Agronomy Journal最新文献

{"title":"Impact of soybean planting date and row width on weed suppression and yield","authors":"Matthew Goddard,&nbsp;Maninder Pal Singh,&nbsp;Christy L. Sprague","doi":"10.1002/agj2.70155","DOIUrl":"10.1002/agj2.70155","url":null,"abstract":"<p>Variable weather patterns and extended growing seasons over the last couple of decades have prompted growers to plant soybean [<i>Glycine max</i> (L.) Merr.] earlier than the historical standard. Field experiments were conducted in Michigan over three site-years to evaluate soybean planting date, row width, and herbicide program on weed suppression and soybean yield. Planting date had minimal impact on soybean stand, except at one site-year where soil crusting reduced stand 44%–52% in early (April 12–21) compared with the typical planting date (May 11–23). Weed densities and biomass for the untreated controls were substantially higher in early compared with the typical planting in all site-years. Soybean planted in 19 cm rows (370,500 and 494,000 seeds ha⁻¹) reduced weed biomass 29%–47% compared with 76 cm rows (370,500 seeds ha⁻¹) in two of three site-years when weeds were not controlled; however, the effects of row width were not observed when a PRE herbicide was applied. Similarly, narrow row widths (&lt;76 cm) resulted in quicker canopy closure compared with 76 cm rows in two of three site-years. Regardless of row width, a PRE followed by a POST herbicide program provided the most consistent weed control. Soybean yield was 7% greater for 19 cm rows at 494,000 seeds ha⁻¹ compared with 76 cm rows in two of three site-years. However, planting date did not affect soybean yield when weed control was good in all site-years. Overall, combining narrow row widths with a complete herbicide program is equally beneficial for soybean planted early and at typical planting dates.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Preserving the art of multivariate thinking: PCA and the wisdom of experience","authors":"Margarita Hartlieb","doi":"10.1002/agj2.70151","DOIUrl":"10.1002/agj2.70151","url":null,"abstract":"&lt;p&gt;The grand tradition of science as practiced by figures like Darwin (&lt;span&gt;1859&lt;/span&gt;) or Alexander von Humboldt (&lt;span&gt;1871&lt;/span&gt;), where all observations, thoughts, and conclusions were meticulously recorded, has largely faded. Today's scientific landscape is becoming increasingly specialized, with fewer opportunities to explore knowledge in a holistic, interconnected way. Adding to this challenge, many long-standing academic positions once held by senior professors are often being remodeled or phased out. Even when successors are appointed, much of the predecessor's tacit knowledge often remains undocumented, putting a vast intellectual legacy at risk of vanishing. This makes it all more important that experienced scholars take the time to document and share their accumulated knowledge.&lt;/p&gt;&lt;p&gt;Editorials like “Notes on the Use of Principal Component Analysis in Agronomic Research” by Matthew (&lt;span&gt;2025&lt;/span&gt;) offer a valuable remedy, creating space to pass on insights gained over a lifetime that cannot easily be captured in typical research papers. Even if certain methodologies may no longer be at the cutting edge, the context, experience, and wisdom embedded in such reflections can guide new generations of scientists.&lt;/p&gt;&lt;p&gt;In this editorial, the author reflects on four decades of experience using principal component analysis (PCA) in agronomic research, highlighting its strengths as a tool for data exploration and pattern detection, particularly in multivariate datasets where traditional univariate approaches may overlook significant interactions among traits.&lt;/p&gt;&lt;p&gt;Given the wealth of practical insights, he begins the editorial by drawing on his own teaching experience, including an applied student-custom biometric dataset to illustrate how PCA captures both correlated and independent traits. Further, the author challenges the widely held belief that data must always be standardized before conducting PCA, as he shows that even PCA with the correlation matrix on unstandardized data yields the same loading coefficients as standardized data.&lt;/p&gt;&lt;p&gt;The editorial also introduces an innovative approach developed by the author himself after years of use of PCAs. He thereby uses cross-correlation of the original principal component (PC) scores to assess how the inclusion of new variables or exclusion of a variable redistributes information across components. Where a set of PC scores is cross-correlated with themselves, this is depicted as diagonal line of correlation values of 1.0 in a background of zero correlations. In the example shown, it is seen that the addition of a new variable to a five-variable PCA resulted in the original PC3 being split into two PCs at PC3 and PC4, with original PCs 4 and 5 being demoted to PCs 5 and 6.&lt;/p&gt;&lt;p&gt;In the next section, the author questions the common rule of discarding PCs with eigenvalues below 1, demonstrating through concrete examples that such components can still harbor biologically relevant signals","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Enhancing crop health monitoring: A deep dive into GNN-integrated models for wheat disease detection","authors":"Uma Yadav,&nbsp;Shweta Bondre","doi":"10.1002/agj2.70148","DOIUrl":"10.1002/agj2.70148","url":null,"abstract":"<p>Plant diseases pose an important threat to agricultural productivity, affecting both the quality and quantity of crops. Early detection and severity assessment of infections in plant crops are critical for effective disease management and minimizing crop loss. This paper proposes a methodology for detecting wheat crop diseases using hybrid deep learning models that combine graph neural networks (GNNs) with convolutional architectures. By leveraging GNN + convolutional neural network (CNN), GNN + ResNet, and GNN + Visual Geometry Group 16 (VGG16) models, we aim to enhance the ability to detect diseases from images of wheat leaves accurately. The proposed models were trained on a comprehensive dataset of wheat crop images, with extensive preprocessing, model training, and hyperparameter tuning to optimize their performance. Our results indicate that the GNN + CNN model achieved the highest accuracy at 93%, followed by GNN + ResNet at 86% and GNN + VGG16 at 82%. These findings suggest that GNN + CNN is particularly effective for disease detection, providing a high degree of accuracy and robustness. This approach shows promise for automated, precise crop disease management, offering a valuable tool for advancing agricultural productivity and disease control.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amino acids as fertilizer for agronomic crops: The next green revolution?","authors":"Ray B. Bryant","doi":"10.1002/agj2.70145","DOIUrl":"10.1002/agj2.70145","url":null,"abstract":"<p>This study sought to determine whether organic N in the form of amino acids could be used as a replacement for synthetic inorganic N fertilizer for growing an agronomic crop, and if so, would there be any agronomic or environmental benefits in doing so. A greenhouse study showed that corn (<i>Zea mays</i> L.) grows equally as well with L-lysine as the only source of N compared to that grown with ammonium nitrate. Positive results were also obtained for L-histidine, but corn did not respond the same to L-alanine or L-arginine. Amino acid profiles for corn grown with ammonium nitrate, L-lysine, and L-histidine were similar. A subsequent field demonstration showed no difference in silage or grain yield between corn grown with liquid L-lysine as N fertilizer in the form commercially produced as animal feed supplement and that of corn grown with urea ammonium nitrate as the N fertilizer. A core lysimeter study showed that the positively charged L-lysine does not leach from soil. Environmental benefits, such as reduced carbon emissions and improved water quality, derived from biosynthetic L-lysine or other amino acid production using N-fixing microorganisms for use as N fertilizer are substantial. This study provides strong incentives for research into alternative means of biosynthesizing L-lysine or other amino acids using N-fixing microorganisms, which might be the next green revolution in agriculture.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Strawberry ripeness detection in complex environment based on improved RT-DETR","authors":"Guoliang Yang,&nbsp;Yonggan Wu,&nbsp;Dali Weng,&nbsp;Lu Zeng","doi":"10.1002/agj2.70162","DOIUrl":"10.1002/agj2.70162","url":null,"abstract":"<p>Accurate and rapid detection of strawberry (Fragaria × ananassa Duchesne ex Rozier) maturity in greenhouse environments is critical for advancing mechanized harvesting, yet existing methods struggle with challenges such as small target sizes, dense clustering, and occlusion by foliage. The real-time detection transformer (RT-DETR), as a real-time end-to-end detector, eliminates the need for NMS processing and provides a baseline for real-time detection. But its performance is limited by computational inefficiency and insufficient robustness in complex agricultural scenarios. To address these limitations, we propose an enhanced strawberry maturity detection model, partical ghost convolution deformable attention simple parameter free and efficient local high feature fusion detection transformer (PDSE-DETR). The backbone network is enhanced using lightweight modules to reduce model complexity while feature extraction capability is maintained. Integrating attention mechanisms with feature pyramids to minimize background interference, boosting detection of densely clustered targets. Optimizing the loss function to improve localization accuracy for small target regression. The PDSE-DETR was validated using the strawberry dataset created in this study. Experimental results demonstrate that PDSE-DETR achieves a 2.1% improvement in average detection accuracy over RT-DETR, while reducing parameters and computational costs by 30.2% and 30.7%, respectively. These advancements enable reliable real-time maturity assessment in practical greenhouse environments, offering a scalable solution to optimize automated strawberry harvesting and reduce operational inefficiencies.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Growing hops (Humulus lupulus L.) in subtropical climates: Effects of climatic patterns on phenology and seasonal crop performance","authors":"Shinsuke Agehara,&nbsp;Roberto Marceddu","doi":"10.1002/agj2.70143","DOIUrl":"10.1002/agj2.70143","url":null,"abstract":"<p>Crop diversification has emerged as a crucial strategy for advancing agricultural sustainability and mitigating the impacts of climate change, while also presenting novel economic opportunities in emerging climatic zones. The burgeoning global craft beer industry has intensified interest in cultivating hops (<i>Humulus lupulus</i> L.) in nontraditional regions, including Brazil, the Southeastern United States, and the Mediterranean. Traditionally adapted to temperate climates, hops must be acclimated to local conditions for successful cultivation in new environments. This study assessed various methodologies for calculating growing degree days (GDDs) and found that Method I demonstrated superior stability for both vegetative and reproductive phases, though Method II exhibited a better overall fit. While the application of <i>T</i>_max &gt; 30°C corrections reduced variability and enhanced <i>R</i>² values, no single method proved definitively superior. Analysis indicated elevated GDD requirements during vegetative stages, attributable to increased thermal averages across the 2-year study period. Positive correlations between GDDs and biometric data suggest distinctive growth responses in subtropical environments compared to temperate regions. Quality evaluations revealed significant variability in bittering and aromatic compounds, with spring 2021 showing higher overall quality. These findings advocate for the viability of a double annual harvest as a strategic approach to optimizing hop production in subtropical climates. The results underscore the necessity for appropriate infrastructure to support post-harvest processing and provide valuable insights for local agricultural stakeholders and brewing industries.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Management alternatives for climate-smart agriculture at two long-term agricultural research sites in the United States: A model ensemble case study","authors":"Ellen D. v. L. Maas,&nbsp;Debjani Sihi","doi":"10.1002/agj2.70146","DOIUrl":"10.1002/agj2.70146","url":null,"abstract":"<p>Greenhouse gas (GHG) emissions reduction efforts are underway to mitigate climate change worldwide. Climate-smart agriculture (CSA) practices have been shown to both increase soil organic carbon (SOC) inputs and reduce net greenhouse gas emissions (GHGnet). We evaluated the GHGnet of several management practices with three biogeochemical models (APSIM, Daycent, and RothC) at two sites with contrasting soils, climates, and cropping systems. Additionally, two future climate scenarios (baseline and high-emissions) provided alternative outcomes of SOC, N₂O, and CH₄ by 2050. In Michigan, most biochar and residue retention with no-till treatments increased SOC stocks; leguminous cover crops, no-till, and reducing fertilizer input lowered N₂O emissions. The lowest biochar treatment lowered GHGnet in the baseline climate scenario, but all other management treatments increased GHGnet under both baseline and high emissions, and all management scenarios increased a mean of 8.0 Mg CO₂-equivalent GHG (CO₂e) ha⁻¹ from baseline to high emissions. Conversely, in Texas, most treatments increased SOC, and N₂O was relatively constant. Every no-till treatment reversed GHGnet in both the baseline and high-emissions climate scenarios but all management scenarios increased a mean of 0.6 Mg CO₂e ha⁻¹ under high emissions. At both sites under high-emissions climate change, cover crops and no-till resulted in the lowest GHGnet overall. Overall, the study showed that no-till, especially with residue retention, and cover crops are important CSA practices to lower the GHGnet of agriculture, but there remains much room to find even more effective solutions to adapt to climate change.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Impact of hybrids, plant population density, and nitrogen strategies on corn grain yield and quality","authors":"Namita Sinha,&nbsp;Dan Jeffers,&nbsp;Ramandeep Sharma,&nbsp;Dylan Williams,&nbsp;Raju Bheemanahalli,&nbsp;Vaughn Reed,&nbsp;W. Brien Henry,&nbsp;Ebrahiem Babiker,&nbsp;Jagman Dhillon","doi":"10.1002/agj2.70135","DOIUrl":"10.1002/agj2.70135","url":null,"abstract":"<p>Corn (<i>Zea mays</i> L.) is a staple food and feed worldwide, and it is imperative to fill the existing corn yield gap. Agronomic optimum plant population (AOPP) and nitrogen rate (AONR) are key factors to consider for improving and maintaining corn production. However, the relationship between corn hybrids at variable planting densities across different N rates on plant morphology, grain yield, and grain quality is not yet fully understood. Therefore, a 2-year multi-site study aimed to assess how corn hybrids with and without <i>Bt</i> traits (DKC 70-27 and DKC 70-25, respectively), plant population (75,000, 87,500, 100,000, and 112,500 plants ha⁻¹), and N rates (0, 112, 224, and 336 kg N ha⁻¹) interact and impact plant characteristics and corn yield. Pooled over four site-years, the AONR ranged from 170 to 200 kg N ha⁻¹. This rate maximized the grain yield to 10–15 Mg ha⁻¹ with no differences noted due to hybrids or plant population. Although a three-way interaction between site-year, hybrid, and plant population was noted, an AOPP was immeasurable, and yield seldom increased when plant population exceeded 87,500 plants ha⁻¹. In conclusion, these findings suggest that both plant population and N rates can be optimized to close yield gaps, increase corn grain yield, and improve corn quality, offering sustainable agricultural solutions for corn production in the mid-southern United States.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital soil mapping via machine learning of agronomic properties for the full soil profile at within-field resolution","authors":"Meyer P. Bohn,&nbsp;Bradley A. Miller","doi":"10.1002/agj2.70144","DOIUrl":"10.1002/agj2.70144","url":null,"abstract":"<p>Fine-resolution maps of agronomic soil properties are essential for capturing within-field variability, supporting precision agriculture, improving understanding of soil–crop interactions, and providing reliable inputs for agroecosystem models. This study evaluated the use of digital soil mapping (DSM) with machine learning to predict 18 properties to a depth of 200 cm. Prediction performance peaked at shallow subsurface depths (15–30 cm), where the influence of dynamic anthropogenic disturbances diminished, and the relationship with processes captured by remote sensing remained strong. Total nitrogen, total organic carbon, and calcium showed the highest accuracy for surface depths (&lt;30 cm) with model efficiency coefficient (MEC) of 0.68–0.79, while sand, clay, and K at mid-depths (30–60 cm) exhibited reasonable accuracy (MECs 0.42–0.5). About 17% of models performed worse than the observed mean baseline. Particle size fraction models showed reduced accuracy at the surface, likely due to episodic surficial processes like erosion. However, performance improved in mid-depths and decreased at greater depths due to lithologic discontinuities. While most models’ MEC declined with depth, root mean squared error remained low due to the homogeneity of parent material. This suggests low spatial accuracy may be acceptable if error across all locations is minimal, which is more important for applications that require minimized error propagation (e.g., crop modeling). Covariate importance analysis showed terrain variables remained predictive at greater depths, while surface imagery became less informative. Trend analysis by hillslope position demonstrated DSM's ability to capture site differences, such as the divergence of topographic patterns with different land management practices.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Delta yield predicts nitrogen fertilizer requirements for corn in US production systems","authors":"Raziel A. Ordóñez,&nbsp;Charles M. White,&nbsp;John T. Spargo,&nbsp;Jason P. Kaye,&nbsp;Matthew Ruark,&nbsp;Javed Iqbal,&nbsp;Charles A. Shapiro,&nbsp;Wade E. Thomason,&nbsp;Nicole M. Fiorellino,&nbsp;Louis A. Thorne,&nbsp;Amy Shober,&nbsp;John H. Grove,&nbsp;Sarah M. Hirsh,&nbsp;Ray R. Weil,&nbsp;Michael J. Castellano,&nbsp;Sotirios V. Archontoulis,&nbsp;Jerry J. Hatfield,&nbsp;Chad D. Lee,&nbsp;Daniel J. Quinn,&nbsp;Zachary P. Sanders,&nbsp;Zoelie Rivera-Ocasio,&nbsp;Sarah Tierney,&nbsp;Kathleen E. Arrington,&nbsp;Andrew M. Lefever,&nbsp;Mauricio Tejera-Nieves,&nbsp;Gerasimos G. Danalatos,&nbsp;Laila A. Puntel,&nbsp;Hanna Poffenbarger,&nbsp;Sam Leuthold,&nbsp;Jarrod Miller,&nbsp;Gurpal S. Toor,&nbsp;Tony J. Vyn","doi":"10.1002/agj2.70150","DOIUrl":"10.1002/agj2.70150","url":null,"abstract":"<p>Predicting crop nitrogen (N) fertilizer needs is a major challenge in contemporary agriculture. Despite the success of current N recommendation tools, environmental concerns over N pollution from agriculture, and the adoption of improved corn (<i>Zea mays</i> L.) technologies with enhanced N efficiencies highlight the need for more accurate N fertilizer recommendation systems. Here, we aimed to develop a methodology to predict corn N requirements based on delta yield (dY = maximum yield−unfertilized yield). To develop this delta yield-based nitrogen (dY-based N) tool, we selected 486 quadratic-plateau corn yield response to N curves (from 732 N rate trials across northern US) to calculate dY and N fertilizer required to reach the yield plateau (N<i>_x</i>). The economic optimum nitrogen rate (EONR) was calculated using different fertilizer:crop price ratios (PR). The response curve outputs were then partitioned into calibration and validation sets. The calibration set was used to select linear models to predict <i>N_x</i> based on dY, resulting in nine state, agroecosystem region, and irrigation-specific sub-models. These sub-models predicted <i>N_x</i> of the validation set with a mean absolute error (MAE) of 33.0 kg N ha⁻¹. Predicted values from the site-year quadratic-plateau response fits were used to improve further predictions’ outcomes. Predictions of EONR based on dY had a lower MAE than the predictions of <i>N_x</i>, ranging between 19.9 and 25.4 kg N ha⁻¹ depending on the PR, highlighting the system's predictive power. The exclusion of non-responsive and linear-response trials in our proposed dY-based approach enables future model refinement to improve EONR prediction accuracy across a broader range of yield responses to fertilizer-N rates. The proposed dY-based N system, which integrates both economic and agronomic inputs (including management, environmental effects on soil N supply, and maximum yields), could help to reduce N losses and provide functional benefits for N optimization.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}