Field Crops Research最新文献

{"title":"Nitrogen reduction and split application increased grain yield and agronomic nitrogen use efficiency of oilseed flax (Linum usitatissimum. L) by increasing nitrogen status","authors":"Ming Wen ,&nbsp;Yuhong Gao ,&nbsp;Lizhuo Guo ,&nbsp;Bin Yan ,&nbsp;Peina Lu ,&nbsp;Bin Wu ,&nbsp;Yifan Wang ,&nbsp;Yue Li ,&nbsp;Zhengjun Cui ,&nbsp;Peng Xu ,&nbsp;Haidi Wang ,&nbsp;Yuanyuan Cui ,&nbsp;Xingkang Ma ,&nbsp;Yongwei Zhao ,&nbsp;Ying Li","doi":"10.1016/j.fcr.2025.109910","DOIUrl":"10.1016/j.fcr.2025.109910","url":null,"abstract":"<div><h3>Context</h3><div>Accurate monitoring of crop nitrogen status is the premise of precise nitrogen application. However, there is a lack of general diagnostic approach for the nitrogen status of oilseed flax, and the mechanism of nitrogen fertilizer managements influencing oilseed flax grain yield and agronomic nitrogen use efficiency (aNUE) is still unclear.</div></div><div><h3>Objectives or methods</h3><div>Six nitrogen application rates (0 (N0), 60 (N60), 90 (N90), 120 (N120), 150 (N150), and 180 (N180) kg hm⁻²) and three split application methods (T1, 100 % of nitrogen at pre-sowing; T2, 2/3 of nitrogen at pre-sowing + 1/3 at budding stage; T3, 1/3 of nitrogen at pre-sowing + 1/3 at branching stage + 1/3 at budding stage) were designed. A critical nitrogen concentration model for oilseed flax was constructed. Then, the effects of different nitrogen fertilizer managements on dry matter and nitrogen accumulation, distribution, translocation, grain yield, and aNUE, as well as relationships between these indices and plant accumulated N deficit (Nand)/nitrogen nutrition index (NNI) of oilseed flax were analyzed.</div></div><div><h3>Results</h3><div>The dry matter-based critical nitrogen concentration model for oilseed flax was Y= 2.4507X-0.33473, with an R2 of 0.91294. Compared with the traditional nitrogen application rate (N180), nitrogen reduction by 33 % (N120) reduced the full-season NNI, nitrogen accumulation, and TransN (pre-anthesis nitrogen translocation from vegetative organs to reproductive organs) by 8.91 %-19.77 %, 10.80 %, and 10.53 %, respectively (p &lt; 0.05), and increased Nand, dry matter accumulation, proportions of dry matter allocated to leaves (PDL) and reproductive organs (PDR) at maturity stage, TransD (pre-anthesis dry matter translocation from vegetative organs to reproductive organs), TransD rate, grain yield, and aNUE by 73.42 %-118.31 %, 4.24 %, 4.81 %, 3.02 %, 9.73 %, 15.89 %, 7.43 %, and 121.92 %, respectively (p &lt; 0.05). Compared with T1, T2 increased the dry matter accumulation, PDL, PDR, TransD, nitrogen accumulation, proportion of nitrogen allocated to reproductive organs at maturity stage, grain yield, and aNUE by 4.99 %, 45.42 %, 4.00 %, 21.80 %, 11.24 %, 8.87 %, 4.09 %, and 50.95 %, respectively (p &lt; 0.05).</div></div><div><h3>Conclusions or implications</h3><div>Among the nitrogen fertilizer managements, the optimal mode N120T2 could improve oilseed flax nitrogen status, coordinate the source-sink relationship in plants to efficiently use photosynthetic assimilates, and promote the translocation of photosynthetic assimilates to sink organs in the late growth stage, thereby increasing the grain yield and aNUE. This study will provide a technical means for precise nitrogen fertilization and yield increase in oilseed flax in the semi-arid region in China.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109910"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864995","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":"Intercropping lablab with maize increases grain production and soil cover, and reduces pest pressure in Tanzania","authors":"Sasha Loewen ,&nbsp;Neil Miller ,&nbsp;Michelle Carkner ,&nbsp;Wilfred Mariki ,&nbsp;Martin Entz","doi":"10.1016/j.fcr.2025.109916","DOIUrl":"10.1016/j.fcr.2025.109916","url":null,"abstract":"<div><h3>Background</h3><div>East African farmers and researchers are sharing a renewed interest in <em>Lablab purpureus</em> (L.) Sweet, a multipurpose leguminous cover crop. Lablab can produce food and fodder, fix nitrogen, scavenge resources, protect the soil, and tolerate drought. To capitalize on its potential, lablab requires more research into its basic agronomy, particularly under intercropping situations in which it is usually grown by small-holder farmers in East Africa.</div></div><div><h3>Objectives</h3><div>The study aimed to understand the effects of lablab-maize intercropping through three measured ecosystem services: 1) grain production of lablab and maize, assessed in yield and land equivalent ratio; 2) late season soil cover of living plant material; and 3) major lablab insect pests: pod boring caterpillars (<em>Maruca vitrata, Helicoverpa armigera, Etiella zinckenella</em>) and pod sucking coreid bugs (<em>Riptortus pedestris, Clavigralla tomentosicollis</em>).</div></div><div><h3>Methods</h3><div>In this study, lablab was intercropped with maize across two agro-ecozones in northern Tanzania over three years. Yield data was collected from both crop species, while late season soil cover and insect pressure focused on lablab. A simple economic analysis examined net return of sole and intercropping as a response to the costs of seed and harvesting.</div></div><div><h3>Results</h3><div>Over the six environments, intercropping reduced lablab grain yields by 35 % (p = 0.009); intercropping reduced maize yields marginally though this was not found to be significant (p = 0.087). The land equivalent ratio of maize and lablab, ranged from 1.36 to 1.96 across environments. Under adequate moisture conditions lablab grown with maize produced 58 % more late season ground cover than when lablab was sole cropped (p = 0.039), whereas in the driest environments, the opposite trend was observed (p = 0.011). The number of pod boring caterpillars (p = 0.052) and pod sucking coreid bugs (p &lt; 0.001) were reduced by 34 % and 57 % respectively by intercropping. Intercropping produced a higher net return than sole cropping lablab or maize.</div></div><div><h3>Conclusions</h3><div>These results demonstrate the diverse benefits of growing maize with lablab allowing for greater food production, increased soil protection, and reduced pest pressure. Of particular importance was the negligible effect of lablab grown with maize, on maize grain yield, highlighting that the detractions of intercropping in smallholder agriculture are outweighed by the advantages. Continued lablab research to identify best agronomic practices and new cultivars will encourage its adoption and help East African farmers diversify and strengthen their cropping systems.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109916"},"PeriodicalIF":5.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860338","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":"Knowledge-guided machine learning with multivariate sparse data for crop growth modelling","authors":"Jingye Han ,&nbsp;Liangsheng Shi ,&nbsp;Qi Yang ,&nbsp;Jin Yu ,&nbsp;Ioannis N. Athanasiadis","doi":"10.1016/j.fcr.2025.109912","DOIUrl":"10.1016/j.fcr.2025.109912","url":null,"abstract":"<div><h3>Context</h3><div>Process-based crop models are widely used to simulate the crop growth process. However, these models face limitations due to the simplified process representation and challenges in parameter estimation. Machine learning methods, as an emerging paradigm, have shown potential in circumventing these limitations, but they are criticized for their black-box nature that does not necessarily encompass known crop growth mechanisms, and their demand for big data that may be not available in most agricultural applications.</div></div><div><h3>Objective</h3><div>This research aims to propose a deep learning architecture that can leverage agronomic knowledge and sparse observational data for crop multivariable simulation, thereby establishing a novel paradigm for crop growth modeling.</div></div><div><h3>Methods</h3><div>We propose a Deep learning Crop Growth Model (DeepCGM) with a mass-conserving architecture that adheres to the principles of crop growth. Two additional knowledge-guided constraints regarding crop physiology and model convergence are designed to train the model with sparse datasets. An observational dataset from a two-year rice experiment of 105 plots is used to evaluate the DeepCGM against a process-based crop model (ORYZA2000) and two classical deep learning models, also employing augmentation methods. To demonstrate the validity and generalizability of the proposed model, we also conducted a replication case study of a three-year rice experiment totaling 122 plots.</div></div><div><h3>Results</h3><div>The DeepCGM architecture produces physically plausible crop growth curves for all simulated variables, while the classical machine learning models may make unreasonable predictions that violate the law of mass conservation. Furthermore, DeepCGM simulates more accurately the observed growth process when compared with the traditional process-based model, with overall accuracy (weighted normalized mean square error) across all variables improves by 8.3 % (2019) and 16.9 % (2018).</div></div><div><h3>Conclusions</h3><div>Knowledge-guided deep learning can integrate the principal mechanisms of crop growth process with deep learning. It addresses the issue of data scarcity, and thereby facilitating data-driven crop growth modelling with multivariable sparse datasets.</div></div><div><h3>Implications</h3><div>OR SIGNIFICANCE: This study highlights the potential of knowledge-guided deep learning to overcome structural error due to the simplification in conventional crop models and reduce the data requirements of data-driven models. The capacity to autonomously identify multivariable dynamic patterns in crop growth from sparse data suggests a new generation of crop growth models.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109912"},"PeriodicalIF":5.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860322","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":"Soybean genetic progress of maturity group IV cultivars under well-watered and drought stress conditions in central Argentina between 1984 and 2014","authors":"Matías de Felipe ,&nbsp;Gabriel Santachiara ,&nbsp;Lucas Borrás ,&nbsp;José L. Rotundo","doi":"10.1016/j.fcr.2025.109903","DOIUrl":"10.1016/j.fcr.2025.109903","url":null,"abstract":"<div><h3>Context</h3><div>Soybean genetic gain in central Argentina has traditionally been estimated under high-yielding conditions. To the present, the contribution of breeding efforts over a water gradient is not known.</div></div><div><h3>Objective</h3><div>To evaluate soybean yield genetic gain under contrasting water availability scenarios to (i) investigate breeding contributions to soybean yield potential and drought stress tolerance, and (ii) evaluate the physiological mechanisms behind the observed yield increases.</div></div><div><h3>Methods</h3><div>We used six cultivars belonging to maturity group IV, which were released into the market between 1984 and 2014. These cultivars were evaluated under well-watered and drought-stressed conditions. Nitrogen (N) capture, partitioning, and concentration were evaluated to understand the physiological attributes that explain seed yield changes over time.</div></div><div><h3>Results</h3><div>On average, the water-stressed condition yielded 61 % less compared to the well-watered. Absolute yield genetic progress was higher in the well-watered environment (31.6 and 12.8 kg ha⁻¹ year⁻¹ for the well-watered and stressed conditions, respectively), but relative yield gain was similar (1 % year⁻¹). Absolute yield changes over time were correlated to higher total N capture (2.1 and 0.5 kg N ha⁻¹ year⁻¹ for the well-watered and stressed condition, respectively). Biological N fixation from emergence to maturity was identified as the driver of this differential N uptake (1.4 and 0.5 kg N ha⁻¹ year⁻¹ in the well-watered and water stressed environment, respectively). More than proportional increases in absolute biological N fixation genetic progress during vegetative stages explained the higher biological N fixation in newer genotypes grown under well-watered conditions (0.5 and 0.03 kg N ha⁻¹ year⁻¹, for the well-watered and stressed condition, respectively).</div></div><div><h3>Conclusions</h3><div>Within the limited set of tested varieties, breeding efforts delivered higher yield potential with some degree of drought stress tolerance. Biological N-fixation modifications are responsible for the absolute yield genetic gain differences in well-watered vs. drought environments. Higher BNF in vegetative stages explained the higher yield potential of the latest cultivars released to the market in this study.</div></div><div><h3>Implications</h3><div>This study complements genetic gain estimations in central Argentina by evaluating the yield trajectories under contrasting water scenarios. Future investigations should focus on the genetic determinants of the processes related to total N uptake and the biological N fixation.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109903"},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860320","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":"Relevance of grain number and grain weight on barley yield responses to environmental and genetic factors","authors":"Román A. Serrago ,&nbsp;Guillermo A. García ,&nbsp;Roxana Savin ,&nbsp;Daniel J. Miralles ,&nbsp;Gustavo A. Slafer","doi":"10.1016/j.fcr.2025.109922","DOIUrl":"10.1016/j.fcr.2025.109922","url":null,"abstract":"<div><h3>Context</h3><div>Global food demand is projected to rise, making it essential to enhance agricultural production to ensure food security while minimizing environmental impacts, particularly in the face of climate change challenges. Understanding the determination of grain yield (GY) in barley is crucial for future advancements in this crop.</div></div><div><h3>Objective</h3><div>This study examines the relationships between the two major components of GY (grain number-GN and grain weight-GW), when driven by genetic and environmental factors.</div></div><div><h3>Methods</h3><div>We compiled data of GY and its numerical components (i.e. GN and GW) to generate a large and unbiased database from every single paper having the word “barley” in the title published over 25 years in four prestigious international journals: Field Crop Research, European Journal of Agronomy, Crop Science and Crop and Pasture Science (formerly Australian Journal of Agricultural Research) between January 1996 and December 2021, both inclusive.</div></div><div><h3>Results</h3><div>GN was significantly better correlated with GY than GW, accounting for 86 % of the variation in GY compared to just 13 % for GW. The changes in GY responsiveness to environmental and genetic factors were mainly due to variations in GN, especially in scenarios with high responsiveness. In contrast, low-responsiveness cases showed trade-offs between GN and GW, suggesting compensatory mechanisms that may not accurately reflect competition for resources.</div></div><div><h3>Conclusions</h3><div>The potential to increase barley GY relies heavily on GN, emphasizing the need to optimise conditions during the critical period of GN determination.</div></div><div><h3>Implications</h3><div>Barley crops under typical field conditions are generally not limited by resource availability during grain filling.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109922"},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860319","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":"Efficient fertilization pattern for rice production within the rice-wheat systems","authors":"Shen Gao,&nbsp;Haoyu Qian,&nbsp;Weiwei Li,&nbsp;Yuhui Wang,&nbsp;Jianwei Zhang,&nbsp;Weike Tao,&nbsp;Jie Sun,&nbsp;Yanfeng Ding,&nbsp;Zhenghui Liu,&nbsp;Yu Jiang,&nbsp;Ganghua Li","doi":"10.1016/j.fcr.2025.109925","DOIUrl":"10.1016/j.fcr.2025.109925","url":null,"abstract":"<div><h3>Context</h3><div>Rice is a staple food for nearly half of the global population. Traditional nitrogen (N) fertilization practices, characterized by high inputs and frequent applications, face increasing challenges related to low N use efficiency (NUE) and rising labor costs.</div></div><div><h3>Research question</h3><div>Previous studies have highlighted the benefits of controlled-release blended fertilizers (CRBF) for enhancing rice production. While limited research has been conducted on the effects of CRBF with different fertilization techniques and their applicability within rice-wheat systems.</div></div><div><h3>Methods</h3><div>A three-year field experiment was conducted at two sites with contrasting soil fertility levels. Six treatments were evaluated: broadcast conventional fertilization of urea (BCF), broadcast conventional fertilization of urea with a 25 % reduction in N (BRF), broadcast fertilization of CRBF with a 25 % reduction in N (BBF), whole-layer fertilization of CRBF with a 25 % reduction in N (WBF), side-deep fertilization of CRBF with a 25 % reduction in N (SBF), and a control without N fertilization.</div></div><div><h3>Results</h3><div>Compared to BCF, SBF increased rice yield by 3.2–8.8 % primarily due to a 5.1–6.7 % increase in panicle numbers, and all CRBF treatments enhanced NUE by 27.6–60.8 % with SBF exhibiting the highest. Notably, SBF improved rice yield and NUE by 6.0–7.0 % and 19.4–25.2 % compared to other CRBF treatments in low-fertility site, respectively, while no significant differences were observed among CRBF treatments in high-fertility site. This pattern reflects the differing responses of rice growth and soil N availability to fertilization patterns under varying soil fertility conditions. Our structural equation model results indicate that fertilization patterns primarily influence rice yield by affecting soil NH₄⁺-N levels and subsequently altering root growth. Economic analysis revealed that SBF resulted in a 37.2–40.1 % increase in net income compared to BCF, primarily due to reduced labor costs and improved yields, with smaller differences among CRBF treatments in high-fertility site.</div></div><div><h3>Conclusions</h3><div>CRBF emerges as a viable alternative to conventional N fertilization, effectively delivering high rice yields and enhanced economic returns across sites with varying soil fertility. SBF optimizes N supply and boosts rice production, yielding significant increases in both yield and NUE, particularly in low-fertility environments.</div></div><div><h3>Implications</h3><div>The findings of this study offer a novel perspective on achieving high yields and efficient fertilization strategies in rice cultivation within rice-wheat systems, potentially guiding future agricultural practices toward improved sustainability and productivity.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109925"},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855096","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":"Relationship between agricultural water and soil resources carrying capacity and crop yield with long-term plastic film mulching coupled with fertilization","authors":"Rui Jiao,&nbsp;Jiubo Pei,&nbsp;Siyin Wang,&nbsp;Mengmeng Wang,&nbsp;Yao Zhang,&nbsp;Jiahui Shi,&nbsp;Xuesong Leng,&nbsp;Heyuan Guan,&nbsp;Sidi Wang,&nbsp;Shuangyi Li","doi":"10.1016/j.fcr.2025.109927","DOIUrl":"10.1016/j.fcr.2025.109927","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;The black soil area located in northeast China is one of the four major black soil areas in the world, with developed agriculture. Due to the long-term intensive utilization of cultivated land and low nutrient input, the deterioration of soil and water resources in cultivated land is accelerated, which challenges the carrying capacity of cultivated land and water resources for grain production. However, the impact of the carrying capacity of land and water resources on grain production at field scale is still unclear. This is crucial for optimizing tillage measures to promote the steady improvement of the carrying capacity of cultivated land and water resources for food production.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;Utilizing a 37-year long-term field positioning experiment, this study quantitatively evaluated the agricultural water and soil resources carrying capacity index (WSI) under varying mulching and fertilization regimes and established their functional correlations with maize yield dynamics.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Based on the 37 year continuous mulching and fertilization experiment of brown earth at Shenyang Agricultural University, 14 treatments were selected, including single application of nitrogen fertilizer (N&lt;sub&gt;2&lt;/sub&gt;, N&lt;sub&gt;4&lt;/sub&gt;), single application of organic fertilizer (M&lt;sub&gt;2&lt;/sub&gt;, M&lt;sub&gt;4&lt;/sub&gt;), organic fertilizer+nitrogen fertilizer (M&lt;sub&gt;1&lt;/sub&gt;N&lt;sub&gt;1&lt;/sub&gt;, M&lt;sub&gt;2&lt;/sub&gt;N&lt;sub&gt;2&lt;/sub&gt;), and no fertilization (CK), under mulching (F) and non mulching (WF) conditions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The results showed that both mulching and fertilization can significantly improve the WSI and crop yield. Among them, the treatment of mulching with high organic fertilizer and nitrogen fertilizer (FM&lt;sub&gt;2&lt;/sub&gt;N&lt;sub&gt;2&lt;/sub&gt;) had the most significant effect on both carrying capacity and yield of agricultural water and soil resources. For every 0.1 unit increase in the WSI, the yield of corn under mulching and non mulching conditions can increase by 11.8 % and 12.01 %, respectively. Under mulching conditions, the average yields of N&lt;sub&gt;2&lt;/sub&gt;, N&lt;sub&gt;4&lt;/sub&gt;, M&lt;sub&gt;2&lt;/sub&gt;, M&lt;sub&gt;4&lt;/sub&gt;, M&lt;sub&gt;1&lt;/sub&gt;N&lt;sub&gt;1&lt;/sub&gt;, and M&lt;sub&gt;2&lt;/sub&gt;N&lt;sub&gt;2&lt;/sub&gt; treatments increased by 43.91 %, 71.3 %, 62.84 %, 97.38 %, 68.58 %, and 85.3 %, respectively. The WSI increased by 6.61 %, 7.47 %, 16.97 %, 20.29 %, 18.83 %, and 22.6 %, respectively; Under non mulching conditions, the average yields of N&lt;sub&gt;2&lt;/sub&gt;, N&lt;sub&gt;4&lt;/sub&gt;, M&lt;sub&gt;2&lt;/sub&gt;, M&lt;sub&gt;4&lt;/sub&gt;, M&lt;sub&gt;1&lt;/sub&gt;N&lt;sub&gt;1&lt;/sub&gt;, and M&lt;sub&gt;2&lt;/sub&gt;N&lt;sub&gt;2&lt;/sub&gt; treatments increased by 48.1 %, 65.5 %, 97.79 %, 134.72 %, 120.55 %, and 122.32 %, respectively. The WSI increased by 4.07 %, 5.58 %, 13.63 %, 18.29 %, 16.78 %, and 20.31 %, respectively. Overall, except for the CK treatment, the WSI of the mulching fertilization treatment was higher than that of the non mulching fertilization treatment.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Un","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109927"},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860321","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":"Suitability assessment of film mulching on maize production in Northwest China: Integrating meta-analysis with machine learning","authors":"Juzhen Xu ,&nbsp;Bowei Duan ,&nbsp;Yanbo Wang ,&nbsp;Xiaowei Liu ,&nbsp;Wenqing He ,&nbsp;Wangsheng Gao ,&nbsp;Yuanquan Chen ,&nbsp;Jixiao Cui","doi":"10.1016/j.fcr.2025.109919","DOIUrl":"10.1016/j.fcr.2025.109919","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context&lt;/h3&gt;&lt;div&gt;Film mulching (FM) is commonly used in Northwest China to address water scarcity in agriculture by promoting soil warming and moisture retention. However, FM is not suitable for all regions, and excessive reliance on this method can lead to overuse, which may harm the farmland ecosystem. Understanding where and how FM is most effective is crucial for ensuring its sustainable use.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This study assessed the suitability of FM on maize considering yield and water use efficiency (WUE) in Northwest China by integrating meta-analysis with machine learning techniques. In addition, the analysis aimed to assess the regional and environmental factors influencing FM performance.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;A meta-analysis was conducted, synthesizing data from 141 studies, to evaluate the influence of FM on maize yield and WUE. Machine learning models, including Random Forest regression, support vector regression, and gradient boosting regression tree, were applied to predict the regional suitability of FM based on climatic, soil, and management practices. Key factors influencing the effect of FM included climatic factors (mean annual precipitation and mean annual temperature), soil characteristics (bulk density, soil organic matter, and total nitrogen), and fertilization strategies (nitrogen and phosphorus). Pearson correlation analysis was conducted to explore the relationship between the 7 factors and the effectiveness of FM, while Random Forest was utilized to prioritize the importance of each factor.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The meta-analysis revealed that FM increased maize yield by 40.55 % and WUE by 40.79 %. Plastic mulch demonstrated superior effectiveness, improving yield by 43.68 % and WUE by 43.85 %. FM performed best under conditions of scarce resources. Among the 7 factors, mean annual precipitation, mean annual temperature, and total nitrogen were of higher importance in the prediction. Random Forest regression excelled in predicting yield and WUE changes. The spatial analysis revealed notable regional variability of FM, with the best results observed in Xinjiang and Gansu.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;This study highlighted the effectiveness of FM in improving maize yield and WUE in Northwest China, with regional variability in its performance. The results indicated that FM was most beneficial in regions with limited water and heat, particularly in Xinjiang and Gansu. Moreover, the study also demonstrated the utility of machine learning models, particularly Random Forest regression, in predicting FM suitability across regions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance&lt;/h3&gt;&lt;div&gt;This study offered valuable insights into the regional suitability of FM for maize production in Northwest China, providing guidance for agricultural policy and management decisions to enhance the sustainability of FM. In addition, by integrating meta-analysis with machine learning, it","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109919"},"PeriodicalIF":5.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852363","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":"Climate suitability determines optimal yielding of dryland maize: A validation on timely sowing as an ancient wisdom","authors":"Jing Wang ,&nbsp;Ling Zhao ,&nbsp;Meng Hao ,&nbsp;Ning Wang ,&nbsp;Qing-Hui Wen ,&nbsp;Fu-Jian Mei ,&nbsp;Aziz Khan ,&nbsp;Bao-Zhong Wang ,&nbsp;Xiao-Lin Zhang ,&nbsp;Wei Wang ,&nbsp;Chang-Lang Yang ,&nbsp;Fei Mo ,&nbsp;Xiu-Ping Tao ,&nbsp;You-Cai Xiong","doi":"10.1016/j.fcr.2025.109920","DOIUrl":"10.1016/j.fcr.2025.109920","url":null,"abstract":"<div><h3>Context</h3><div>Climate-based timely sowing is an ancient wisdom, which can facilitate crops to sufficiently utilize climate resources and harvest optimal yielding. However, few studies have validated or addressed this issue from the perspective of climate suitability degree in association with crop maturity and sowing dates.</div></div><div><h3>Objective</h3><div>1) To explore the sole and integrated effects of sowing dates and crop maturing on maize growth and yielding in dry and wet growing seasons; 2) To reveal the mechanisms of appropriate sowing date affecting yield formation in association of climatic resource utilization; and 3) to validate the ancient wisdom of timely sowing in agricultural production.</div></div><div><h3>Methods</h3><div>A two-year field experiment was conducted to evaluate the effects of three sowing dates (advanced, SD1; timely, SD2; delayed, SD3) and three maize varieties with early-, medium- and late- maturing (EM, MM and LM) in northwest China. Dry matter accumulation, grain yield, climatic suitability degree, and other related parameters were determined and analyzed.</div></div><div><h3>Results and conclusion</h3><div>Maize yield performance varied from the growing seasons (dry 2017 and wet 2018) under different sowing dates, but followed a similar trend between two years (i.e. two growing seasons, the same below). In 2017, maize yield was 11.3 % and 22.4 % greater in SD2 than that of SD1 and SD3, respectively (<em>p</em> &lt; 0.05). Similarly, in 2018, it increased by 11.1 % and 18.3 % in SD2 respectively, relative to SD1 and SD3 (<em>p</em> &lt; 0.05). As for the medium- and late-maturing varieties, it appeared to be 7.3 % and 9.8 % greater in 2017, and 25.2 % and 28.6 % greater in 2018 respectively, than the early-maturing one (<em>p</em> &lt; 0.05). Overall, the optimal yielding performance was observed in the late-maturing variety under SD2, up to 7.2 ton ha⁻¹ in 2017 and 10.38 ton ha⁻¹ in 2018 respectively, followed by the medium-maturing variety under SD2. Mechanistically, the highest climate suitability degree across whole growth period (S(C)wgp) was observed in SD2 across three varieties. Regardless of crop maturity, the S(C)wgp was significantly positively correlated with main agronomic traits. Particularly, crop growth rate, leaf area duration, net assimilation rate and maximum relative dry matter accumulation rate performed significantly better in SD2 than those of SD1 and SD3, suggesting that appropriate sowing time can maximize yield potential.</div></div><div><h3>Implications or significance</h3><div>The findings underscore the importance of timely sowing in maize production, confirming the reasonability and feasibility of timely sowing as an ancient agricultural wisdom based on climate suitability degree.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109920"},"PeriodicalIF":5.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855353","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":"Revisiting plant density by environment interaction in maize across contrasting sowing dates","authors":"Guido Di Mauro ,&nbsp;Diego H. Rotili ,&nbsp;Gonzalo Parra ,&nbsp;Brenda L. Gambin ,&nbsp;Jerónimo Costanzi ,&nbsp;José Micheloud ,&nbsp;Gustavo Martini ,&nbsp;María Paolini ,&nbsp;Raí Schwalbert","doi":"10.1016/j.fcr.2025.109917","DOIUrl":"10.1016/j.fcr.2025.109917","url":null,"abstract":"<div><h3>Context</h3><div>The definition of the agronomic optimum plant density (AOPD) in maize is a critical management practice due to seed cost and impact on final yield. Farmers often reduce plant density when planting later in the season because of the lower expected yield compared to earlier plantings. However, this practice may lead to lost yield opportunities that need to be quantified.</div></div><div><h3>Objectives</h3><div>Our objectives were i) to understand how farmers define maize plant density for different planting dates and, ii) to explore the yield response to plant density in early and late plantings across a range of yield environments (YE).</div></div><div><h3>Methods</h3><div>We explored maize on-farm records (2017–2021; n = 25,143 fields) and field experiments (n = 491 paired comparisons) across Argentina under early (ESM) and late (LSM) plantings to characterize plant density used by farmers and attainable yields at contrasting sowing dates. Then, we conducted field experiments across different YEs, where several commercial genotypes were tested at different plant densities under both ESM (n = 39 location-years) and LSM (n = 54 location-years).</div></div><div><h3>Results and conclusion</h3><div>The proportion of area with ESM and LSM varied across regions and YEs in Argentina. Farmers usually chose higher plant densities at ESM than LSM, but not necessarily ESM always out-yielded LSM in the study region. Maize response to plant density varied depending on the YE, with no apparent difference between sowing dates.</div></div><div><h3>Implications</h3><div>Although practical reasons often justify reducing plant density in later planting, farmers should base their decisions about the AOPD based on the expected YE regardless of the planting date. Accurately predicting the YE should therefore be a key priority to optimize yields and resource allocation. The expected yield in later planting seems to be currently underestimated by farmers.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"328 ","pages":"Article 109917"},"PeriodicalIF":5.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855352","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}