European Journal of Agronomy最新文献

{"title":"Controlled release urea combined with normal urea maintains the N balance and improves the environmental and economic benefits in wheat–maize multiple cropping","authors":"Shiju Liu ,&nbsp;Yongqi Li ,&nbsp;Yaru Zhang ,&nbsp;Lijin Chen ,&nbsp;Tao Wang ,&nbsp;Hongxia Li ,&nbsp;Yuncheng Liao ,&nbsp;Yajun Li ,&nbsp;Guangxin Zhang ,&nbsp;Juan Han","doi":"10.1016/j.eja.2024.127446","DOIUrl":"10.1016/j.eja.2024.127446","url":null,"abstract":"<div><div>Controlled release urea combined with normal urea (CRUNU) can potentially improve crop yields and reduce the associated environmental risk. However, the effects of CRUNU on farmland environmental benefits and the agroecosystem nitrogen (N) balance have not been evaluated in the winter wheat–summer maize multiple cropping system in northwest China, and few studies have quantified the impacts of CRUNU on N losses with this cropping system based on life cycle assessment. Therefore, we performed a field experiment for two years during 2020–2022 with two types of N fertilizer (normal urea (NU) and CRUNU) and at three N application rates (low: 135 kg N ha^–1, medium: 180 kg N ha^–1, and high: 225 kg N ha^–1) at Caoxinzhuang experimental farm to comprehensively evaluate the effects of CRUNU on the agronomic, N balance, environmental, and economic benefits in winter wheat–summer maize cropping. Compared with NU, CRUNU helped to synchronize the N supply and demand for wheat and maize, and under all three N application rates, the annual average grain yield and grain N uptake increased with CRUNU, as well as reducing the volatilization of NH₃ by 16.69 %, N₂O emissions by 25.16 %, and N losses due to nitrate (NO₃^–) leaching by 44.23–61.65 %, thereby maintaining the total N storage. Considering both the N input and output, CRUNU achieved a lower N surplus than NU and maintained the N balance in the farmland ecosystem. In addition, CRUNU significantly reduced the reactive N losses at the three N application rates to decrease the N footprint (NF) by 25.48–42.85 %, where CRUNU obtained the lowest NF at the medium N application rate. More importantly, the benefits of CRUNU for increasing the grain yield at different N application rates offset the higher agricultural input costs and reduced the environmental costs due to N₂O emissions, NH₃ volatilization, and NO₃^– leaching losses, so the net benefits increased by 23.14–29.25 %. Furthermore, the net benefits under CRUNU did not differ significantly at the medium and high N application rates. Therefore, we recommend CRUNU application at the medium rate as an effective strategy for improving the N balance, environmental effects, and economic benefits in wheat–maize multiple cropping systems</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127446"},"PeriodicalIF":4.5,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-benefits of a customized nutrient management approach tailored to smallholder farming for cabbage (Brassica oleracea L.)","authors":"Mengjiao Liu ,&nbsp;Binggeng Yang ,&nbsp;Xiya Wang ,&nbsp;Xinpeng Xu ,&nbsp;Wencheng Ding ,&nbsp;Ping He ,&nbsp;Wei Zhou","doi":"10.1016/j.eja.2024.127453","DOIUrl":"10.1016/j.eja.2024.127453","url":null,"abstract":"<div><div>The inappropriate use of fertilizers in cabbage (<em>Brassica oleracea</em> L.) production is widespread worldwide; however, there are few easily implementable methods of fertilizer application rates suitable for smallholders. We established a nutrient expert system for cabbage (NEc) using data collected in China’s cabbage-growing regions from 2000 to 2023. The NEc addressed issues related to nutrient-application imbalances and excessive fertilization by optimizing N, P₂O₅, and K₂O usage based on yield responses, agronomic efficiency, and nutrient uptake. Additionally, field experiments were conducted to assess the utility of NEc in terms of yields, economic benefits, and nutrient-recovery efficiency compared to farmers’ practices (FP). The resulting database revealed a significant quadratic relationship (<em>P</em> &lt; 0.05) between the yield response and agronomic efficiency. Quantitative evaluation of the fertility of tropical soils model, used to simulate optimal nutrient demands, reveals that the simulated nutrient requirements for N, P, and K increase linearly as the yield increases when the target yield is within 70 % of potential yield. In other words, to produce 1 Mg of cabbage, it requires 2.46 kg of N, 0.33 kg of P and 2.26 kg of K. The statistical results of collected data showed that optimal fertilization significantly (<em>P</em> &lt; 0.05) enhanced cabbage yield, nutrient utilization efficiency, and net benefit. It was observed that fertilizer application rate exerted a direct and positive impact on these parameters. Field verification experiment demonstrated that NEc led to co-benefits, including a 7 % increase in yield, a 15.2 % increase in net profit, and improved agronomic efficiency (14.7 %∼101.2 %) compared to FP. The NEc approach enabled optimization of fertilizer applications based on specific production conditions, thereby enhancing cabbage yield, economic benefits, and nutrient-recovery efficiency. Thus, the NEc approach developed in this study was superior over traditional fertilization methods and is highly suitable for small-scale cabbage farming.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127453"},"PeriodicalIF":4.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive review on technological breakthroughs in precision agriculture: IoT and emerging data analytics","authors":"Anil Kumar Saini ,&nbsp;Anshul Kumar Yadav ,&nbsp;Dhiraj","doi":"10.1016/j.eja.2024.127440","DOIUrl":"10.1016/j.eja.2024.127440","url":null,"abstract":"<div><div>Rapid population expansion has led to a corresponding rise in the demand for sustenance. Researchers have found that traditional agricultural practices are insufficient to meet the demands of commodities, and their inefficiency poses the most pressing obstacle to addressing the growing global food demand. Precision agriculture (PA) is an advanced hierarchy farming system supported by multidisciplinary technologies such as specialized sensors, communication protocols, algorithms, and management tools, helping mitigate the problems of conventional farming by ensuring maximum production and minimum wastage. Given the rapid evolution of the aforementioned multidisciplinary technologies, this review paper analyzed 24337 research documents from 1938 to April 2024 using bibliographical software from the Scopus dataset. Internet of Things (IoT), Agriculture Robots (AR), and Artificial Intelligence (AI) are currently driving ongoing research, with frequency occurrences of 12.245, 8.259, and 7.791, highlighting the trend towards interconnected farming systems and data-driven automated systems. Bibliographical evidence indicates the current utilization of AI, AR, and IoT for accurate assessments like crop yield prediction, disease and weed detection, and soil analysis. Additionally, China is the most productive country in terms of publication, while the United States leads in terms of patents. This review paper also explores emerging trends that could guide future research, including blockchain technology, big data analysis, computing paradigms, and drone technology. Subsequently, a PA framework has been suggested to facilitate innovation in this field, followed by the open issues, highlighting the ongoing concerns related to insufficient infrastructure, integration, cost, and security measures, with the aim to engage all stakeholders.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127440"},"PeriodicalIF":4.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lodging dynamics and seed yield for two soybean genotypes with contrasting lodging-susceptibility","authors":"Guido Di Mauro,&nbsp;José L. Rotundo","doi":"10.1016/j.eja.2024.127445","DOIUrl":"10.1016/j.eja.2024.127445","url":null,"abstract":"<div><div>Plant lodging prior to harvest is a potential yield limiting factor in soybean production, especially in high-yield, irrigated environments. The mechanism(s) through which lodging limits yield, and the benefits of lodging resistant genotypes are not clearly understood. The objectives of this study were (i) to measure temporal lodging dynamics of two soybean genotypes with contrasting lodging resistance under irrigated conditions, and (ii) to quantify the effect of lodging on soybean yield and yield components. To address these objectives, ACA530 (lodging susceptible) and SRM5001 (lodging resistant) in combinations with two lodging treatments (unstaked and staked plots to reduce lodging) were evaluated during two years under irrigated conditions. We evaluated temporal lodging dynamics by recording 3D coordinates of all nodes per plant and estimated a quantitative lodging ratio. The lodging resistant genotype did not lodge either year while the susceptible genotype, between R1-R3 in year 2 and between R3-R5 in year 1. While stakes within the canopy reduced lodging of the susceptible genotype there was not full control, and this was specifically noted in year 2. The lodging resistant genotype produced a yield 38 % greater than the lodging susceptible genotype through increased seed number (p&lt;0.001) and total biomass at maturity (p&lt;0.001). Interestingly, while the staked treatment reduced lodging of the susceptible genotype there was no yield improvement suggesting that the reduced yield of this genotype is not mechanistically associated with lodging. In this limited dataset, the two important contributions are: i) a methodology to manipulate and measure soybean lodging and, ii) that yield formation is not affected negatively when lodging occurs.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127445"},"PeriodicalIF":4.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphology-based weed type recognition using Siamese network","authors":"A.S.M. Mahmudul Hasan ,&nbsp;Dean Diepeveen ,&nbsp;Hamid Laga ,&nbsp;Michael G.K. Jones ,&nbsp;A.A.M. Muzahid ,&nbsp;Ferdous Sohel","doi":"10.1016/j.eja.2024.127439","DOIUrl":"10.1016/j.eja.2024.127439","url":null,"abstract":"<div><div>Automatic weed detection and classification can significantly reduce weed management costs and improve crop yields and quality. Weed detection in crops from imagery is inherently a challenging problem. Because both weeds and crops are of similar colour (green on green), their growth and texture are somewhat similar; weeds also vary based on crops, geographical locations, seasons and even weather patterns. This study proposes a novel approach utilising object detection and meta-learning techniques for generalised weed detection, transcending the limitations of varying field contexts. Instead of classifying weeds by species, this study classified them based on their morphological families aligned with farming practices. An object detector, e.g., a YOLO (You Only Look Once) model is employed for plant detection, while a Siamese network, leveraging state-of-the-art deep learning models as its backbone, is used for weed classification. This study repurposed and used three publicly available datasets, namely, Weed25, Cotton weed and Corn weed data. Each dataset contained multiple species of weeds, whereas this study grouped those into three classes based on the weed morphology. YOLOv7 achieved the best result as a plant detector, and the VGG16 model as the feature extractor for the Siamese network. Moreover, the models were trained on one dataset (Weed25) and applied to other datasets (Cotton weed and Corn weed) without further training. The study also observed that the classification accuracy of the Siamese network was improved using the cosine similarity function for calculating contrastive loss. The YOLOv7 models obtained the mAP of 91.03 % on the Weed25 dataset, which was used for training the model. The mAPs for the unseen datasets were 84.65 % and 81.16 %. As mentioned earlier, the classification accuracies with the best combination were 97.59 %, 93.67 % and 93.35 % for the Weed25, Cotton weed and Corn weed datasets, respectively. This study also compared the classification performance of our proposed technique with the state-of-the-art Convolutional Neural Network models. The proposed approach advances weed classification accuracy and presents a viable solution for dataset independent, i.e., site-independent weed detection, fostering sustainable agricultural practices.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127439"},"PeriodicalIF":4.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The nitrogen supply capacity and application methods of straw-chemical mixed fertilizer in the sweet corn variety ‘Zhetian 19’","authors":"Junfeng Hou ,&nbsp;Bin Chen ,&nbsp;Ping Zhang ,&nbsp;Yanli Wang ,&nbsp;Heping Tan ,&nbsp;Hailiang Han ,&nbsp;Fei Bao ,&nbsp;Fucheng Zhao","doi":"10.1016/j.eja.2024.127438","DOIUrl":"10.1016/j.eja.2024.127438","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The global production of crop straw has been steadily increasing as the demand for crops continues to grow, with current output reaching approximately 4 billion tons annually. Crop straw is a nutrient-rich resource, but if not properly managed, it can pose environmental risks. Effective utilization of straw remains a significant challenge in agricultural production. To address environmental issues such as pollution from straw burning, soil degradation, low crop germination rates, and the increase in soil-borne diseases, this study adopts the \"organic-inorganic granular fertilizer\" method. By converting straw into granulated fertilizer and returning it to the field, this approach not only repurposes agricultural waste but also enhances soil quality and crop yields. A three-year field experiment (2020–2022) was conducted to investigate the effects of various application rates of SCMF (Straw Chemical Mixed Fertilizer) and optimal fertilization methods on the photosynthetic process, yield, soil nutrients, and sugar content of sweet corn. In 2020, SCMF and urea were applied to plots according to different fertilization methods and rates: S0, SUT0.5, SUT, SUB0.5, SUB, CK0, and CK. In 2021, based on the optimal fertilization rate identified in 2020, different fertilization methods were tested: SUT, SUB0.5UT0.5, SUB, CK0, and CK.In 2022, under the optimal fertilization method, SCMF application rates were adjusted according to a 10 % variation in nitrogen fertilizer content: S1.2UB, S1.1UB, SUB, S0.9UB, S0, CK0, and CK.Considering the chlorophyll content, leaf area index, dry matter accumulation, yield, soil nutrient status, and sugar concentration in sweet corn from 2020 to 2022, the SUB treatment demonstrated superior performance. Compared to CK (247.2 kg N ha&lt;sup&gt;−1&lt;/sup&gt;), the SUB treatment (229.2 kg N ha&lt;sup&gt;−1&lt;/sup&gt;) enhanced both the yield and quality of sweet corn, while SCMF applications led to an increase in sugar content. In 2022, the SUB treatment resulted in a 9.5 % increase in chlorophyll content, and the leaf area index at 10 days after planting (DAP) was the highest observed. This increase in leaf area index contributed to a higher accumulation of dry matter (6.3 %) and ultimately led to an 8.7 % increase in sweet corn yield and a 9.7 % increase in soluble sugar content. The findings suggest that the SUB fertilization rate and method are optimal for achieving higher chlorophyll content, leaf area index, yield, and soluble sugar concentration in sweet corn. Additionally, soil nutrient analyses indicated that SCMF applications improved soil pH, total nitrogen, and organic matter content.Therefore, the SUB treatment resulted in increased chlorophyll content and leaf area index, enhancing photosynthetic efficiency and providing a larger area for dry matter accumulation and yield. The application of SUB reduced nitrogen fertilizer input by 20 % while increasing sweet corn yield, contributing to higher agricultural productivity and off","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127438"},"PeriodicalIF":4.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous canola windrowing and herbicide treatment improve the production of sequenced winter wheat","authors":"Brian L. Beres ,&nbsp;Zhijie Wang ,&nbsp;Ramona M. Mohr ,&nbsp;Charles M. Geddes ,&nbsp;Christian Willenborg ,&nbsp;Breanne D. Tidemann ,&nbsp;William May ,&nbsp;Hiroshi Kubota ,&nbsp;Sheryl A. Tittlemier","doi":"10.1016/j.eja.2024.127437","DOIUrl":"10.1016/j.eja.2024.127437","url":null,"abstract":"<div><div>In the context of canola (<em>Brassica napus</em> L.)-winter wheat (<em>Triticum aestivum</em> L.) rotational systems, the timing of canola stubble availability and effective weed management play a crucial role in the production of a subsequent winter wheat phase. This study, conducted from 2018 to 2022 across the Canadian prairies, applied a genotype × environment × management framework to examine how manipulations to canola harvest management can help optimize winter wheat production. The factorial treatment structure included two canola hybrids (early- and late-maturing), three canola harvest management systems (early-timing and conventional windrowing at 40 % and 60 % seed color change, respectively, and straight-cutting at 10 % seed moisture), and three weed management treatments (pre-harvest herbicide for canola, pre-plant herbicide for winter wheat, and pre-harvest+pre-plant herbicides). Windrowing and pre-harvest herbicides were completed simultaneously by retrofitting the swather with an onboard sprayer. Across all 16 site-years, winter wheat planted after a late-maturing canola hybrid demonstrated comparable performance to that after early-maturing canola. However, delaying canola harvest reduced winter wheat yields. Conventional windrowing in conjunction with pre-harvest herbicide or pre-harvest+pre-plant herbicides improved winter wheat yields and enhanced weed management, while maintaining canola seed quality, as no herbicide residues were detected in the harvested seed. Our previous research indicated that in-crop herbicide applications are unnecessary due to the high competitiveness of winter wheat against weeds. This research reaffirms in-crop herbicides could be eliminated and underscores the competitiveness and sustainability that a winter wheat phase offers when integrated in Canadian Prairie cropping systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"163 ","pages":"Article 127437"},"PeriodicalIF":4.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-return trade-offs in diversified cropping systems under conservation agriculture: Evidence from a 14-year long-term field experiment in north-western India","authors":"Hari Sankar Nayak ,&nbsp;Maxwell Mkondiwa ,&nbsp;Kiranmoy Patra ,&nbsp;Ayan Sarkar ,&nbsp;K. Srikanth Reddy ,&nbsp;Pramod Kumar ,&nbsp;Sneha Bharadwaj ,&nbsp;Rajbir Singh ,&nbsp;Chiter Mal Parihar","doi":"10.1016/j.eja.2024.127436","DOIUrl":"10.1016/j.eja.2024.127436","url":null,"abstract":"<div><div>Conservation agriculture practices are promoted to increase productivity, profitability, and sustainability across diverse cropping systems. Many studies have used these goals in decision support frameworks to identify the most effective treatment among those examined. While this approach is valuable, it lacks actionable guidance for farmers regarding maximizing return, while minimizing risk. It does not provide specific recommendations on how to allocate land across various cropping systems and tillage practices to achieve such objectives. This would require another long-term experiment exploring various combinations of treatments. To address this challenge, we propose the application of modern portfolio theory, specifically leveraging mean-variance and conditional value at risk optimization models. Using these models has enabled us to identify the optimal cropping system combinations with different tillage practices that maximized yield and net returns with minimal associated risk. The proposed approach allows for recommendations involving combinations of treatments that may not have been previously tested in a geography. In a 14-year long-term conservation agriculture study involving twelve combination of tillage and cropping systems, we showed how different combination of treatments differ in risk-return profile using mean-variance and conditional value-at-risk models that trace out a frontier of options—combinations of treatments that give highest returns at minimal risk. For example, we find that across risk neutral (most profitable) and most risk averse (lowest risk) farmers, the optimal treatments on the frontier encompass of maize-mustard-mungbean (MMuMb) under zero tillage and maize-wheat-mungbean (MWMb) under bed planting (which offer high returns and associated risk), maize-maize-<em>Sesbania</em> (MMS) under zero tillage (providing a balance of moderate returns and risk), and MMS under conventional tillage (yielding lower returns and risk). Additionally, risk-averse farmers stand to gain by diversifying their land allocation. For instance, they could allocate 54 % of their land to MMuMb under zero tillage and 46 % to MWMb under bed planting to target net returns of INR 1,32,000, with downside risk of INR 56,000, otherwise they can allocate 44 % and 56 % of their land to MMS under zero tillage and MWMb under bed planting, respectively, with a targeted net return of INR 1,22,000 and downside risk of INR 43,540. This highlights the nuanced trade-off between risk and return in maize based diversified cropping systems under different tillage practices. Leveraging mean-variance and conditional value at risk optimization models in the analysis of long-term experiments can yield novel treatment combinations that hold promise and can be recommended to farmers for implementation.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"162 ","pages":"Article 127436"},"PeriodicalIF":4.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New insights to understand the influence of hairy vetch on maize yield and its response to nitrogen application","authors":"Francisco Cafaro La Menza ,&nbsp;Fernando Salvagiotti ,&nbsp;Nicolas E. Maltese ,&nbsp;Roxana P. Eclesia ,&nbsp;Mirian Barraco ,&nbsp;Laura Echarte ,&nbsp;Pablo A. Barbieri ,&nbsp;Walter D. Carciochi","doi":"10.1016/j.eja.2024.127434","DOIUrl":"10.1016/j.eja.2024.127434","url":null,"abstract":"<div><div>Including hairy vetch (<em>Vicia villosa</em> Roth.) in a crop sequence before maize (<em>Zea mays</em> L.) can enhance the cash crop grain yield and reduce nitrogen (N) fertilizer needs, though the effects are inconsistent. This study aimed to identify the variables influencing maize grain yield response to hairy vetch as a preceding cover crop and N fertilization in maize following hairy vetch. We conducted 70 experiments evaluating four treatments resulting from the inclusion (or not) of hairy vetch previous to maize with and without subsequent N fertilization. Our findings revealed significant (p&lt;0.05) positive response of maize to hairy vetch in 21 % of the experiments (average increase of 2.79 t ha⁻¹) and yield reductions in 13 % (average decrease of −2.02 t ha⁻¹). The magnitude of this response was explained by the N-limited yield index (ratio between N-fertilized and non-N fertilized maize yield), N contribution from vetch, water balance of the whole sequence, and management practices of both crops (e.g., sowing dates and hairy vetch cycle length). Maize grain yield following hairy vetch showed positive response to N application in 27 % of the experiments (average of 2.68 t ha⁻¹). Positive responses to N fertilization were evident in environments with a high N-limited yield index, low N contribution from hairy vetch, favorable water availability, and low soil organic matter concentration. These findings provide valuable insights for producers seeking to optimize the use of hairy vetch to reduce reliance on synthetic fertilizers on succeeding maize crop, ultimately contributing to more sustainable and diversified cropping systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"162 ","pages":"Article 127434"},"PeriodicalIF":4.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ex-ante analyses using machine learning to understand the interactive influences of environmental and agro-management variables for target-oriented management practice selection","authors":"Reshmi Sarkar ,&nbsp;Charles Long ,&nbsp;Brian Northup","doi":"10.1016/j.eja.2024.127432","DOIUrl":"10.1016/j.eja.2024.127432","url":null,"abstract":"<div><div>Conservation management in dryland agriculture preserves water, improves soil health and yields. To comprehend the complex interactions of conservation management and environmental factors in a rainfed forage system of the US Great Plains, distinguish the superior influence of conservation over conventional management, and have a different perspective from simulation modeling, machine learning (ML) and artificial intelligence models were adapted in 2022. The variables in this study included ten years of daily recorded weather data and yield values simulated by the DSSAT model suite, considering four years of actual data on aboveground and belowground biomass, depth-wise carbon, water content, various physicochemical soil parameters, and management practices (Sarkar and Northup 2023). Two optimized ML models, Random Forest and AdaBoost, were found to perform better, when the algorithms of six ML models- namely Decision Tree, Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost were tuned with different hyperparameters, validated and trained before predicting the biomass yields. Feature Importance plotting by these two models revealed the five most influencing similar variables, which were in different orders: average maximum temperature during daylight hours, total soil water, seasonal average minimum temperature, cumulative potential evapotranspiration and CO₂. Hence, SHapley Additive exPlanation (SHAP) algorithm was adopted to dive into the database and clarify the interaction effects of management practices especially tillage and soil cover with different environmental variables. Interestingly, the SHAP model indicated soil cover as the 5th most important variable, followed by maximum temperature during daylight hours, cumulative potential evapotranspiration, seasonal minimum temperature and CO₂. The interaction plotting of SHAP analysis also manifested that intensity of tillage and use of no soil cover could be detrimental. Considering the rising atmospheric CO2 levels and temperatures, along with depleting soil water, no-till practices with a springtime cover of grass peas or field peas and the addition of 100 % residue can be acclaimed for high water-use efficiency and increased aboveground biomass of rainfed sorghum sudangrass in drylands. We recommend using impeccable dataset, particularly from diverse agro-environmental systems with various tillage practices and soil covers, before regional adoption. Additionally, exploring the impacts on diverse soil types is advisable before selecting a sustainable management strategy for precision agriculture.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"162 ","pages":"Article 127432"},"PeriodicalIF":4.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672811","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}