04 and 0.02, respectively, compared to assimilating SM1 alone. Moreover, joint assimilation significantly improved the estimation of winter wheat yield compared to a model without assimilation (open-loop model) at the regional scale, with the R2 increasing by 0.57 and 0.59, respectively, and the RMSE decreasing by 1808.12 and 859.20 kg/ha in 2015 and 2018, respectively. The yield estimated by the joint assimilation of SM1 and LAI showed more spatial heterogeneity than that estimated by the open-loop model. This study shows that assimilating the optimal SM layer from the VIC model into the WOFOST model enhances the reliability of crop yield estimation, providing policymakers with information to improve crop management.
{"title":"Estimation of winter wheat yield by assimilating MODIS LAI and VIC optimized soil moisture into the WOFOST model","authors":"Jing Zhang , Guijun Yang , Junhua Kang , Dongli Wu , Zhenhong Li , Weinan Chen , Meiling Gao , Yue Yang , Aohua Tang , Yang Meng , Zhihui Wang","doi":"10.1016/j.eja.2024.127497","DOIUrl":"10.1016/j.eja.2024.127497","url":null,"abstract":"<div><div>Accurate and timely crop yield prediction is essential for effective agricultural management and food security. Soil moisture (SM) is a major factor that directly influences crop growth and yield, especially in arid regions. Hydrological models are often used to determine SM, which can be incorporated into crop growth models to estimate crop yield in large-scale areas. However, in existing studies on the coupling of hydrological models and crop models, there is little integration of remote sensing observation indicators into the coupled models, and few studies focus on selecting the most effective depth of SM and the number of SM layers. In this study, we developed a framework for integrating the Variable Infiltration Capacity (VIC) model and the WOrld FOod STudies (WOFOST) model to estimate winter wheat yield in the Yellow River Basin (YRB). The framework first selected the optimal SM layer from three layers and then jointly assimilated this SM as well as the leaf area index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) model into the WOFOST model using a genetic algorithm (GA). Results showed that the VIC model had a high performance in the validation period across the four subregions, with the Nash Sutcliffe Efficiency (NSE) of the simulated daily runoff and the observed runoff ranging from 0.31 to 0.73 and the corresponding Root Mean Square Error (RMSE) ranging from 256.55 to 467.21 m³ /s. The first SM layer (SM1), with a depth of 0–10 cm in the Longmen-Toudaoguai subregion and 0–26 cm in the Huayuankou-Longmen subregion, was found to be optimal, and jointly assimilating SM1 and LAI resulted in the best performance at the point scale (coefficient of determination (R²) = 0.85 and 0.87 in 2015 and 2018, respectively). The R<sup>2</sup> improved by 0.11 and 0.06 in 2015 and 2018, respectively, compared to assimilating LAI alone, and the R<sup>2</sup> improved by 0.04 and 0.02, respectively, compared to assimilating SM1 alone. Moreover, joint assimilation significantly improved the estimation of winter wheat yield compared to a model without assimilation (open-loop model) at the regional scale, with the R<sup>2</sup> increasing by 0.57 and 0.59, respectively, and the RMSE decreasing by 1808.12 and 859.20 kg/ha in 2015 and 2018, respectively. The yield estimated by the joint assimilation of SM1 and LAI showed more spatial heterogeneity than that estimated by the open-loop model. This study shows that assimilating the optimal SM layer from the VIC model into the WOFOST model enhances the reliability of crop yield estimation, providing policymakers with information to improve crop management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127497"},"PeriodicalIF":4.5,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935449","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}
Pub Date : 2025-01-04DOI: 10.1016/j.eja.2024.127491
Xue Kong , Bo Xu , Yang Meng , Qinhong Liao , Yu Wang , Zhenhai Li , Guijun Yang , Ze Xu , Haibin Yang
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 > 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.
{"title":"Assessing tea foliar quality by coupling image segmentation and spectral information of multispectral imagery","authors":"Xue Kong , Bo Xu , Yang Meng , Qinhong Liao , Yu Wang , Zhenhai Li , Guijun Yang , Ze Xu , 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 > 0 (GreenPix). The PLSR model based on VI_OtsuPix performed well in estimating the total polyphenols (TPP), achieving a coefficient of determination (R<sup>2</sup>) of 0.39 and a root mean square error (RMSE) of 32.24 mg g<sup>−1</sup>. In predicting free amino acids (FAA), VI_P75Pix demonstrated the best inversion accuracy (R<sup>2</sup> = 0.53, RMSE = 3.41 mg g<sup>−1</sup>). 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}
Pub Date : 2025-01-03DOI: 10.1016/j.eja.2024.127499
Li Li , Jinkang Yang , Yalin Yu , Awais Shakoor , Ahmad Latif Virk , Feng-Min Li , Haishui Yang , Zheng-Rong Kan
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 (P < 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 (P < 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 < 9 g kg−1 (26.0 %), double cropping system (23.8 %), soil initial pH > 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.
{"title":"Crop straw converted to biochar increases soil organic carbon but reduces available carbon","authors":"Li Li , Jinkang Yang , Yalin Yu , Awais Shakoor , Ahmad Latif Virk , Feng-Min Li , Haishui Yang , 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> < 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> < 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<sup>−1</sup> (28.0 %), initial SOC < 9 g kg<sup>−1</sup> (26.0 %), double cropping system (23.8 %), soil initial pH > 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}
Pub Date : 2025-01-02DOI: 10.1016/j.eja.2024.127495
Qianyu Fan , Jiancheng Xie , Jintao Du , Huanyu Ge , Cuilan Wei , Hao Qian , Hai Liang , Jun Nie , Feng Hu , Songjuan Gao , Weidong Cao
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 (NMV) or rice straw N (NRS) 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 NRS uptake and NRS recovery of rice by 53.2 % and 53.4 %, respectively, and the NRS recovery in soil was increased by 55.4 %. This study concluded that co-incorporation of MV and RS facilitated the efficient utilization of NRS by increasing NRS uptake of rice and recovery in soil.
{"title":"Rice straw nitrogen can be utilized by rice more efficiently when co-incorporating with milk vetch","authors":"Qianyu Fan , Jiancheng Xie , Jintao Du , Huanyu Ge , Cuilan Wei , Hao Qian , Hai Liang , Jun Nie , Feng Hu , Songjuan Gao , 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<sub>MV</sub>) or rice straw N (N<sub>RS</sub>) to rice N uptake are still unclear. Two long-term field experiments and a <sup>15</sup>N 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 <sup>15</sup>N dual-label pot experiment indicated that compared to single RS, co-incorporation of MV and RS increased the N<sub>RS</sub> uptake and N<sub>RS</sub> recovery of rice by 53.2 % and 53.4 %, respectively, and the N<sub>RS</sub> recovery in soil was increased by 55.4 %. This study concluded that co-incorporation of MV and RS facilitated the efficient utilization of N<sub>RS</sub> by increasing N<sub>RS</sub> 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}
Pub Date : 2024-12-31DOI: 10.1016/j.eja.2024.127502
Carolina Fabbri , Antonio Delgado , Lorenzo Guerrini , Marco Napoli
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.
{"title":"Precision nitrogen fertilization strategies for durum wheat: a sustainability evaluation of NNI and NDVI map-based approaches","authors":"Carolina Fabbri , Antonio Delgado , Lorenzo Guerrini , 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}
Pub Date : 2024-12-27DOI: 10.1016/j.eja.2024.127500
Xiaoyue Wang , Xiaopeng Wu , Yongzhi Hua , Yuqing Li , Liangchuan Ma , Yihuang Gong , Wanchao Zhu , Shutu Xu , Jiquan Xue , Xiaoliang Qin , Kadambot H.M. Siddique
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.
{"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 , Xiaopeng Wu , Yongzhi Hua , Yuqing Li , Liangchuan Ma , Yihuang Gong , Wanchao Zhu , Shutu Xu , Jiquan Xue , Xiaoliang Qin , 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 ×10<sup>4</sup> plants/hm<sup>2</sup>), regular (6 ×10<sup>4</sup> plants/hm<sup>2</sup>), medium (7.5 ×10<sup>4</sup> plants/hm<sup>2</sup>), and high (9 ×10<sup>4</sup> plants/hm<sup>2</sup>) 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}
Pub Date : 2024-12-26DOI: 10.1016/j.eja.2024.127493
Michaela Pia Laumer , Adolf Kellermann , Franz-Xaver Maidl , Kurt-Jürgen Hülsbergen , Thomas Ebertseder
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 > 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.
{"title":"Influence of agronomic parameters and storage parameters on the frying color of French fry potatoes (Solanum tuberosum L.)","authors":"Michaela Pia Laumer , Adolf Kellermann , Franz-Xaver Maidl , Kurt-Jürgen Hülsbergen , 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 > 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}
Pub Date : 2024-12-26DOI: 10.1016/j.eja.2024.127494
Xuening Yang , Xuanze Zhang , Zhigan Zhao , Ning Ma , Jing Tian , Zhenwu Xu , Junmei Zhang , Yongqiang Zhang
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 (transp_eff_cf) (r = -0.66 and 0.63, p = 0.001 and 0.003, respectively) and grain growth rate (grin_gth_rate) (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 (tt_emerg_to_endjuv) shows varying sensitivity across years (STi = 0.03–0.26), influenced by maximum temperature. Our results demonstrated that transp_eff_cf and grain_gth_rate 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.
{"title":"Rainfall and maximum temperature are dominant climatic factors influencing APSIM-Maize cultivar parameters sensitivity in semiarid regions","authors":"Xuening Yang , Xuanze Zhang , Zhigan Zhao , Ning Ma , Jing Tian , Zhenwu Xu , Junmei Zhang , 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><sub><em>i</em></sub> = 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}
Pub Date : 2024-12-25DOI: 10.1016/j.eja.2024.127490
Shengli Liu , Tongtong Shi , Tong Li , Xinru You , Shuai Dai , Wenkui Wang , Zhanbiao Wang , Xiongfeng Ma
Spatial co-occurring crop yield failures strongly affect crop productivity, resulting in profound socioeconomic consequences. Cotton (Gossypium hirsutum L.) 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.
{"title":"Climate normals shape regional disparities of cotton yield failures compared to dominant impacts from climate extremes","authors":"Shengli Liu , Tongtong Shi , Tong Li , Xinru You , Shuai Dai , Wenkui Wang , Zhanbiao Wang , 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}
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