Pub Date : 2025-02-04DOI: 10.1016/j.eja.2025.127534
Takashi S.T. Tanaka, René Gislum
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.
{"title":"Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning","authors":"Takashi S.T. Tanaka, 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 (R<sup>2</sup>: 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}
Pub Date : 2025-02-04DOI: 10.1016/j.eja.2025.127524
Diego Quintero , Vikalp Mishra , Ashutosh S. Limaye , Nicole Van Abel , Julius Bright Ross , Arif Rashid
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.
{"title":"Bayesian calibration of management practices for a crop model implemented in a subsistence farming region","authors":"Diego Quintero , Vikalp Mishra , Ashutosh S. Limaye , Nicole Van Abel , Julius Bright Ross , 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}
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 ICM1–4: conventional (CT); ICM5–6: double zero-tilled and ICM7–8: triple zero-tilled ICM practices. ICM5–8 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 ICM5–8 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 ICM5–8 practices. On average, the residue-retained ICM7–8 practices led to a ∼19 % and ∼12 % increase in the grain and stover yields, respectively over the ICM1–4 practices. In maize, the highest sustainable yield index too was recorded under the ICM5–8 practices, which was ∼19 % higher than the ICM1–4. 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.
{"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 , Vijay Pooniya , R.R. Zhiipao , Niraj Biswakarma , Dinesh Kumar , Kajal Das , Y.S. Shivay , S.S. Rathore , Nilutpal Saikia , Santanu Kundu , Arjun Singh , M.C. Meena , Arti Bhatia , 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<sub>1–4</sub>: conventional (CT); ICM<sub>5–6</sub>: double zero-tilled and ICM<sub>7–8</sub>: triple zero-tilled ICM practices. ICM<sub>5–8</sub> 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<sub>5–8</sub> 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<sub>5–8</sub> practices. On average, the residue-retained ICM<sub>7–8</sub> practices led to a ∼19 % and ∼12 % increase in the grain and stover yields, respectively over the ICM<sub>1–4</sub> practices. In maize, the highest sustainable yield index too was recorded under the ICM<sub>5–8</sub> practices, which was ∼19 % higher than the ICM<sub>1–4</sub>. 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}
Pub Date : 2025-02-03DOI: 10.1016/j.eja.2025.127525
Albert Berdjour , Amit Kumar Srivastava , Safiétou Sanfo , Bocar Ahamadou , Frank Ewert , Thomas Gaiser
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 < 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 < 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 < 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.
{"title":"Impact of fertilizer applications on grain and vegetable crops in smallholder Mixed Crop-Livestock (MCL) systems in West Africa","authors":"Albert Berdjour , Amit Kumar Srivastava , Safiétou Sanfo , Bocar Ahamadou , Frank Ewert , 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<sup>−1</sup> 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 < 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 < 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 < 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}
Pub Date : 2025-02-02DOI: 10.1016/j.eja.2025.127522
L. Fagnant , P. Aubry , O. Duchene , J.M. Jungers , B. Dumont
Thinopyrum intermedium 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 Th. intermedium 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, Th. intermedium is characterized by a conservative phalanx growth strategy. However, 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.
{"title":"Seasonal allocation of dry matter and nitrogen in Th. intermedium across stand ages","authors":"L. Fagnant , P. Aubry , O. Duchene , J.M. Jungers , 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<sup>−1</sup>). 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<sup>−1</sup>, 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}
Conservation tillage helps maintain soil structure, reduces erosion and improves water retention, while little is known about the drivers for the improvement of crop yield. To explored the key mechanisms responsible for enhancing crop yields, we set up two conventional tillage practices (DT: Deep tillage 30 cm, SRT: shallow rotary tillage 15 cm) and two conservation tillage practices (NT: The straw is removed after the crop is harvested and no tillage is carried out, KSNT: Leave high stubble during harvest, and leave straw and stubble on the surface without tillage treatment) from 2016. Results showed that compared with DT and SRT, KSNT and NT significantly increased soybean yield by 115.52–171.3 kg·km−2, and maize yield by 723.02–863.24 kg·km−2. In 2022 and 2023 years, compared with DT and SRT, KSNT significantly increased soil total nutrients, available nutrients, enzyme activity, and microbial biomass, followed by NT. In addition, KSNT significantly altered the soil bacterial and fungal communities structure, increased the diversity of soil bacterial and fungal, and bacteria appeared to be more sensitive to tillage systems than fungal communities. Soil quality index (SQI) in NT and KSNT was improved compared with DT and SRT, which was associated with crop yield. Our study found that KSNT increased crop yield by improving soil quality, reducing soil erosion, reshaping bacterial and fungal microbiota, and it was a suitable tillage method for the Northeast region.
{"title":"Crop yield and soil quality of soya bean-maize rotation in response to 8-year keep stubble with no tillage practices on the Northeast China","authors":"Weijian Zhang, Jingyi Feng, Xueyan Bai, Wanying He, Jixian Mo, Qiance Gao, Kunjie Wang, Siyu Gu","doi":"10.1016/j.eja.2025.127526","DOIUrl":"10.1016/j.eja.2025.127526","url":null,"abstract":"<div><div>Conservation tillage helps maintain soil structure, reduces erosion and improves water retention, while little is known about the drivers for the improvement of crop yield. To explored the key mechanisms responsible for enhancing crop yields, we set up two conventional tillage practices (DT: Deep tillage 30 cm, SRT: shallow rotary tillage 15 cm) and two conservation tillage practices (NT: The straw is removed after the crop is harvested and no tillage is carried out, KSNT: Leave high stubble during harvest, and leave straw and stubble on the surface without tillage treatment) from 2016. Results showed that compared with DT and SRT, KSNT and NT significantly increased soybean yield by 115.52–171.3 kg·km<sup>−2</sup>, and maize yield by 723.02–863.24 kg·km<sup>−2</sup>. In 2022 and 2023 years, compared with DT and SRT, KSNT significantly increased soil total nutrients, available nutrients, enzyme activity, and microbial biomass, followed by NT. In addition, KSNT significantly altered the soil bacterial and fungal communities structure, increased the diversity of soil bacterial and fungal, and bacteria appeared to be more sensitive to tillage systems than fungal communities. Soil quality index (SQI) in NT and KSNT was improved compared with DT and SRT, which was associated with crop yield. Our study found that KSNT increased crop yield by improving soil quality, reducing soil erosion, reshaping bacterial and fungal microbiota, and it was a suitable tillage method for the Northeast region.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127526"},"PeriodicalIF":4.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125283","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.2025.127528
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}
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