Pub Date : 2025-02-01DOI: 10.1016/j.eja.2025.127528
Jinrong Cui , Youliu Zhang , Hao Chen , Yaoxuan Zhang , Hao Cai , Yu Jiang , Ruijun Ma , Long Qi
Accurately acquiring the weed category information is a crucial and indispensable step for effective field management. However, most of weed recognition techniques mainly rely on either pure Convolutional Neural Network (CNN) or Transformer architectures. CNNs excel at extracting local features but struggle to capture global representations. On the other hand, Transformers can capture long-distance feature dependencies but often lose local feature details. These limitations result in suboptimal performances of existing weed recognition models. To address these challenges, this paper proposes a novel hybrid network model, coined as CSWin-MBConv. CSWin-MBConv combines CNN and Transformer architectures in parallel, with CNN branch used to extract local features and Transformer branch employed to capture global representations. In order to enhance the fusion of feature maps from these two branches, we customize the CBAM feature fusion module (CFFM), which facilitates the generation of more comprehensive feature representations. Extensive experiments demonstrate that CSWin-MBConv, whilst being more parameter- and computation-conserving, achieves superior recognition accuracy (98.50 %) and F1-score (98.56 %), outperforming the state-of-the-art CNN and Transformer architectures (e.g., EfficientNet and Swin Transformer). Taking the accuracy as well as the efficiency into account, our proposed model provides a practical support for weed management of paddy fields.
{"title":"CSWin-MBConv: A dual-network fusing CNN and Transformer for weed recognition","authors":"Jinrong Cui , Youliu Zhang , Hao Chen , Yaoxuan Zhang , Hao Cai , Yu Jiang , Ruijun Ma , Long Qi","doi":"10.1016/j.eja.2025.127528","DOIUrl":"10.1016/j.eja.2025.127528","url":null,"abstract":"<div><div>Accurately acquiring the weed category information is a crucial and indispensable step for effective field management. However, most of weed recognition techniques mainly rely on either pure Convolutional Neural Network (CNN) or Transformer architectures. CNNs excel at extracting local features but struggle to capture global representations. On the other hand, Transformers can capture long-distance feature dependencies but often lose local feature details. These limitations result in suboptimal performances of existing weed recognition models. To address these challenges, this paper proposes a novel hybrid network model, coined as CSWin-MBConv. CSWin-MBConv combines CNN and Transformer architectures in parallel, with CNN branch used to extract local features and Transformer branch employed to capture global representations. In order to enhance the fusion of feature maps from these two branches, we customize the CBAM feature fusion module (CFFM), which facilitates the generation of more comprehensive feature representations. Extensive experiments demonstrate that CSWin-MBConv, whilst being more parameter- and computation-conserving, achieves superior recognition accuracy (98.50 %) and F1-score (98.56 %), outperforming the state-of-the-art CNN and Transformer architectures (e.g., EfficientNet and Swin Transformer). Taking the accuracy as well as the efficiency into account, our proposed model provides a practical support for weed management of paddy fields.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127528"},"PeriodicalIF":4.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125278","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 : 2025-02-01DOI: 10.1016/j.eja.2025.127532
Yao Xiao , Wenqi Luo , Kejun Yang, Jian Fu, Peng Wang
Combined plow tillage and buried straw sustainably increases maize yields. However, how it regulates the temporal and spatial-temporal dynamics of soil properties, maize growth, and bacterial community assembly in semi-arid black soil agricultural fields remains unclear. Therefore, we aimed to evaluate the changes in soil properties, root growth, photosynthetic capacity, and bacterial assembly and their contributions to maize yield after 7 years of different straw-returning treatments. The experiment comprised no-tillage straw-mulching (NTSM), plow tillage with buried straw (PTBS), and rotary-tillage straw removal (RTS-). We used high-throughput sequencing to investigate the bacterial community structures and assembly in different seasons and soil depths and assessed soil properties, root growth, and photosynthetic capacity. Compared with the effects observed under NTSM, PTBS improved average 0–40 cm soil layer nutrient content, promoted root growth, and improved photosynthetic rate, increasing yield. NTSM and PTBS treatments significantly changed the soil bacterial community structure and increased the relative abundance of beneficial bacteria. PTBS treatment significantly enhanced carbon-nitrogen-related functional groups. PTBS microbial community showed high microbial diversity and highly deterministic bacterial assembly processes. The dominant genera and biomarkers enriched in the different treatments had similar correlated environmental factors but opposite correlation trends. Soil nutrients, root growth, and photosynthetic rate explained most of the variations in annual maize yield, while bacteria indirectly affected annual yield through nutrient and root characteristics. Our results indicate that soil nutrients, root growth, photosynthetic rate, and bacteria contribute to maize yield increase in plow tillage with buried straw treatment. NTSM only benefits the soil nutrients in the topsoil.
{"title":"Plow tillage with buried straw increases maize yield by regulating soil properties, root growth, photosynthetic capacity, and bacterial community assembly in semi-arid black soil farmlands","authors":"Yao Xiao , Wenqi Luo , Kejun Yang, Jian Fu, Peng Wang","doi":"10.1016/j.eja.2025.127532","DOIUrl":"10.1016/j.eja.2025.127532","url":null,"abstract":"<div><div>Combined plow tillage and buried straw sustainably increases maize yields. However, how it regulates the temporal and spatial-temporal dynamics of soil properties, maize growth, and bacterial community assembly in semi-arid black soil agricultural fields remains unclear. Therefore, we aimed to evaluate the changes in soil properties, root growth, photosynthetic capacity, and bacterial assembly and their contributions to maize yield after 7 years of different straw-returning treatments. The experiment comprised no-tillage straw-mulching (NTSM), plow tillage with buried straw (PTBS), and rotary-tillage straw removal (RTS-). We used high-throughput sequencing to investigate the bacterial community structures and assembly in different seasons and soil depths and assessed soil properties, root growth, and photosynthetic capacity. Compared with the effects observed under NTSM, PTBS improved average 0–40 cm soil layer nutrient content, promoted root growth, and improved photosynthetic rate, increasing yield. NTSM and PTBS treatments significantly changed the soil bacterial community structure and increased the relative abundance of beneficial bacteria. PTBS treatment significantly enhanced carbon-nitrogen-related functional groups. PTBS microbial community showed high microbial diversity and highly deterministic bacterial assembly processes. The dominant genera and biomarkers enriched in the different treatments had similar correlated environmental factors but opposite correlation trends. Soil nutrients, root growth, and photosynthetic rate explained most of the variations in annual maize yield, while bacteria indirectly affected annual yield through nutrient and root characteristics. Our results indicate that soil nutrients, root growth, photosynthetic rate, and bacteria contribute to maize yield increase in plow tillage with buried straw treatment. NTSM only benefits the soil nutrients in the topsoil.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127532"},"PeriodicalIF":4.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125013","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 : 2025-01-29DOI: 10.1016/j.eja.2025.127523
Talita Trapp , Jean Michel Moura-Bueno , Gustavo Nogara de Siqueira , Leandro Hahn , Danilo Eduardo Rozane , Antonio João de Lima Neto , William Natale , Arcângelo Loss , Gustavo Brunetto
Apple yield can be maximized through balanced nutritional management of orchards. This can be achieved by defining nutritional standards and nutrient reference values through multivariate database analysis. The objectives of the study were: i) to set and compare apple trees’ nutritional standards based on the Diagnosis and Recommendation Integrated System (DRIS) and on the Composition Nutritional Diagnosis (CND) and ii) to generate nutrients’ critical levels (CL) and sufficiency ranges (SR) in relation to the productivity of apple trees. The study was carried out in commercial Gala and Fuji cultivar orchards in Fraiburgo, Lebon Régis, Santa Cecília, and Monte Carlo municipalities - Santa Catarina State, Brazil. A database comprising information on fruit yield and leaf nutrient contents, from 10,179 observations performed from 2006 to 2021 was used in the study. According to the results, there is low association between mean nutrient balance index (NBIm) and nutritional imbalance index (CND-r²), and yield. DRIS and CND methods were effective in diagnosing apple trees’ nutritional status. Apple trees’ greatest nutritional requirements comprise the following macronutrients: nitrogen (N), potassium (K) and calcium (Ca), and the following micronutrients: manganese (Mn), iron (Fe) and zinc (Zn), all of them determined through the CND method. CL and SR established through the DRIS and CND methods were alike, but the best-adjusted SRs were found through CND: 22.1 – 26.4; 1.4 – 2.1; 11.5 – 15.8; 11.0 – 15.2; 2.9 – 4.0 g kg−1 and 70.2 – 116.7; 208.2 – 552.7; 5.8 – 8.6; 99.3 – 182.9; 31.4 – 41.9 mg kg−1 for N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and B, respectively, for the cv. Gala; and 24.3 – 28.5; 1.6 – 2.2; 10.7 – 15.0; 12.0 – 16.01; 2.7 – 3.8 g kg−1 and 75.9 – 125.6; 107.3 – 364.1; 6.1 – 9.1; 43.4 – 124.4; 32.8 – 44.3 mg kg−1 for N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and B, respectively, for the cv. Fuji. The use of the CL and SR standards established here will allow the rational use of fertilizers, but also the adequate nutritional balance of the orchards to achieve the greatest yield efficiency.
{"title":"Nutrients’ critical level propositions and sufficiency ranges aimed at high apple yield under subtropical climate","authors":"Talita Trapp , Jean Michel Moura-Bueno , Gustavo Nogara de Siqueira , Leandro Hahn , Danilo Eduardo Rozane , Antonio João de Lima Neto , William Natale , Arcângelo Loss , Gustavo Brunetto","doi":"10.1016/j.eja.2025.127523","DOIUrl":"10.1016/j.eja.2025.127523","url":null,"abstract":"<div><div>Apple yield can be maximized through balanced nutritional management of orchards. This can be achieved by defining nutritional standards and nutrient reference values through multivariate database analysis. The objectives of the study were: i) to set and compare apple trees’ nutritional standards based on the Diagnosis and Recommendation Integrated System (DRIS) and on the Composition Nutritional Diagnosis (CND) and ii) to generate nutrients’ critical levels (CL) and sufficiency ranges (SR) in relation to the productivity of apple trees. The study was carried out in commercial Gala and Fuji cultivar orchards in Fraiburgo, Lebon Régis, Santa Cecília, and Monte Carlo municipalities - Santa Catarina State, Brazil. A database comprising information on fruit yield and leaf nutrient contents, from 10,179 observations performed from 2006 to 2021 was used in the study. According to the results, there is low association between mean nutrient balance index (NBIm) and nutritional imbalance index (CND-r²), and yield. DRIS and CND methods were effective in diagnosing apple trees’ nutritional status. Apple trees’ greatest nutritional requirements comprise the following macronutrients: nitrogen (N), potassium (K) and calcium (Ca), and the following micronutrients: manganese (Mn), iron (Fe) and zinc (Zn), all of them determined through the CND method. CL and SR established through the DRIS and CND methods were alike, but the best-adjusted SRs were found through CND: 22.1 – 26.4; 1.4 – 2.1; 11.5 – 15.8; 11.0 – 15.2; 2.9 – 4.0 g kg<sup>−1</sup> and 70.2 – 116.7; 208.2 – 552.7; 5.8 – 8.6; 99.3 – 182.9; 31.4 – 41.9 mg kg<sup>−1</sup> for N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and B, respectively, for the cv. Gala; and 24.3 – 28.5; 1.6 – 2.2; 10.7 – 15.0; 12.0 – 16.01; 2.7 – 3.8 g kg<sup>−1</sup> and 75.9 – 125.6; 107.3 – 364.1; 6.1 – 9.1; 43.4 – 124.4; 32.8 – 44.3 mg kg<sup>−1</sup> for N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and B, respectively, for the cv. Fuji. The use of the CL and SR standards established here will allow the rational use of fertilizers, but also the adequate nutritional balance of the orchards to achieve the greatest yield efficiency.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127523"},"PeriodicalIF":4.5,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152266","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 : 2025-01-28DOI: 10.1016/j.eja.2025.127519
Shijin Yao , Bin Wang , De Li Liu , Siyi Li , Hongyan Ruan , Qiang Yu
Sugarcane is an important crop for global food and energy production. However, its production is greatly affected by inter-annual climate variations in major production regions. While previous studies have assessed climate impacts on sugarcane yield at individual sites, a regional-scale understanding of the climate-yield relationship remains unclear. Here, we collected historical sugarcane yields (1980–2022) and meteorological data from 23 sites across Australia’s eastern coastline sugarcane belt. Three statistical methods, random forest (RF), eXtreme gradient boosting regression (XGBoost), and multiple linear regression (MLR), were used to assess the impacts of climatic factors on sugarcane yield. The results showed that the machine learning methods, particularly RF, outperformed MLR in estimating sugarcane yield. The RF model explained 45–62 % of yield variations in Australia’s sugarcane regions based on climatic means and extreme climate indices. Growing season rainfall was identified as the most important factor influencing sugarcane yield in the Northern region, while CDD (consecutive dry days) was critical in the Central region, and TNn (minimum daily minimum temperature) was the dominant factor in the Southern region. Notably, the dominant factors exhibited a non-linear relationship with yield. In the Southern region, the lowest temperatures above 5 °C produced high yields. By contrast, in the Northern region, yields decreased with rainfall exceeding 1500 mm. Similarly, in the Central region, the increase in CDD substantially reduced yields, with yields reaching a low level after 70 days of CDD. To address these impacts, region-specific adaptation strategies are recommended, including the cultivation of waterlogging-tolerant crop varieties, the development of efficient irrigation systems, and the adoption of low-temperature-tolerant cultivars. This study highlights the critical importance of quantifying the contribution of climate variables to crop yield variability, thereby informing the development of effective, region-specific management practices.
{"title":"Assessing the impact of climate variability on Australia’s sugarcane yield in 1980–2022","authors":"Shijin Yao , Bin Wang , De Li Liu , Siyi Li , Hongyan Ruan , Qiang Yu","doi":"10.1016/j.eja.2025.127519","DOIUrl":"10.1016/j.eja.2025.127519","url":null,"abstract":"<div><div>Sugarcane is an important crop for global food and energy production. However, its production is greatly affected by inter-annual climate variations in major production regions. While previous studies have assessed climate impacts on sugarcane yield at individual sites, a regional-scale understanding of the climate-yield relationship remains unclear. Here, we collected historical sugarcane yields (1980–2022) and meteorological data from 23 sites across Australia’s eastern coastline sugarcane belt. Three statistical methods, random forest (RF), eXtreme gradient boosting regression (XGBoost), and multiple linear regression (MLR), were used to assess the impacts of climatic factors on sugarcane yield. The results showed that the machine learning methods, particularly RF, outperformed MLR in estimating sugarcane yield. The RF model explained 45–62 % of yield variations in Australia’s sugarcane regions based on climatic means and extreme climate indices. Growing season rainfall was identified as the most important factor influencing sugarcane yield in the Northern region, while CDD (consecutive dry days) was critical in the Central region, and TNn (minimum daily minimum temperature) was the dominant factor in the Southern region. Notably, the dominant factors exhibited a non-linear relationship with yield. In the Southern region, the lowest temperatures above 5 °C produced high yields. By contrast, in the Northern region, yields decreased with rainfall exceeding 1500 mm. Similarly, in the Central region, the increase in CDD substantially reduced yields, with yields reaching a low level after 70 days of CDD. To address these impacts, region-specific adaptation strategies are recommended, including the cultivation of waterlogging-tolerant crop varieties, the development of efficient irrigation systems, and the adoption of low-temperature-tolerant cultivars. This study highlights the critical importance of quantifying the contribution of climate variables to crop yield variability, thereby informing the development of effective, region-specific management practices.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127519"},"PeriodicalIF":4.5,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055401","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}
Rice is the world's most consumed staple food crop and there is a need to increase its yield in terms of food security. Understanding rice yields is important for farmers and national decision-making, and is critical for increasing yields. Remote sensing and machine learning have improved the accuracy and efficiency of yield monitoring. In particular, the combination of unmanned aerial vehicles (UAV) and convolutional neural networks (CNN), which is a type of deep learning, has been studied in recent years owing to its flexibility in data acquisition and high accuracy. Rice yield predictions using UAV and CNN have been reported to build more robust models after the mid-ripening stage. However, optimal input image conditions, such as the growth stage of image acquisition, spectral bands, and image cut-out areas, have not been studied, and there is room for improvement in this respect. In addition, recent efforts to find clues to improve the reliability and accuracy of advanced machine learning models have focused on explainable artificial intelligence (XAI), which attempts to reveal the basis of model inferences. However, there are almost no examples of using XAI for regression tasks with CNN in the research field of agricultural sciences. Therefore, in this study, the optimal input image conditions were investigated for the prediction of rice yield using a CNN based on UAV aerial images collected after the mid-ripening stage. An attempt was made to provide a rationale for the results by visualizing the region of interest in the CNN model. First, using red edge spectral bands at the maturity stage was more effective than at the mid-ripening stage. In addition, higher accuracy was achieved by allowing feature extraction from a slightly wider area than the actual harvested area, especially at the maturity stage. Furthermore, visualization of the region of interest showed that yield prediction was more focused on panicles at the maturity stage. This provided a relevant rationale for optimal input image conditions. In summary, this study identified the optimal input image conditions that enabled yield prediction with higher accuracy. Additionally, using XAI, which visualizes the region of interest, increases the trustworthiness of the model outputs. The results of this study will improve the accuracy and reliability of yield prediction models.
{"title":"A study on optimal input images for rice yield prediction models using CNN with UAV imagery and its reasoning using explainable AI","authors":"Tomoaki Yamaguchi , Taiga Takamura , Takashi S.T. Tanaka , Taiichiro Ookawa , Keisuke Katsura","doi":"10.1016/j.eja.2025.127512","DOIUrl":"10.1016/j.eja.2025.127512","url":null,"abstract":"<div><div>Rice is the world's most consumed staple food crop and there is a need to increase its yield in terms of food security. Understanding rice yields is important for farmers and national decision-making, and is critical for increasing yields. Remote sensing and machine learning have improved the accuracy and efficiency of yield monitoring. In particular, the combination of unmanned aerial vehicles (UAV) and convolutional neural networks (CNN), which is a type of deep learning, has been studied in recent years owing to its flexibility in data acquisition and high accuracy. Rice yield predictions using UAV and CNN have been reported to build more robust models after the mid-ripening stage. However, optimal input image conditions, such as the growth stage of image acquisition, spectral bands, and image cut-out areas, have not been studied, and there is room for improvement in this respect. In addition, recent efforts to find clues to improve the reliability and accuracy of advanced machine learning models have focused on explainable artificial intelligence (XAI), which attempts to reveal the basis of model inferences. However, there are almost no examples of using XAI for regression tasks with CNN in the research field of agricultural sciences. Therefore, in this study, the optimal input image conditions were investigated for the prediction of rice yield using a CNN based on UAV aerial images collected after the mid-ripening stage. An attempt was made to provide a rationale for the results by visualizing the region of interest in the CNN model. First, using red edge spectral bands at the maturity stage was more effective than at the mid-ripening stage. In addition, higher accuracy was achieved by allowing feature extraction from a slightly wider area than the actual harvested area, especially at the maturity stage. Furthermore, visualization of the region of interest showed that yield prediction was more focused on panicles at the maturity stage. This provided a relevant rationale for optimal input image conditions. In summary, this study identified the optimal input image conditions that enabled yield prediction with higher accuracy. Additionally, using XAI, which visualizes the region of interest, increases the trustworthiness of the model outputs. The results of this study will improve the accuracy and reliability of yield prediction models.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127512"},"PeriodicalIF":4.5,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055410","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 : 2025-01-26DOI: 10.1016/j.eja.2025.127517
Francesco Ferrero, Ernesto Tabacco, Gabriele Rolando, Giorgio Borreani
Intensive dairy farming relies on large inputs of N fertilizer and on the off-farm nitrogen to sustain a high milk output per hectare, which leads to an overuse of N, and to a reduction of Nitrogen Use Efficiency (NUE). This multiyear study aims to verify, through a Living Lab approach, on two commercial dairy farms in the Northern Italy, the effect of planning and managing of a forage system on N balance and NUE. Two periods (3 years each) before and after the changes in the farm and forage management were considered. The introduction of legume crops, double cropping, winter crops, and the adoption of early cutting of forages coupled with an efficient conservation of forages, were adopted on the farms. These actions have improved the uptake potential of the crops and the recycling of N from livestock to the forage system and back again. Changes in forage system management allowed to increase the average dry matter yield and N uptake per hectare on both farms, mainly due to the contribution of alfalfa, Italian ryegrass, and corn harvested as high moisture silage. The N output from cash crops, forages, and N input from nitrogen fertilizers were reduced on both farms, while the input from biological N fixation was increased. This resulted in a reduction of around 30 % of N surplus on the farms, and in a higher N efficiency. This study shows that milk production could be intensified, and nitrogen surplus could be reduced by acting on the management of the forage system and of conservation techniques to improve feed self-sufficiency, and by relying on the introduction of legume crops and on a reduction of off-farm nitrogen, through a synergistic Living Lab approach.
{"title":"Integrated forage system reduces off-farm purchased nitrogen and limit surplus on intensive dairy farms in northern Italy","authors":"Francesco Ferrero, Ernesto Tabacco, Gabriele Rolando, Giorgio Borreani","doi":"10.1016/j.eja.2025.127517","DOIUrl":"10.1016/j.eja.2025.127517","url":null,"abstract":"<div><div>Intensive dairy farming relies on large inputs of N fertilizer and on the off-farm nitrogen to sustain a high milk output per hectare, which leads to an overuse of N, and to a reduction of Nitrogen Use Efficiency (NUE). This multiyear study aims to verify, through a Living Lab approach, on two commercial dairy farms in the Northern Italy, the effect of planning and managing of a forage system on N balance and NUE. Two periods (3 years each) before and after the changes in the farm and forage management were considered. The introduction of legume crops, double cropping, winter crops, and the adoption of early cutting of forages coupled with an efficient conservation of forages, were adopted on the farms. These actions have improved the uptake potential of the crops and the recycling of N from livestock to the forage system and back again. Changes in forage system management allowed to increase the average dry matter yield and N uptake per hectare on both farms, mainly due to the contribution of alfalfa, Italian ryegrass, and corn harvested as high moisture silage. The N output from cash crops, forages, and N input from nitrogen fertilizers were reduced on both farms, while the input from biological N fixation was increased. This resulted in a reduction of around 30 % of N surplus on the farms, and in a higher N efficiency. This study shows that milk production could be intensified, and nitrogen surplus could be reduced by acting on the management of the forage system and of conservation techniques to improve feed self-sufficiency, and by relying on the introduction of legume crops and on a reduction of off-farm nitrogen, through a synergistic Living Lab approach.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127517"},"PeriodicalIF":4.5,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055408","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}
A current significant vineyard challenge is how other-than tillage soil management solutions can be implemented without incurring in major competition for water and nutrients with the consociated grapevines. Therefore, a three-year study was carried at two hilly sites (Sartori and Ottina) in Northern Italy to evaluate the impact of two novel winter cover crop terminations (interrow rolling, R, and sub-row mulching, SRM) on vine physiology and performance as compared to standard practice, either alternate tilled-grassed interrow and/or green manuring (GM). Moreover, at the Ottina site, cover crop sowing used two different seed mixtures: the cereal based Humusfert (HF) and the legume based Nitrofert (NF). Each season, in summer, soil and vine water status were measured as soil saturation degree (θs/θsat), pre-dawn (ΨPD) and midday leaf water potential (ΨMD), whereas gas exchange was monitored as leaf assimilation (A) and stomatal conductance (gs). At both sites, for data pooled over years, the amount of rolled dry biomass was lower than 650 g m−2 and R did not result in any significant variation of water status, gas exchange and vine performance versus the standard practice. Vice versa, at the Sartori site, the amount of mulched biomass under the row was quite abundant (1.517 g m−2 dry weight) and effective for weed suppression. SRM also maintained less negative ΨPD in the warmest part of 2022 and 2023 seasons without, however, greatly impacting leaf water status, gas exchange and vine performances. At Ottina, SRM assured, for data pooled over years, higher total soluble solids (TSS) and malic acid and lower pH than R suggesting that sugar accumulation and acid degradation were partially decoupled. At the same site, NF allowed to increase yield without any concurrent change in vigor with a consequent ripening delay in terms of lower TSS, pH and anthocyanins, and higher tartaric acid at harvest, a sought effects under a global warming scenario. It is also practically relevant that HF lowered berry [K] significantly vs. NF suggesting that it might be used as an additional control tool to limit K accumulation into the berries and, eventually, wine pH. Overall, while SRM had better performance than R in terms of weed suppression, soil water status and berry ripening, results can vary as a function of the type of seed mixture which, in turn, closely interacts with specific environmental conditions.
{"title":"Novel termination techniques of winter cover crops in the vineyard: Effects on physiology and performance of Pinot Noir and Malvasia di Candia aromatica grapevines","authors":"Leonardo Cunial , Irene Diti , Paolo Bonini , Rachele Patelli , Matteo Gatti , Gabriele Cola , Massimiliano Bordoni , Thi Ngoc Anh Nguyen , Claudia Meisina , Roberto Confalonieri , Livia Paleari , Stefano Poni","doi":"10.1016/j.eja.2025.127514","DOIUrl":"10.1016/j.eja.2025.127514","url":null,"abstract":"<div><div>A current significant vineyard challenge is how other-than tillage soil management solutions can be implemented without incurring in major competition for water and nutrients with the consociated grapevines. Therefore, a three-year study was carried at two hilly sites (Sartori and Ottina) in Northern Italy to evaluate the impact of two novel winter cover crop terminations (interrow rolling, R, and sub-row mulching, SRM) on vine physiology and performance as compared to standard practice, either alternate tilled-grassed interrow and/or green manuring (GM). Moreover, at the Ottina site, cover crop sowing used two different seed mixtures: the cereal based Humusfert (HF) and the legume based Nitrofert (NF). Each season, in summer, soil and vine water status were measured as soil saturation degree (θs/θsat), pre-dawn (Ψ<sub>PD</sub>) and midday leaf water potential (Ψ<sub>MD</sub>), whereas gas exchange was monitored as leaf assimilation (A) and stomatal conductance (g<sub>s</sub>). At both sites, for data pooled over years, the amount of rolled dry biomass was lower than 650 g m<sup>−2</sup> and R did not result in any significant variation of water status, gas exchange and vine performance versus the standard practice. Vice versa, at the Sartori site, the amount of mulched biomass under the row was quite abundant (1.517 g m<sup>−2</sup> dry weight) and effective for weed suppression. SRM also maintained less negative Ψ<sub>PD</sub> in the warmest part of 2022 and 2023 seasons without, however, greatly impacting leaf water status, gas exchange and vine performances. At Ottina, SRM assured, for data pooled over years, higher total soluble solids (TSS) and malic acid and lower pH than R suggesting that sugar accumulation and acid degradation were partially decoupled. At the same site, NF allowed to increase yield without any concurrent change in vigor with a consequent ripening delay in terms of lower TSS, pH and anthocyanins, and higher tartaric acid at harvest, a sought effects under a global warming scenario. It is also practically relevant that HF lowered berry [K] significantly vs. NF suggesting that it might be used as an additional control tool to limit K accumulation into the berries and, eventually, wine pH. Overall, while SRM had better performance than R in terms of weed suppression, soil water status and berry ripening, results can vary as a function of the type of seed mixture which, in turn, closely interacts with specific environmental conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127514"},"PeriodicalIF":4.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152630","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 : 2025-01-22DOI: 10.1016/j.eja.2025.127521
Quanyi Hu , Jun Yang , Zhanpeng Chen , Xuelin Zhang , Chengfang Li
The economic benefits of rice–wheat (RW) and rice–oilseed rape (RO) rotation in China are low. By contrast, the rice–edible mushroom Stropharia rugosoannulata (RE) rotation yields significantly higher economic benefits than RW and RO rotations. Furthermore, RE rotation can avoid air pollution caused by rice straw burning and has been widely adopted in China. Nevertheless, it remains unclear how the rotation affects CH4 and N2O emissions and global warming potential. Herein, three rice-based rotations, including RW, RO and RE rotations, were conducted in central China. The RE rotation resulted in the lowest CH4 emission from the winter crop season as well as the lowest annual N2O emission from the rice seasons among the three rotations. Moreover, compared with the RW and RO rotations, the RE rotation significantly increased the soil organic carbon content by 30.2 % and 31.2 %, and the rice yield by 16.0 % and 17.0 %, respectively. Hence, the RE rotation significantly reduced the net global warming potential by 2008.4 % and 696.5 % compared with the RW and RO rotations, respectively. Furthermore, the RE rotation improved soil fertility compared with the other two rotations. Although the RE rotation required the highest agricultural input among the three rotations, it contributed to the highest net ecosystem economic profits owing to its highest agricultural income and lowest environmental damage cost. Thus, RE rotation is an effective rice-based rotation that can use rice straws to reduce the net global warming potential and increase economic benefits and soil fertility. Therefore, RE rotation may serve as an alternative strategy for achieving sustainable agricultural production in winter fallow areas of the rice-upland region in Yangtze River Basin, China.
{"title":"Rice-edible mushroom Stropharia rugosoannulata rotation mitigates net global warming potential while enhancing soil fertility and economic benefits","authors":"Quanyi Hu , Jun Yang , Zhanpeng Chen , Xuelin Zhang , Chengfang Li","doi":"10.1016/j.eja.2025.127521","DOIUrl":"10.1016/j.eja.2025.127521","url":null,"abstract":"<div><div>The economic benefits of rice–wheat (RW) and rice–oilseed rape (RO) rotation in China are low. By contrast, the rice–edible mushroom <em>Stropharia rugosoannulata</em> (RE) rotation yields significantly higher economic benefits than RW and RO rotations. Furthermore, RE rotation can avoid air pollution caused by rice straw burning and has been widely adopted in China. Nevertheless, it remains unclear how the rotation affects CH<sub>4</sub> and N<sub>2</sub>O emissions and global warming potential. Herein, three rice-based rotations, including RW, RO and RE rotations, were conducted in central China. The RE rotation resulted in the lowest CH<sub>4</sub> emission from the winter crop season as well as the lowest annual N<sub>2</sub>O emission from the rice seasons among the three rotations. Moreover, compared with the RW and RO rotations, the RE rotation significantly increased the soil organic carbon content by 30.2 % and 31.2 %, and the rice yield by 16.0 % and 17.0 %, respectively. Hence, the RE rotation significantly reduced the net global warming potential by 2008.4 % and 696.5 % compared with the RW and RO rotations, respectively. Furthermore, the RE rotation improved soil fertility compared with the other two rotations. Although the RE rotation required the highest agricultural input among the three rotations, it contributed to the highest net ecosystem economic profits owing to its highest agricultural income and lowest environmental damage cost. Thus, RE rotation is an effective rice-based rotation that can use rice straws to reduce the net global warming potential and increase economic benefits and soil fertility. Therefore, RE rotation may serve as an alternative strategy for achieving sustainable agricultural production in winter fallow areas of the rice-upland region in Yangtze River Basin, China.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127521"},"PeriodicalIF":4.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152631","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}
The content of phenolic compounds in olive fruits is a matter of interest, not only because of their contribution to olive oil quality but also to their beneficial effects on human health. While some studies mention genetic and agronomic factors affecting the olive fruit phenolic composition, there is still a lack of information on the role of the environmental growth temperature. This study addresses the impact of different thermal regimes on hydrophilic phenol contents from two olive cultivars (Arbequina and Coratina) growing at several environments in Argentina. The variability associated with the growing environment was significant for all compounds analyzed; lower total phenol contents were associated with warmer environments. Verbascoside and oleuropein aglycone were the compounds reflecting more clearly this general tendency; their contents were approximately 2–3 fold lower in the warmest than in the coldest environment. To assess relationships between thermal records and phenolic contents, various models were tested; those including the thermal time showed the best fit. In general, data from cv. Arbequina showed better fit than those from cv. Coratina. As a summary, a genotype-associated response is suggested whereby the tested cultivars would have the ability to accumulate higher amounts of total and specific phenols when grown in cooler environments.
{"title":"Phenolic content and profile of olive fruits: Impact of contrasting thermal regimes in non-Mediterranean growing environments","authors":"Pierluigi Pierantozzi , Mariela Torres , Cibeles Contreras , Vitale Stanzione , Martín Tivani , Luciana Gentili , Valerio Mastio , Peter Searles , Magdalena Brizuela , Fabricio Fernández , Alejandro Toro , Carlos Puertas , Eduardo Trentacoste , Juan Kiessling , Marina Bufacchi , Fiammetta Alagna , Ornella Calderini , María Cristina Valeri , Luciana Baldoni , Damián Maestri","doi":"10.1016/j.eja.2025.127506","DOIUrl":"10.1016/j.eja.2025.127506","url":null,"abstract":"<div><div>The content of phenolic compounds in olive fruits is a matter of interest, not only because of their contribution to olive oil quality but also to their beneficial effects on human health. While some studies mention genetic and agronomic factors affecting the olive fruit phenolic composition, there is still a lack of information on the role of the environmental growth temperature. This study addresses the impact of different thermal regimes on hydrophilic phenol contents from two olive cultivars (Arbequina and Coratina) growing at several environments in Argentina. The variability associated with the growing environment was significant for all compounds analyzed; lower total phenol contents were associated with warmer environments. Verbascoside and oleuropein aglycone were the compounds reflecting more clearly this general tendency; their contents were approximately 2–3 fold lower in the warmest than in the coldest environment. To assess relationships between thermal records and phenolic contents, various models were tested; those including the thermal time showed the best fit. In general, data from cv. Arbequina showed better fit than those from cv. Coratina. As a summary, a genotype-associated response is suggested whereby the tested cultivars would have the ability to accumulate higher amounts of total and specific phenols when grown in cooler environments.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127506"},"PeriodicalIF":4.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020051","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 : 2025-01-20DOI: 10.1016/j.eja.2025.127513
Shuang Lei , Sajjad Raza , Annie Irshad , Yun Jiang , Ahmed Salah Elrys , Zhujun Chen , Jianbin Zhou
Inefficient nitrogen (N) utilizing cropping systems add substantial amount of unconsumed N fertilizer in the soil profile as residual N. High nitrate accumulation in the soil profile is detrimental to soil health and significantly contributes to groundwater pollution. This study investigated the long-term fate of residual N in the deep soil profile, including its contributions to crop yield and nitrogen recovery efficiency. A field experiment was carried out for seven years comprising winter wheat and summer maize as an annual rotation. For the initial two years (2015–2017) N fertilizer was applied at two rates (N340 including 160 kg N ha1 for wheat and 180 kg N ha1 for maize, N500 including 220 kg N ha1 for wheat and 280 kg N ha1 for maize) with and without nitrification inhibitor (dicyandiamide, DCD). While for the next five years (2017–2022) the crops were grown without N fertilization. N fertilization increased grain yield compared to control, and DCD addition caused additional increases in yield mainly at 500 kg N ha1. The yield did not decrease in the first year without fertilization but started to decrease considerably in later years. N fertilization increased grain N uptake by more than 100 % in all treatments in fertilized years and the first unfertilized year compared to control but decreased strongly in the second year without fertilization. The cumulative fertilizer N recovery did not differ among N fertilized treatments during fertilized years but increased strongly when N uptake under unfertilized years were incorporated. The cumulative fertilizer N recovery in N340 + DCD increased from 37 %–44 % in fertilized years to 74 %–80 % in unfertilized years. A substantial residual nitrate accumulation (237–489 kg N ha1) accumulated in the soil profile (0–200 cm) after two years of fertilization. Crops grown during unfertilized years fulfilled the majority of their N requirements from residual nitrate as indicated through considerable decrease in residual N accumulation (31 %–41 %) in the first unfertilized year. Soil N accumulation became less than 60 kg ha1 after growing crops without fertilization for four years with no difference among treatments including control. This study highlights that residual N is an important component of crop N uptake and should therefore be used to correct N application rates to avoid overuse of N fertilizers.
利用种植制度的低效氮肥使大量未消耗的氮肥在土壤剖面中作为残余氮添加。土壤剖面中硝酸盐的高积累不利于土壤健康，并显著导致地下水污染。本研究探讨了深层土壤剖面中残余氮的长期命运，包括其对作物产量和氮恢复效率的贡献。进行了为期7年的田间试验，其中冬小麦和夏玉米每年轮作一次。在前两年（2015-2017），施氮量为两种（N340，小麦施氮量为160 kg ，玉米施氮量为180 kg ，N500，小麦施氮量为220 kg ，玉米施氮量为280 kg ），有和没有硝化抑制剂（双氰胺，DCD）。而在接下来的五年（2017-2022年）里，这些作物在种植时不施用氮肥。与对照相比，施氮增加了籽粒产量，DCD的额外增加主要在500 kg N ha1。在不施肥的第一年产量没有下降，但在随后的年份开始明显下降。与对照相比，施氮在施肥年和未施肥第1年使各处理的籽粒吸氮量增加了100 %以上，但在未施肥的第二年则大幅下降。氮肥累积恢复率在各施肥处理间无显著差异，但在未施肥处理下结合氮素吸收时显著提高。N340 + DCD的累积氮肥回收率由施肥年的37 % ~ 44 %提高到未施肥年的74 % ~ 80 %。施肥2年后，土壤剖面（0 ~ 200 cm）中残留硝酸盐积累量显著（237 ~ 489 kg N ha1）。在未施肥年份生长的作物通过残余硝酸盐满足了大部分的氮需求，这表明在第一个未施肥年份，残余氮积累显著减少（31% % - 41% %）。作物不施肥4年后，土壤氮素积累量低于60 kg ha1，包括对照在内的处理间无差异。本研究强调，残氮是作物氮吸收的重要组成部分，因此应用于校正施氮量，以避免氮肥的过度使用。
{"title":"Long-term legacy impacts of nitrogen fertilization on crop yield, nitrate accumulation, and nitrogen recovery efficiency","authors":"Shuang Lei , Sajjad Raza , Annie Irshad , Yun Jiang , Ahmed Salah Elrys , Zhujun Chen , Jianbin Zhou","doi":"10.1016/j.eja.2025.127513","DOIUrl":"10.1016/j.eja.2025.127513","url":null,"abstract":"<div><div>Inefficient nitrogen (N) utilizing cropping systems add substantial amount of unconsumed N fertilizer in the soil profile as residual N. High nitrate accumulation in the soil profile is detrimental to soil health and significantly contributes to groundwater pollution. This study investigated the long-term fate of residual N in the deep soil profile, including its contributions to crop yield and nitrogen recovery efficiency. A field experiment was carried out for seven years comprising winter wheat and summer maize as an annual rotation. For the initial two years (2015–2017) N fertilizer was applied at two rates (N340 including 160 kg N ha<sup><img>1</sup> for wheat and 180 kg N ha<sup><img>1</sup> for maize, N500 including 220 kg N ha<sup><img>1</sup> for wheat and 280 kg N ha<sup><img>1</sup> for maize) with and without nitrification inhibitor (dicyandiamide, DCD). While for the next five years (2017–2022) the crops were grown without N fertilization. N fertilization increased grain yield compared to control, and DCD addition caused additional increases in yield mainly at 500 kg N ha<sup><img>1</sup>. The yield did not decrease in the first year without fertilization but started to decrease considerably in later years. N fertilization increased grain N uptake by more than 100 % in all treatments in fertilized years and the first unfertilized year compared to control but decreased strongly in the second year without fertilization. The cumulative fertilizer N recovery did not differ among N fertilized treatments during fertilized years but increased strongly when N uptake under unfertilized years were incorporated. The cumulative fertilizer N recovery in N340 + DCD increased from 37 %–44 % in fertilized years to 74 %–80 % in unfertilized years. A substantial residual nitrate accumulation (237–489 kg N ha<sup><img>1</sup>) accumulated in the soil profile (0–200 cm) after two years of fertilization. Crops grown during unfertilized years fulfilled the majority of their N requirements from residual nitrate as indicated through considerable decrease in residual N accumulation (31 %–41 %) in the first unfertilized year. Soil N accumulation became less than 60 kg ha<sup><img>1</sup> after growing crops without fertilization for four years with no difference among treatments including control. This study highlights that residual N is an important component of crop N uptake and should therefore be used to correct N application rates to avoid overuse of N fertilizers.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127513"},"PeriodicalIF":4.5,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020055","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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