European Journal of Agronomy最新文献

{"title":"UAV-based rice aboveground biomass estimation using a random forest model with multi-organ feature selection","authors":"Jing Shi ,&nbsp;Kaili Yang ,&nbsp;Ningge Yuan ,&nbsp;Yuanjin Li ,&nbsp;Longfei Ma ,&nbsp;Yadong Liu ,&nbsp;Shenghui Fang ,&nbsp;Yi Peng ,&nbsp;Renshan Zhu ,&nbsp;Xianting Wu ,&nbsp;Yan Gong","doi":"10.1016/j.eja.2025.127529","DOIUrl":"10.1016/j.eja.2025.127529","url":null,"abstract":"<div><h3>Background</h3><div>Aboveground biomass (AGB) is important for monitoring crop growth and field management. Accurate estimation of AGB helps refine field strategies and advance precision agriculture. Remote sensing with Unmanned Aerial Vehicles (UAVs) has become an effective method for estimating key parameters of rice.</div></div><div><h3>Methods</h3><div>This study involved four experiments conducted across varied locations and timeframes to collect field sampling data and UAV imagery. Feature extraction, including Vegetation Index (VI), textures, and canopy height, was performed. Key factors influencing biomass estimation across different rice organs were analyzed. Based on these insights, a Random Forest model was developed for AGB estimation.</div></div><div><h3>Results</h3><div>The VIS-Leaf factor-Spike factor-Stem factor (VIS-L-Sp-St) model proposed in this study outperformed traditional methods. The training set achieved an R² of 0.89 with a reduced RMSE of 191.30 g/m², surpassing the traditional VIS model (R²=0.64, RMSE=363.53 g/m²). Notably, in the validation set, the VIS-L-Sp-St model showed good transferability, with an R² of 0.85 and RMSE of 196.55 g/m², outperforming MLR (R²=0.02, RMSE=5944.09 g/m²), PLSR (R²=0.18, RMSE=934.27 g/m²) methods, BP (R²=0.14, RMSE=581.61 g/m²) method and SVM method((R²=0.45, RMSE=600.91 g/m²).</div></div><div><h3>Conclusions</h3><div>Sensitivity analysis showed that different rice organs respond differently to specific features. This insight improves feature selection efficiency and enhances AGB estimation accuracy. The organ-specific AGB estimation model highlights its potential to support precision agriculture and field management, contributing to advancements in agricultural research and application.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127529"},"PeriodicalIF":4.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376892","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":"Machine learning-driven analysis of greenhouse gas emissions from rice production in major Chinese provinces: Identifying key factors and developing reduction strategies","authors":"Songhua Huan ,&nbsp;Xiuli Liu","doi":"10.1016/j.eja.2025.127536","DOIUrl":"10.1016/j.eja.2025.127536","url":null,"abstract":"<div><div>Rice cultivation is a significant contributor to global greenhouse gas (GHG) emissions. However, the complex nonlinear relationship between driving factors and GHG emission intensity (GHGI) remains poorly understood, and effective reduction strategies are still needed. This study integrates machine learning models and SHapley Additive Explanations (SHAP) to assess the nonlinear relationship and design GHGI reduction strategies based on data from 14 provinces in China from 2012 to 2022. The key findings are as follows. (1) For GHGI reduction, the optimal conditions include an annual average sunshine duration of 47–75 days, an annual average temperature of 15.3–17.9℃, annual average precipitation levels of either 1000.0–1368.4 or 1680.0–2004.7 mm, soil pH below 5.6 or above 6.5, soil total nitrogen content of 17.0–20.3 g/kg, and soil organic carbon content of 15.0–22.5 g/kg. The recommended application rates for nitrogen, phosphate, and potassium fertilizers are 160.0–311.0 kg/ha, 124.9–129.9 kg/ha and 144.0–194.3 kg/ha, respectively. Agricultural practices such as transplanting, mixed farming, tillage and mid-season drainage demonstrate higher GHGI reduction potential compared to other measures. (2) For lowest-cost GHGI reduction strategies in major provinces, Heilongjiang, Jilin, and Liaoning provinces could reduce GHGI to 0.28, 0.15, and 0.05 tCO₂e/t, respectively, by adjusting sunshine conditions. Hainan, Guangdong, Fujian, Jiangsu, Jiangxi, Zhejiang and Guangxi provinces could achieve GHGI reductions to 0.62, 0.31, 0.21, 0.47, 0.57, 0.92 and 0.28 tCO₂e/t, respectively, by optimizing nitrogen fertilizer application and labor practices. Hunan and Anhui provinces could reduce GHGI to 0.57 and 0.85 tCO₂e/t by adjusting irrigation modes. Implementing these strategies would result in an average GHGI reduction of 28.75 %, although production costs per mu for early, mid-to-late indica and japonica rice in major provinces would increase by 28.87 %, 27.95 % and 27.38 %, respectively, compared to the original production costs. These findings provide valuable insights and a scientific basis for developing GHGI reduction strategies in rice production and enhancing the sustainability of this critical agricultural sector.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127536"},"PeriodicalIF":4.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing climate change impacts and adaptation strategies for key crops in the Republic of Moldova using the AquaCrop model","authors":"Jorge Alvar-Beltrán ,&nbsp;Andrea Setti ,&nbsp;Jane Mugo ,&nbsp;Nicolae Bucor ,&nbsp;Gherman Bejenaru ,&nbsp;Arianna Gialletti ,&nbsp;Ala Druta","doi":"10.1016/j.eja.2025.127530","DOIUrl":"10.1016/j.eja.2025.127530","url":null,"abstract":"<div><div>Impact-based models are an essential tool to better understand the effects of climate change on crop production and to aid in the adaptation planning processes. However, in the Republic of Moldova (RoM), there is limited integration of crop simulation findings into adaptation policies and plans (see National Adaptation Plan (NAP) adopted in 2024). To bring novelty to this topic, the Food and Agriculture Organization (FAO), in conjunction with the State Hydrometeorological Service and the State Commission for Plant Variety Testing of the RoM, aims to assess the effect of future climate on five crops of national importance (maize, tomatoes, sunflowers, green peas, and wheat). We use state-of-the-art climate (Coordinated Regional Downscaling Experiment (CORDEX) and Coordinated Output for Regional Evaluations (CORE)) and crop models (AquaCrop) for two climate change scenarios: Representative Concentration Pathways (RCPs) 2.6 and 8.5. Adaptation solutions across the RoM are explored by advancing or delaying the sowing dates and enhancing field management decisions by improving soil fertility and reducing weed stress. Statistically significant (<em>p &lt; 0.05</em>) higher yields are simulated when advancing the sowing date of maize and when growing medium cycle varieties as opposed to short cycle. A CO₂-enriched environment (RCP 8.5) leads to statistically significantly higher yields among C3 crops (wheat and green peas) but has detrimental effects on C4 crops (maize). Limiting climatic drivers include decreasing seasonal rainfall, a higher number of dry days and heat-stress conditions during the summertime, and, conversely, fewer cold days during the wintertime necessary for wheat vernalization. As a result, this research not only provides valuable insights for stakeholders mandated to provide evidence-based adaptation, such as the National Commission on Climate Change, but also uncovers potential adaptation solutions to mitigate the adverse effects of climate change.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127530"},"PeriodicalIF":4.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307191","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":"Refining the soil and water component to improve the MoSt grass growth model","authors":"L. Bonnard ,&nbsp;L. Delaby ,&nbsp;M. O’Donovan ,&nbsp;M. Murphy ,&nbsp;E. Ruelle","doi":"10.1016/j.eja.2025.127520","DOIUrl":"10.1016/j.eja.2025.127520","url":null,"abstract":"<div><div>Knowledge of previous and future grass growth is an important factor for grassland management decision making. It allows the farmer to predict the availability of grass for the herd on a short-term basis and adapt grassland management practise accordingly. The Moorepark St Gilles Grass Growth Model (MoSt GG) is used to predict grass growth weekly on 84 grassland farms across Ireland. The repeated use of the model on these farms has identified areas for improvement that have been addressed in this paper. Among these improvements, the soil sub-model component has been further developed to better represent different soil types and to account for different soil depths, improving the simulations of water and soil nitrogen fluxes (V2_V1_+soil). A soil sub-layer of 10 cm was added to better simulate growth recovery after a drought period (V3_V2+water). The radiation component was improved by including the day length in the grass growth estimation (V4_V3+rad) instead of only accounting for daily cumulative solar radiation. These improvements were evaluated against several experiments conducted in Ireland and France. The developments improved model accuracy for every experiment evaluated. The RMSE in the original version of the model ranged from 322 to 1011 kg of DM/ha, whereas in the latest version of the MoSt GG model (V4_V3+rad), the RMSE ranged from 312 to 671 kg of DM/ha. The further consideration of soil characteristics resulted in a higher variability in grass production and N leaching depending on soil type and weather conditions, leading to improved growth trend representation. The addition of the soil sub-layer (V3_V2+water) improved the accuracy in drier years (French experiment) due to the more realistic grass growth recovery after a drought. The latest version of the model (V4_V3+rad) simulates grass production more accurately than the previous versions and increases the reliability of grass growth prediction.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127520"},"PeriodicalIF":4.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307192","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":"I-DRo: A new indicator to assess spatiotemporal diversity and ecosystem services of crop rotations","authors":"Olivier Keichinger ,&nbsp;Loïc Viguier ,&nbsp;Guénaëlle Corre-Hellou ,&nbsp;Antoine Messéan ,&nbsp;Frédérique Angevin ,&nbsp;Christian Bockstaller","doi":"10.1016/j.eja.2025.127531","DOIUrl":"10.1016/j.eja.2025.127531","url":null,"abstract":"<div><div>Agroecological farming systems depend on ecosystem services (ES) to replace external anthropogenic inputs. Crop diversification is increasingly being put forward as a way to support ES service provision. Actors involved in promoting, designing and implementing diversified cropping systems presumably need to assess the degree of diversity and performance of diversified cropping systems at one stage or another. This article focuses on crop rotation diversity since it is a core component of annual cropping systems. In this study, we designed a new global indicator (I-DRo) based on a set of other indicators used to assess the temporal diversity of crop rotation (which includes functional diversity through ES provision,) as well as taxonomic diversity and spatial diversity. I-DRo covers various crop diversification strategies in the rotation: introduction of new crops or cover crops, multiple cropping, intercropping, relay cropping and strip cropping. The proposed indicators may be used separately or aggregated in a hierarchical way according to a fuzzy decision tree. Using I-DRo requires only data on field width and the main crop and cover crop species. Initial tests showed the indicator could potentially be used to support various actors in their decision-making, although results on the predictive quality were mixed given the degree of simplification. Contextualizing the calculation method for ES assessment would be one avenue of investigation. Lastly, use at EU level could support the implementation of new cross-compliance measures on crop diversification, but would require efforts to harmonize data on main crops and cover crops.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127531"},"PeriodicalIF":4.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307193","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":"Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning","authors":"Takashi S.T. Tanaka,&nbsp;René Gislum","doi":"10.1016/j.eja.2025.127534","DOIUrl":"10.1016/j.eja.2025.127534","url":null,"abstract":"<div><div>The critical nitrogen dilution curve (CNDC) and associated nitrogen nutrition index (NNI) are known to provide valuable information indicating whether the crops are experiencing luxury nitrogen (N) uptake—where they absorb more N than needed for optimal growth— or suffering from N insufficiency, where they fail to meet their optimal growth requirements. The aim of this study was to explore the potential of using UAV-based remote sensing and weather data to quantify NNI in a winter wheat crop. For that purpose, field trials with different N application strategies were conducted over three cropping seasons. The calibrated CNDC used in this study showed a better performance in detecting yield reduction caused by the N insufficiency compared to using a CNDC developed in a previous study (default CNDC). Machine learning models (i.e., random forest and partial least squares regression) were used to predict shoot biomass, N concentration, and NNI. The results showed that machine learning models could predict crop N status at medium or high accuracies (R²: 0.59–0.95). However, the default NNI predictions based on UAV data consistently indicated N insufficiency even when the crop was not suffering from N insufficiency. Whereas the calibrated NNI predictions occasionally could detect a reduction in yield caused by N deficiency. Robustness and scalability of the CNDC have rarely been discussed but based on our findings we suggest testing whether the preferred CNDC should be calibrated for a specific cultivar or region is particularly important when using remote sensing technologies for nondestructive N status measurements.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127534"},"PeriodicalIF":4.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125317","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":"Bayesian calibration of management practices for a crop model implemented in a subsistence farming region","authors":"Diego Quintero ,&nbsp;Vikalp Mishra ,&nbsp;Ashutosh S. Limaye ,&nbsp;Nicole Van Abel ,&nbsp;Julius Bright Ross ,&nbsp;Arif Rashid","doi":"10.1016/j.eja.2025.127524","DOIUrl":"10.1016/j.eja.2025.127524","url":null,"abstract":"<div><div>Rainfed agriculture is crucial for food security in sub-Saharan Africa, yet it faces significant challenges from climate variability, soil degradation, and limited access to resources. Process-based crop models are widely used in agricultural research as well as in decision support systems. These systems play an important role in aiding policymakers in designing and implementing strategies to enhance food security. Farm management practices are one essential input for crop models. However, that data exhibit farm-scale variabilities and is usually scarce in regions with fragile food production systems, rendering the powerful crop modeling tools ineffective, particularly in large-scale applications. We present a new approach to infer the relevant management practices of a region in a data scarce environment. We introduce Bayesian calibration as a method to infer key management practices using the CERES-Maize model within DSSAT, in order to provide more reliable yield estimates in a subsistence-farming region. This novel approach allows to better represent the uncertainty in the unknown input management practices in addition to the soil and weather-related variabilities. A study case was presented using farm-level maize yield data from 18 wards in North-western Zimbabwe from the 2021/22 season. The calibrated model provided reliable yield estimates for 72 % of the wards, significantly outperforming the non-calibrated model, which captured the observed yield for only 22 % of the wards. Furthermore, the calibrated model better captured intra-regional yield variation, with an R² of 0.42 and a d-agreement index of 0.67. This approach underscores the importance of accurately representing the variability of management practices in larger-scale implementations of crop models. This approach will allow the crop models to be effectively used for monitoring and forecasting of crop yield for a wide swath of fragile lands with limited data availabilities.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127524"},"PeriodicalIF":4.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125321","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":"Pre- and post-anthesis dry matter and nutrient accumulation, partitioning, remobilization and crop productivity of maize under the long-term integrated crop management practices","authors":"Anamika Barman ,&nbsp;Vijay Pooniya ,&nbsp;R.R. Zhiipao ,&nbsp;Niraj Biswakarma ,&nbsp;Dinesh Kumar ,&nbsp;Kajal Das ,&nbsp;Y.S. Shivay ,&nbsp;S.S. Rathore ,&nbsp;Nilutpal Saikia ,&nbsp;Santanu Kundu ,&nbsp;Arjun Singh ,&nbsp;M.C. Meena ,&nbsp;Arti Bhatia ,&nbsp;Suman Dutta","doi":"10.1016/j.eja.2025.127527","DOIUrl":"10.1016/j.eja.2025.127527","url":null,"abstract":"<div><div>Integrated crop management (ICM) practices play a critical role in enhancing the maize’s physiological growth, optimizing the dry matter and nutrients’ acquisition coupled with increased productivity. The effect of these comprehensive long-term ICM practices was investigated on the growth and physiological characteristics, dry matter and nutrient accumulation, partitioning and remobilization, productivity, and sustainability of the maize under the field conditions in semi-arid regions of sub-tropical India. Eight ICM practices were evaluated over nine consecutive years (2014–2023), which included ICM_1–4: conventional (CT); ICM_5–6: double zero-tilled and ICM_7–8: triple zero-tilled ICM practices. ICM_5–8 practices improved maize growth attributes over the conventional ICM practices, wherein the increment in the relative growth and the net assimilation rates were 7.9–8.2 %, and 14.1–15.5 %, respectively. Further, these practices improved the photosynthesis rates (9.7–20.5 %), stomatal conductance (11.5–19.1 %), and transpiration efficiency (5.4–14.2 %). In addition, the residue-retained practices showed a greater reduction in canopy temperature (-3.2 to −4˚C) over the CT (-1.6 to −2.7˚C), along with the enhancements in total chlorophyll (31.1–49.7 %), and carotenoids (26.9–50.3 %) at anthesis stage. Additionally, the ICM_5–8 demonstrated the increases of 38.8–60.1 %, 168–219 %, and 45.9–81.3 % in pre-anthesis translocation of nitrogen (N), phosphorus (P), and potassium (K), respectively over the conventional ICM practices. Likewise, the post-anthesis N, P, and K translocation increased by 20.3–29.4 %, 34.7–71.5 %, and 37.9–40.1 %, respectively under the residue-retained double and triple-ZT ICM_5–8 practices. On average, the residue-retained ICM_7–8 practices led to a ∼19 % and ∼12 % increase in the grain and stover yields, respectively over the ICM_1–4 practices. In maize, the highest sustainable yield index too was recorded under the ICM_5–8 practices, which was ∼19 % higher than the ICM_1–4. The study underscores the potential of adopting residue-retained zero-tilled ICM practices to enhance the maize yields and continuance of sustainability in the long-run.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127527"},"PeriodicalIF":4.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125323","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":"Impact of fertilizer applications on grain and vegetable crops in smallholder Mixed Crop-Livestock (MCL) systems in West Africa","authors":"Albert Berdjour ,&nbsp;Amit Kumar Srivastava ,&nbsp;Safiétou Sanfo ,&nbsp;Bocar Ahamadou ,&nbsp;Frank Ewert ,&nbsp;Thomas Gaiser","doi":"10.1016/j.eja.2025.127525","DOIUrl":"10.1016/j.eja.2025.127525","url":null,"abstract":"<div><div>Mixed crop-livestock (MCL) systems can enhance crop yield, and improve nutrient cycling while reducing chemical fertilizer use. However, only a limited number of studies that reported this assumption were conducted under real-world conditions of small-scale farmers or followed an integrated approach. A survey was conducted in the 2021/2022 and 2022/2023 cropping seasons in Ghana and Burkina Faso, respectively, to determine the impact of fertilizer application practices on the yield of grain and vegetable crops in real-world MCL systems. Detailed information on fertilizer management practice and yield was collected from 317 MCL system farms distributed across three (3) districts/provinces in the Upper East region of Ghana and over the Plateau central of Burkina Faso, respectively summarising data on their grain and vegetable yields under (1) major fertilizer sources; organic, chemical, and combined (organic + chemical), (2) N fertilizer rate (crop x country specific N kg ha⁻¹ recommendation), (3) application timing of fertilizer sources (recommended crop x country specific time of application), and (4) fertilizer placement methods (broadcast versus side placement versus furrow). Results show that the use of different fertilizer source increased (P &lt; 0.05) yields of all grain crops (in Burkina Faso) and maize, rice, sorghum, millet, cowpea and all vegetable crops (in Ghana). The application of crop and country specific recommended N rates significantly influenced (P &lt; 0.05) yields of sorghum, cowpea and green beans in Burkina Faso and rice, sorghum, millet, cowpea and pepper in Ghana compared to low N application rates. The contribution of manure application and appropriate timing on yield mostly differed between countries, such that high tendencies of increased yields were recorded when manure was applied for 0–3 weeks before planting (WBP) in Burkina Faso, while in Ghana, the highest yield improvements were observed when application periods exceeded 3 WBP. Not broadcasting chemical fertilizer only increased (P &lt; 0.05) yields of millet and green beans (in Burkina Faso) and vegetable crops in both countries. These results help improve our understanding of fertilizer practices in mixed crop-livestock systems of Burkina Faso and Ghana, and may help guide recommended fertilizer management in MCL systems of these countries and similar ecologies in West Africa.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127525"},"PeriodicalIF":4.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125326","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":"Seasonal allocation of dry matter and nitrogen in Th. intermedium across stand ages","authors":"L. Fagnant ,&nbsp;P. Aubry ,&nbsp;O. Duchene ,&nbsp;J.M. Jungers ,&nbsp;B. Dumont","doi":"10.1016/j.eja.2025.127522","DOIUrl":"10.1016/j.eja.2025.127522","url":null,"abstract":"<div><div><em>Thinopyrum intermedium</em> is currently proposed as a perennial grain for both forage and grain production. Undergoing domestication, its grain yields are low, while its long-lasting organs are ensuring environmental benefits. However, understanding the resource allocation dynamics of <em>Th. intermedium</em> is needed. Dry matter (DM) and nitrogen (N) allocations within the different plant parts were quantified over the growing season on various experimental sites and stand ages. Low resource mobilization to spikes was observed after flowering, contrarily to N allocation within stem bases. Indeed, root production and stem bases thickening over the years represented significant N sinks. In addition, the total N within the plant, weakly allocated to spikes (i.e., 10–26 %), can decrease at the end of the growing season (i.e., from 34 to 56 kg ha⁻¹). This could be explained by root turnover and release of N-rich root exudates to the soil. With a low exportation of N at grain maturity, averaging 60 kg ha⁻¹, a strategy of nutrient conservation was highlighted. Furthermore, through a small proportion of rhizomes, <em>Th. intermedium</em> is characterized by a conservative phalanx growth strategy. However<em>,</em> plant growth conditions could modulate rhizomes’ production as variation within varying stand densities were observed. Finally, we observed an increase of allocation to stem bases in older stands, coupled to a decrease of the reproductive allocation through lower proportion of reproductive tillers. Thus, work dedicated to understanding the allocation of resources in the plant will be beneficial to help identify possible synergies and trade-offs between grain production and ecological services.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127522"},"PeriodicalIF":4.5,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125007","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}