Agronomy Journal最新文献

{"title":"Weed emergence patterns as affected by topsoil movement within an eroded landform","authors":"Sharon K. Schneider,&nbsp;Apurba K. Sutradhar,&nbsp;Robert H. Gulden","doi":"10.1002/agj2.70029","DOIUrl":"https://doi.org/10.1002/agj2.70029","url":null,"abstract":"<p>Soil erosion significantly affects within-field soil properties, crop productivity, weed emergence patterns, and weed growth. Spatial variability in weed densities, emergence timing, weed seed production, seed viability, herbicide bioavailability, and other factors complicate weed management strategies in eroded landscapes. Reversing soil erosion by replacing translocated topsoil (soil-landscape rehabilitation) is one method to improve crop productivity of severely eroded land, but bulk soil movement changes soil properties and weed seedbanks that influence weed spatial distribution patterns, emergence, and growth. We evaluated weed community responses to soil movement within a hilly landform. Soil-landscape rehabilitation was performed by moving 15–20 cm of accumulated topsoil from the lower slope and adding it to the upper slope positions (areas of net soil loss by erosion). Adjacent plots were left in their eroded condition. Weed density and species richness were monitored for 4 years. Annual grasses dominated the weed community in the upper slope, and weed abundance was highest in the most eroded landscape positions. Soil addition improved soil characteristics for crop growth but did not increase weed densities. The density of weeds was lower where soil was removed in the first 2 years after soil movement, likely through removal of weed seeds with soil. Within 3 years of soil movement, weed abundance and species richness were the same or lower in areas of soil removal and no soil removal. In these experiments, movement of soil (and entrained weed seeds) within an eroded landform to improve productivity did not exacerbate weed issues.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497062","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":"Soil test phosphorus predicts field-level but not subfield-level corn yield response","authors":"Vaughn Reed,&nbsp;Jenni Fridgen,&nbsp;Bronc Finch,&nbsp;John Spargo,&nbsp;Josh McGrath,&nbsp;James M. Bowen,&nbsp;Gene Hahn,&nbsp;Douglas Smith,&nbsp;Edwin Ritchey","doi":"10.1002/agj2.70028","DOIUrl":"https://doi.org/10.1002/agj2.70028","url":null,"abstract":"<p>Soil test-based fertilizer recommendations traditionally serve to predict average nutrient needs across fields, but their effectiveness for precision agriculture remains uncertain. Our objectives were to evaluate whether soil phosphorus (P) concentrations predicted corn (<i>Zea mays</i>,r L.) yield response to P at the sub-field level, and to determine if soil test critical levels varied within field boundaries. We conducted research over seven growing seasons at two Kentucky sites collecting spatially dense yield response data from over 150 paired plots per field. Mehlich 3 extractable phosphorus (M3P) soil ranged from 0.8 to 63 mg kg⁻¹, with 96% of sample points falling below the University of Kentucky's fertilizer cutoff of 30 mg kg⁻¹ M3P for corn. Each plot (10⁻² ha) received 0 or 29.5 kg ha⁻¹ P. While M3P effectively predicted average field-level response, with yield increases in five of seven site-years, it failed to predict subfield responses, where only 51% of plots showed positive yield response to P application. Linear plateau models revealed that conventional statistical treatments of soil test correlation data mask important subfield variability. The poor relationship between soil test P and yield response at the subfield scale suggests that variable rate P management requires incorporating additional factors beyond soil P concentration or moving away from such deterministic models toward probabilistic models. Our findings demonstrate that while current soil test recommendations provide accurate field-scale guidance, they lack the precision required for variable rate application.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475782","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":"Using soybean historical field trial data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors","authors":"Matheus D. Krause,&nbsp;Kaio Olimpio G. Dias,&nbsp;Asheesh K. Singh,&nbsp;William D. Beavis","doi":"10.1002/agj2.70023","DOIUrl":"https://doi.org/10.1002/agj2.70023","url":null,"abstract":"<p>Soybean [<i>Glycine max</i> (L.) Merr.] provides plant-based protein for global food production and is extensively bred to create cultivars with greater productivity in distinct environments through multi-environment trials (MET). The application of MET assumes that trial locations provide representative environmental conditions that cultivars are likely to encounter when grown by farmers. A retrospective analysis of MET data spanning 63 locations between 1989 and 2019 was conducted to identify mega-environments (ME) for soybean seed yield in the primary production areas of North America. ME were identified using data from phenotypic values, geographic, soil, and meteorological records at the trial locations. Results indicate that yield variation was mostly explained by location and location by year interaction. The phenotypic variation due to genotype by location interaction effects was greater than genotype by year interaction effects. The static portion of the genotype by environment interaction variance represented 26.30% of its total variation. The observed locations sampled from the target population of environments can be divided into two or three ME, thereby suggesting that improvements in the response to selection can be achieved when selecting directly within clusters (i.e., regions and ME) versus selecting across all locations. In addition, we published the <span>R</span> package <span>SoyURT</span> that contains the datasets used in this work.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446963","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":"Does splitting the nitrogen rate for corn (Zea mays) reduce the carbon dioxide equivalence?","authors":"Skye Brugler,&nbsp;David E. Clay,&nbsp;Deepak Joshi,&nbsp;Donna M. Rizzo,&nbsp;Sharon A. Clay,&nbsp;Thandi Nleya,&nbsp;Gary Hatfield","doi":"10.1002/agj2.70025","DOIUrl":"https://doi.org/10.1002/agj2.70025","url":null,"abstract":"<p>Splitting N fertilizer application between the corn (<i>Zea mays</i>) plants pre-emergence and vegetative growth stage has the potential to reduce N losses while increasing N use efficiency. However, there are few published reports that show that splitting fertilizer reduces greenhouse gas emissions. Therefore, this study compared the CO_2e (carbon dioxide equivalent) calculated from N₂O-N and CO₂-C emissions from a single pre-emergence application of 157 kg urea- N ha⁻¹ with a nonfertilized control and two applications of 78.5 kg urea-N ha⁻¹ that were applied at pre-emergence and at the corn plants V6 growth stage. Over three growing seasons (2021, 2022, and 2023), soil temperature, moisture, N₂O-N and CO₂-C emissions were measured six times per day from when the urea was applied to harvest. N₂O-N and CO₂-C emissions were separated into pre-split and post-split periods, and carbon dioxide equivalence was calculated. Splitting the N rate: (1) increased N₂O-N emissions in 2021 and 2022; (2) reduced CO₂-C emissions during the post-split period in 2022 and 2023; and (3) did not influence total CO_2e in 2021 and reduced CO_2e emissions in 2022 and 2023. Based on these results, splitting the N fertilizer rate between pre-emergence and the corn plants V6 growth stage should be considered as a potential climate smart practice.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423997","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":"Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops","authors":"A. Thieme,&nbsp;J. Jennewein,&nbsp;W. D. Hively,&nbsp;B. T. Lamb,&nbsp;A. K. Whitcraft,&nbsp;S. B. Mirsky,&nbsp;S. C. Reberg-Horton,&nbsp;C. Justice","doi":"10.1002/agj2.70011","DOIUrl":"https://doi.org/10.1002/agj2.70011","url":null,"abstract":"<p>Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha⁻¹) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha⁻¹) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha⁻¹, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. <i>R</i>² = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. <i>R</i>² = 0.748, RMSE = 13.10 kg ha⁻¹ from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling (<i>n </i>= 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. <i>R</i>² = 0.700–0.769, RMSE = 10.70–15.40 kg ha⁻¹). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. <i>R</i>²= 0.765, RMSE = 12.37 kg ha⁻¹), management variables (species, season, adj. <i>R</i>²= 0.772, and RMSE = 12.13 kg ha⁻¹), and biophysical variables (height, fractional ground cover, adj. <i>R</i>²= 0.818, and RMSE = 10.29 kg ha⁻¹). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424001","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":"Historical changes and yield in the Ohio corn performance test: A 50-year summary","authors":"Jhonata Cantuaria Medeiros,&nbsp;Jean Zavala,&nbsp;Mohsen Shahrokhi,&nbsp;Richard Minyo,&nbsp;Allen Geyer,&nbsp;Alexander Lindsey,&nbsp;Peter Thomison,&nbsp;Osler Ortez","doi":"10.1002/agj2.21746","DOIUrl":"https://doi.org/10.1002/agj2.21746","url":null,"abstract":"<p>Reaching production potential, crop quality, and profitability are pivotal goals across cropping systems. The Ohio corn performance test (OCPT) has deployed research approaches in the last 50 years. Partnerships between Ohio State University, seed companies, and cooperating farmers have tested commercially available corn (<i>Zea mays</i> L.) hybrids across several locations in the state. This work aims to identify historical changes in agronomic characteristics, environmental factors, crop management, and their association with crop productivity and gross income over 50 years. Yield improvements were observed, from 9.34 (1972–1981) to 14.78 (2012–2021) Mg ha⁻¹. Adopting management practices such as crop rotation and soil conservation practices (e.g., minimum till, no-till, and stale seedbed) accompanied production improvements. Our results showed that seeding rate, seedling emergence, and final stands had strong correlations with yield (81%, 64%, and 82%). Regions with better weather conditions (i.e., more precipitation, higher average temperatures, lower wind speed) also had strong correlations with yield; the central region had the highest average yield. In this 50-year dataset, OCPT yields represented gross income values 40% higher compared to the average for the state of Ohio during the same period. This study indicates that yield improvements in the corn performance test have been achieved through synergistic changes in new hybrids, key management practices, and coupled with suitable growing environments. Our work reaffirms that selecting hybrids that are best adapted to specific growing environments is a primary factor in achieving high yields and profits at the farm level.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.21746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396992","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 population and family selection accuracy with statistical genetics models accounting for epistatic effects for wheat breeding","authors":"Rafael Tobias Lang Fronza,&nbsp;Henrique Caletti Mezzomo,&nbsp;Cláudio Vieira Batista,&nbsp;Estéfano Moresco,&nbsp;Kaio Olimpio das Graças Dias,&nbsp;Maicon Nardino","doi":"10.1002/agj2.70024","DOIUrl":"https://doi.org/10.1002/agj2.70024","url":null,"abstract":"<p>Few studies have investigated the effect on the genotypic value of wheat (<i>Triticum aestivum</i> L.) families with the adoption of the additive and epistatic (additive × additive) relationship matrix. The objective of this study is to select F_2:3 families of wheat by means of three statistical genetics models (without pedigree information, additive, and additive plus additive × additive epistatic) and to evaluate the selection rank between the traditional model and the model with best fit of families for recombination and for deriving progenies. The experiment was composed of a total of 880 F_2:3 families of tropical wheat, from 56 populations conducted by the genealogical method, which came from a full diallel involving the cultivars BRS 254, BRS 264, and BRS 394, CD 1303, Tbio Aton, Tbio Ponteiro, Tbio Duque, and Tbio Sossego. The pedigree matrix was calculated, obtaining approximately 20 generations of ancestry of the parents. The data were analyzed in three genetic-statistical models: Model 1—without information on family relationship; Model 2—computing the additive relationship matrix; and Model 3—including the additive and epistatic (additive × additive) relationship matrix. Using the additive and epistatic (additive × additive) pedigree matrix has a significant effect on most traits. The selection revealed families of populations with potential to be used in recombinations: BRS 254/CD 1303, Tbio Ponteiro/BRS 394, and BRS 394/Tbio Ponteiro, with genetic value to derive progenies: BRS 254/Tbio Aton, Tbio Aton/Tbio Duque, and BRS 394/Tbio Aton, and with both attributes: BRS 254/CD 1303, BRS 394/Tbio Ponteiro, and Tbio Sossego/BRS 264.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389133","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":"An inter-laboratory comparison of soil organic carbon analysis on a farm with four agricultural management systems","authors":"William Brinton,&nbsp;Bruno Basso,&nbsp;Neville Millar,&nbsp;Kris Covey,&nbsp;Charles Bettigo,&nbsp;Sindhu Jagadamma,&nbsp;Frank Loeffler","doi":"10.1002/agj2.70018","DOIUrl":"https://doi.org/10.1002/agj2.70018","url":null,"abstract":"<p>Soil organic carbon (SOC) as a key soil health indicator is integral to the soil's capacity to function as a vital living ecosystem that sustains plants, animals, and humans. Accurate SOC estimation is essential to decision-making for an increasing number of stakeholders, such as farmers, industry professionals, and policymakers, to determine the environmental benefit of agricultural practices, and more recently, allocate financial rewards through carbon market initiatives. Our study examined SOC variability in soils from four different regenerative management systems on a single farm using stratification and sample compositing, and analyzed by four different laboratories using dry combustion, the recommended analytical method, but one which varied according to laboratory standard operating procedures (SOP). Results showed significant variation in SOC levels for the same soil samples at different laboratories (1.6 ± 0.2 g kg⁻¹), variation comparable to that between the distinct management systems (1.5 ± 0.4 g kg⁻¹). Our findings show that analytical variability within and between laboratories must be considered, that use of the same laboratory, and to the extent possible the same SOP for successive SOC measurements at the same location is necessary, and that rigorous stratification alongside minimal sample consolidation should be conducted to generate analytical sample numbers that cater to logistics, economics, and scientific rigor.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389134","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":"Estimating hard winter wheat yield with historical and novel methods","authors":"Maria C. M. Sciencia,&nbsp;Cody F. Creech,&nbsp;Katherine A. Frels,&nbsp;Amanda C. Easterly","doi":"10.1002/agj2.70021","DOIUrl":"https://doi.org/10.1002/agj2.70021","url":null,"abstract":"<p>Estimating potential crop yield during the growing season allows growers to adjust inputs, set reasonable harvest expectations, and guide marketing decisions. Therefore, the ability to estimate yield is a valuable yet difficult goal for growers. To evaluate the potential of several methods of wheat (<i>Triticum aestivum</i> L.) grain yield prediction, this experiment used published methods based on phenological characteristics: stand and tiller/spike counts, and newer methods that employ image- and reflectance-based approaches, such as fractional green canopy cover (FGCC) and normalized difference vegetation index (NDVI) readings. This experiment was conducted across six locations (Banner, Box Butte, Cheyenne, Intensively Managed at Cheyenne, Deuel, and Kimball counties) in western Nebraska during 2019–2020, 2020–2021, and 2021–2022 for a total of 11 site-years. Treatments consisted of seven winter wheat varieties that were evaluated in the Winter Wheat State Variety Trials. Stand count did not show a significant correlation with wheat yield (0.09) nor model fit for yield estimation. Spike count was significantly correlated with yield (0.54), but efforts to use it to estimate final yield were not significant. Due to the inconsistency of yield prediction with historical methods, analyses of novel methods of yield estimation were warranted. NDVI and FGCC readings correlate with wheat yield and model fit efforts were successful. NDVI at Feekes 10 correlated significantly at 0.39, while FGCC had correlations of 0.56, 0.50, and 0.68 at Feekes 2, 4, and 10 (respectively). This experiment suggests that NDVI and FGCC are methods that could be used to replace outdated and laborious approaches.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362862","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":"Yield-stability selection index applied to identify top genotypes in a Hevea brasiliensis segregating population in Antioquia, Colombia","authors":"Danilo Augusto Monsalve García,&nbsp;José Miguel Cotes Torres,&nbsp;Alejandro Alberto Navas Arboleda,&nbsp;Oscar de Jesús Córdoba-Gaona","doi":"10.1002/agj2.70015","DOIUrl":"https://doi.org/10.1002/agj2.70015","url":null,"abstract":"&lt;p&gt;Rubber (&lt;i&gt;Hevea brasiliensis&lt;/i&gt;) production in Colombia has been primarily dependent on three predominant genotypes for over five decades. However, challenges such as disease susceptibility and low yield and quality underscore the need for increased genetic variability. Yield-stability indexes are essential tools for identifying superior genotypes in &lt;i&gt;Hevea&lt;/i&gt; breeding programs. In 1999, a segregating rubber plantation with 3395 trees was established at the Agrosavia El Nus Research Center. Georeferencing, tapping data, and total solids yield were assessed through 10 evaluations per tree. This study aimed to identify high-performing rubber genotypes using an adjusted total solids yield-stability index. Phenotypic values were adjusted to account for microclimate effects and temporal variation. The resulting index, applied through stratified mass selection, provided a closer approximation of genotypic performance. Based on the index, the top 50 and bottom 10 genotypes were identified, offering insights into elite individuals' contributions to total solids production. Notably, genotypes 2a13, 2a317, and 3a89 exhibited higher predicted yield-stability (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mover&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;̂&lt;/mo&gt;\u0000 &lt;/mover&gt;\u0000 &lt;annotation&gt;${hat p}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) index values, with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mover&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;̂&lt;/mo&gt;\u0000 &lt;/mover&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mn&gt;13&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;87.03&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mover&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;̂&lt;/mo&gt;\u0000 &lt;/mover&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mn&gt;317&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;78.50&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${hat p_{2{mathrm{a}}13}} = 87.03,{mathrm{;;;}}{hat p_{2{mathrm{a}}317}} = 78.50,$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mover&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;̂&lt;/mo&gt;\u0000 &lt;/mover&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 ","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":"117 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362863","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}