{"title":"Strawberry ripeness detection in complex environment based on improved RT-DETR","authors":"Guoliang Yang, Yonggan Wu, Dali Weng, 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, 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><sub>max</sub> > 30°C corrections reduced variability and enhanced <i>R</i><sup>2</sup> 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, 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, N<sub>2</sub>O, and CH<sub>4</sub> 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 N<sub>2</sub>O 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 CO<sub>2</sub>-equivalent GHG (CO<sub>2</sub>e) ha<sup>−1</sup> from baseline to high emissions. Conversely, in Texas, most treatments increased SOC, and N<sub>2</sub>O 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 CO<sub>2</sub>e ha<sup>−1</sup> 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}
Namita Sinha, Dan Jeffers, Ramandeep Sharma, Dylan Williams, Raju Bheemanahalli, Vaughn Reed, W. Brien Henry, Ebrahiem Babiker, Jagman Dhillon
{"title":"Impact of hybrids, plant population density, and nitrogen strategies on corn grain yield and quality","authors":"Namita Sinha, Dan Jeffers, Ramandeep Sharma, Dylan Williams, Raju Bheemanahalli, Vaughn Reed, W. Brien Henry, Ebrahiem Babiker, 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<sup>−1</sup>), and N rates (0, 112, 224, and 336 kg N ha<sup>−1</sup>) interact and impact plant characteristics and corn yield. Pooled over four site-years, the AONR ranged from 170 to 200 kg N ha<sup>−1</sup>. This rate maximized the grain yield to 10–15 Mg ha<sup>−1</sup> 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<sup>−1</sup>. 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, 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 (<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}
Raziel A. Ordóñez, Charles M. White, John T. Spargo, Jason P. Kaye, Matthew Ruark, Javed Iqbal, Charles A. Shapiro, Wade E. Thomason, Nicole M. Fiorellino, Louis A. Thorne, Amy Shober, John H. Grove, Sarah M. Hirsh, Ray R. Weil, Michael J. Castellano, Sotirios V. Archontoulis, Jerry J. Hatfield, Chad D. Lee, Daniel J. Quinn, Zachary P. Sanders, Zoelie Rivera-Ocasio, Sarah Tierney, Kathleen E. Arrington, Andrew M. Lefever, Mauricio Tejera-Nieves, Gerasimos G. Danalatos, Laila A. Puntel, Hanna Poffenbarger, Sam Leuthold, Jarrod Miller, Gurpal S. Toor, Tony J. Vyn
{"title":"Delta yield predicts nitrogen fertilizer requirements for corn in US production systems","authors":"Raziel A. Ordóñez, Charles M. White, John T. Spargo, Jason P. Kaye, Matthew Ruark, Javed Iqbal, Charles A. Shapiro, Wade E. Thomason, Nicole M. Fiorellino, Louis A. Thorne, Amy Shober, John H. Grove, Sarah M. Hirsh, Ray R. Weil, Michael J. Castellano, Sotirios V. Archontoulis, Jerry J. Hatfield, Chad D. Lee, Daniel J. Quinn, Zachary P. Sanders, Zoelie Rivera-Ocasio, Sarah Tierney, Kathleen E. Arrington, Andrew M. Lefever, Mauricio Tejera-Nieves, Gerasimos G. Danalatos, Laila A. Puntel, Hanna Poffenbarger, Sam Leuthold, Jarrod Miller, Gurpal S. Toor, 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><sub>x</sub></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<sub>x</sub></i> based on dY, resulting in nine state, agroecosystem region, and irrigation-specific sub-models. These sub-models predicted <i>N<sub>x</sub></i> of the validation set with a mean absolute error (MAE) of 33.0 kg N ha<sup>−1</sup>. 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<sub>x</sub></i>, ranging between 19.9 and 25.4 kg N ha<sup>−1</sup> 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}
William J. Rutland, Brian K. Pieralisi, Darrin M. Dodds, Whitney D. Crow, G. Dave Spencer, J. Wes Lowe, Brian E. Mills
{"title":"Cotton response to row pattern and plant density: Part II. Boll distribution","authors":"William J. Rutland, Brian K. Pieralisi, Darrin M. Dodds, Whitney D. Crow, G. Dave Spencer, J. Wes Lowe, Brian E. Mills","doi":"10.1002/agj2.70159","DOIUrl":"10.1002/agj2.70159","url":null,"abstract":"<p>Studies have shown cotton (<i>Gossypium hirsutum</i> L.) can sustain high yields at various row spacings and plant densities. The ability of cotton to compensate is due to boll distribution. There are numerous studies evaluating the effects of skip-row production and narrow row spacing on boll distribution. However, very little research is available evaluating the effects of wide row spacing and reduced plant density on boll distribution. Studies were conducted to determine how boll distribution is affected by plant density and row spacing. The effects of plant density and row spacing on boll distribution in cotton were investigated in Starkville, MS, on a Leaper silty clay loam (fine, smectitic, nonacid, thermic Vertic Epiaquepts) and in Stoneville, MS, on a Beulah very fine sandy loam (Coarse-loamy, mixed, active, thermic Typic Dystrudepts). Boll distribution was influenced by row spacing and plant density. As plant density increased regardless of row spacing, a greater percentage of bolls by weight are oriented toward the top of the plant. At a wider row spacing, a greater percentage of total yield occurred at positions 2, 3, 4, and 5.</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":"144997946","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}
Carlos Felipe dos Santos Cordeiro, Leonardo Vesco Galdi, Gilmar Santos Martins Junior, Alexandrius de Moraes Barbosa, Fábio Rafael Echer
{"title":"Radiation use efficiency and peanut yield as affected by planting pattern and plant density in different crop systems","authors":"Carlos Felipe dos Santos Cordeiro, Leonardo Vesco Galdi, Gilmar Santos Martins Junior, Alexandrius de Moraes Barbosa, Fábio Rafael Echer","doi":"10.1002/agj2.70152","DOIUrl":"10.1002/agj2.70152","url":null,"abstract":"<p>Adjusting plant density and planting pattern are strategies to improve the production efficiency of Virginia-type peanut (<i>Arachis hypogaea</i> L.), but little is known about their effects on radiation use efficiency (RUE), pod maturity, and pod yield. The objective of the study was to evaluate the yield components, pod maturity, and RUE of peanut based on planting patterns and plant density in areas with different cropping histories. Four experiments were conducted between 2021 and 2023 in the western state of São Paulo, Brazil. Treatments included planting patterns (single-row and twin-row) and plant densities (88,888; 111,110; 133,332; and 155,554 plants ha<sup>−1</sup>). The optimal plant density was between 111,110 and 133,332 plants ha<sup>−1</sup>. In the drier growing season the reduction of each 1000 plants ha<sup>−1</sup> in plant densities below 111,110 plants ha<sup>−1</sup> caused a pod yield decrease of 60 kg ha<sup>−1</sup> in the new field and 65 kg ha<sup>−1</sup> in the rotation field. In the wetter growing season, the increase of each 1000 plants ha<sup>−1</sup> in the plant density above 111,110 plants ha<sup>−1</sup> caused a yield decrease of 41 kg ha<sup>−1</sup> in the rotation field. Although the twin-row pattern increased leaf area index (LAI), it did not result in higher RUE or peanut yield compared to the single-row pattern. Under the lowest plant density, there was a delay in pod maturation. Our results indicate that it is possible to achieve adequate LAI, RUE, and pod maturity with plant densities between 111,110 and 133,332 plants ha<sup>−1</sup>, regardless of the planting pattern.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934873","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}
Vesh R. Thapa, Katja Koehler-Cole, Amanda Easterly, Nicolas Cafaro La Menza, Grace E. Pacheco, Bridget McKinley, Andrea Basche
{"title":"Biomass and forage nutritive value of spring-planted cover crops in a semiarid region","authors":"Vesh R. Thapa, Katja Koehler-Cole, Amanda Easterly, Nicolas Cafaro La Menza, Grace E. Pacheco, Bridget McKinley, Andrea Basche","doi":"10.1002/agj2.70154","DOIUrl":"10.1002/agj2.70154","url":null,"abstract":"<p>Integrating cover crops (CCs) into cropping systems offers multiple benefits, including soil erosion control, nitrogen cycling, organic matter accumulation, weed suppression, and forage for livestock. Biomass, a key driver of these benefits, depends on species selection and adaptation to environmental conditions. In the western US's semiarid climate—characterized by cold, dry winters and short spring growing periods—CC options are limited due to winterkill risk and limited time between main crop cultivation. This study evaluated the biomass of 20 commercially available species, including grasses (seven), legumes (four), brassicas (three), and mixtures (six), grown from March to May at Sidney and Scottsbluff, Nebraska, in 2022 and 2023. At Sidney, Jerry oat (<i>Avena sativa</i> L.) had the greatest biomass (1.13 Mg ha<sup>−1</sup>), exceeding other species by over 60%. At Scottsbluff, P919 barley (<i>Hordeum vulgare</i> L.) and a mixture of Lavina barley, 4010 pea (<i>Pisum sativum</i> L.), and Barsica rapeseed (<i>Brassica napus</i> L.) had the greatest biomass (0.71 Mg ha<sup>−1</sup>). Grasses had 140% greater biomass than legumes and brassicas. Spring-planted CCs also had supplemental forage potential, with greater crude protein and total digestible nutrients, particularly in oat and barley. Growing degree days and precipitation explained 44% and 34% of biomass variation, respectively. All species had carbon-to-nitrogen ratios below 16. Despite lower biomass than reported thresholds, modest spring biomass of some species offers an alternative to winterkill or autumn planting constraints in semiarid systems. Results underscore the importance of selecting appropriate species suited to local conditions to maximize biomass and nutritive value.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935240","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}
Bryan Whittenton, Brendan Zurweller, Jeffery Gore, Yangyang Song
{"title":"Optimal digging time of peanut in the mid-southern United States","authors":"Bryan Whittenton, Brendan Zurweller, Jeffery Gore, Yangyang Song","doi":"10.1002/agj2.70156","DOIUrl":"10.1002/agj2.70156","url":null,"abstract":"<p>Peanut (<i>Arachis hypogaea</i> L.) digging time is critical for achieving optimal grade and yield potential. Growing degree days (GDDs) indices have successfully predicted peanut growth, development, and digging timing in the United States. However, this method was developed for mid-maturing cultivars (∼140 days after planting to harvest maturity) that are no longer cultivated. No study has evaluated the accuracy of GDD models to predict peanut seed maturity for new mid-maturing cultivars in the mid-south. This 3-year study assessed the relationship between maturity indices (MIs), GDD, pod yield, and grade of mid-maturing cultivars Georgia-06G and IPG-914 at two Mississippi locations. The objective was to determine optimal digging time (ODT) for peanuts cultivated at latitudes (33–35° N) in the mid-southern production region. Results showed the ratio of black, brown (maturity index 1), and orange (maturity index 2) colored mesocarps had a weak correlation with peanut grade. Our GDD models indicated ODT for peanut production in Mississippi is ∼1835 GDDs, which corresponded to 72% black, brown, and orange pods. This ODT differed between cultivars, with Georgia-06G having roughly 50 GDDs more ODT than IPG-914. Location and year differences also existed, indicating a favorable cultivation environment can extend growth and development time, resulting in a delayed ODT. These findings provide improved estimates of ODT that can improve peanut yield and grade for peanuts grown at mid-southern US latitudes. Further research should explore contributions of individual color classes to grade and pod yield for new cultivars.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935035","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}