Forecasting crop performance through non-destructive tools is crucial for enabling timely, data-driven decisions for sustainable cotton production. This study evaluated physiological, PIs [chlorophyll index (CI), nitrogen balance index (NBI)], vegetation, VIs [normalized difference vegetation index (NDVI), normalized difference red edge (NDRE)], and biochemical, BIs [leaf nitrogen, petiole nitrate-N] indicators to forecast aboveground biomass accumulation (AGB), nitrogen (N) uptake, and lint yield in cotton. We hypothesized that the predictive strength of each indicator would vary by type and growth stage. Field experiments were conducted across five site-years in West and North Florida, USA, using six N rates (0-252 kg N/ha) in four replications. Quadratic regression (QR) model identified PIs at peak flowering and VIs at first flower and cutout growth stages as the most robust predictors of AGB. A random forest regression (RF) model also showed similar results, with PIs at peak flowering and VIs at peak flowering and first flower as the best indicators for AGB prediction. A strong relationship of BIs with N uptake at bloom and post-bloom growth stages evaluated through QR and RF supports their use for early reproductive assessment. Lint yield predicted using QR and RF was best forecasted by VIs at peak flowering, and cutout. Principal component analysis confirmed mid-to-late season VIs as major drivers of AGB and lint yield variability, while as early reproductive BIs as major indicators of N uptake. This research establishes a robust framework for real-time indicator and growth stage-based biomass, N uptake, and yield forecasting in cotton.

Nitrogen (N) fertilizer recommendations for rainfed cotton ( Gossypium hirsutum L.) in Florida have remained static at 67 kg N ha −1 since 1981. A study conducted at the West Florida Research and Education Center in Jay, FL, in 2022 and 2023, evaluated the response of cotton (cv. DP‐2038) to six N rates (0, 50, 101, 151, 202, and 252 kg ha −1 ), using a randomized complete block design with four replications. Total rainfall during growing season was 923 mm in 2022 and 710 mm in 2023. Results show petiole nitrate N concentration of 5050–9700 mg kg −1 at bloom and 363–1138 mg kg −1 at 4 weeks after bloom is sufficient for rainfed cotton in Florida. Aboveground biomass achieved with 252 kg N ha −1 was 25%–92% higher than with other N rates. The greatest seed cotton (3761 kg ha −1 ) and lint yield (1817 kg ha −1 ) were recorded with 151 kg N ha −1 , but this was statistically similar ( p > 0.05) to 252, 202, and 101 kg N ha −1 . Lint turnout decreased with increasing N rate, with maximum in the unfertilized control and minimum with 252 kg N ha −1 . Fertilizer N use efficiency (NUE) and internal NUE decreased by 76% and 53%, respectively, with an increase in N rate from 50 to 252 kg ha −1 . The best fit linear plateau model indicated an agronomic optimum rate of 127 kg N ha −1 . These results show the need to revise N recommendations for rainfed cotton in Florida to maximize yield and economic returns while promoting sustainable agricultural practices.

This study investigates the use of high-resolution satellite (PlanetScope), and uncrewed aerial vehicle (UAV) multispectral imagery combined with machine learning to predict biomass and nitrogen (N) content in cotton biomass. This study was conducted in Northwest Florida to explore the potential of using high-resolution satellite data alone or in combination with UAV for remote sensing. While UAV multispectral imagery offers high spatial and spectral resolution, it involves higher costs and operational complexity. By contrast, a low-cost RGB camera presents a simpler, user-friendly alternative for capturing relevant crop data. Additionally, the PlanetScope satellite imagery, with its automatic acquisition and extensive coverage, minimizes the need for on-site operations, offering a scalable solution for precision agriculture. The study tested the effectiveness of satellite-only data in cotton monitoring, comparing its performance to UAV multispectral sensors. The primary objective was to assess whether satellite imagery alone, or in combination with UAV multispectral or RGB data, enhances machine learning model predictions and whether RGB imagery provides sufficient spatial detail to complement satellite data. The results showed that integrating UAV multispectral data with satellite imagery significantly improved prediction accuracy, achieving an R2 of 0.779 for N uptake and 0.885 for biomass prediction. Satelliteonly data provided moderate accuracy for N uptake estimation (R2=0.50-0.54) but performed well for biomass estimation (R2=0.57-0.72). Notably, fusing satellite data with UAV RGB data offered comparable performance, highlight a scalable and cost-effective alternative for precision agriculture. However, combining UAV RGB data with satellite imagery also yielded strong results, demonstrating its value as a cost-effective and scalable solution for real-time crop monitoring. These findings underscore the value of multisource data fusion for real time crop monitoring and nutrient management.

For irrigated cotton (Gossypium hirsutum L.) in Florida, the current nitrogen (N) fertilizer recommendation is 67 kg N ha-1 and has not changed in the last 40 years despite changes in cultural practices and development of new varieties. A study was conducted at three locations to re-evaluate cotton [Delta Pine 2038 B3XF (DP 2038)] response to six N rates (0, 50, 101, 151, 202, and 252 kg ha-1), using a randomized complete block design with four replications on sandy soils. The objectives of this study were to quantify N rate effects on (1) growth, (2) in-season petiole nitrate-N (PNN), and (3) yield and N use efficiency, with the goal of N rate optimization. Results indicate that leaf area index was maximized at 101-151 kg N ha-1. Application of 101 kg N ha-1 maintained PNN sufficiency throughout bloom. PNN between 7800 and 8692 mg kg-1 at bloom, and 1733 and 4500 mg kg-1 at 4 weeks after bloom can be considered sufficient for optimum yield. Statistically, no significant increase in biomass and lint yield was found beyond the application of 101 kg N ha-1. A negative correlation was found between N applied and fertilizer N use efficiency (r = -0.85), and internal N use efficiency (r = -0.61). The best-fit linear plateau model showed 113 kg N ha-1 as the agronomic and economic optimum N rate for irrigated cotton in Florida. Yield goal-based analysis indicates that 50 kg N ha-1 (45 lbs N acre-1) is required to produce 2.5 bales of cotton ha-1 (similar to 1 bale acre-1; 1 bale = 218 kg lint), enabling site-specific, yield-targeted N application.

State-level cooperative extension services provide fertilizer recommendations for row crops in the United States. Of these, nitrogen (N) recommendations are arguably the most important because N is the most common yield-limiting nutrient in nonlegume crop production systems. Throughout the peanut ( Arachis hypogaea L.) growing region of the United States, Cooperative Extension Services generally recommends 22–67 kg N/ha credit to crops following peanut, likely due to the assumption that peanut, being a legume, contributes N to the following crop. The body of peer-reviewed literature indicates that N credits from peanut to the subsequent crop are negligible. Recent literature indicates that apparent differences in yield following peanut compared to a nonlegume are a result of nonlegume crop residue favoring N immobilization rather than N mineralization from peanut residue. Taken together, recent research corroborates the few previous scientific publications addressing the issue, namely, that cooperative extension service recommendations to reduce N fertilization to crops after peanut are not supported by the peer-reviewed literature. Future field research should include summer fallows to determine if yield differences between legumes and nonlegumes are due to N credits by the legume or N immobilization by nonlegumes. Data on N loss pathways following peanut are needed to identify management strategies that can mitigate N losses after peanut harvest. In conclusion, the preponderance of peer-reviewed science does not support current Extension recommendations regarding peanut N credits to the following crop.

Off-target drift of herbicides can seriously reduce peanut (Arachis hypogea L.) growth and yield and is of great concern to growers who will need to manage sensitive crops near new herbicide-tolerant crops. Field experiments were conducted in 2021 and 2022 with 25% labeled rates of dicamba, glufosinate, glyphosate, lactofen, and paraquat to simulate drift on peanut. The objective was to evaluate the effects of low-rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery-based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury from the herbicides applied at vegetative (V3) and reproductive (R3) growth stages. Peanut suffered greater yield reduction (33%) when exposed to the herbicides at R3 than at V3 growth stage (19%) across all herbicides applied. The order of herbicides that induced yield reductions in peanut was glyphosate > glufosinate = dicamba > paraquat = lactofen. Regardless of exposure timing, NDVI values generated from UAV imagery could not differentiate paraquat or lactofen injury from the weed-free check. However, NDVI values could differentiate between injured and weed-free check plants up to 2 and 4 weeks after treatment (WAT) for dicamba at R3 and V3 exposure timing, respectively, up to 4 WAT for glufosinate, and 8 WAT for glyphosate. NDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating, which can be influenced by individual estimation bias.

Herbicides are the primary tool for controlling weeds in peanut ( Arachis hypogaea L.) and are crucial to sustainable peanut production in the United States. The literature on chemical weed management in peanut in the past 53 yr (1970 to 2022) in the United States was systematically reviewed to highlight the strengths and weaknesses of different herbicides and identify current research gaps in chemical weed management. Residual weed control in peanut is achieved mainly with dimethenamid- P , ethalfluralin, pendimethalin, and S -metolachlor. More recently, the use of the protoporphyrinogen oxidase inhibitor flumioxazin and acetolactate synthase inhibitors, such as diclosulam, for residual weed control in peanut has increased considerably. Postemergence broadleaf weed control in peanut is achieved mainly with acifluorfen, bentazon, diclosulam, imazapic, lactofen, paraquat, and 2,4-DB, while the graminicides clethodim and sethoxydim are the major postemergence grass weed control herbicides in peanut. Although several herbicides are available for weed control in peanut, no single herbicide can provide season-long weed control due to limited application timing, lack of extended residual activity, variability in weed control spectrum, and rotational restrictions. Therefore, effective weed management in peanut often requires herbicide mixtures and/or sequential application of preplant-incorporated, preemergence, and/or postemergence herbicides. However, the available literature showed a substantive range in herbicide efficacy due to variations in environmental conditions and flushes of weed germination across years and locations. Despite the relatively high efficacy of herbicides, the selection of herbicide-resistant weeds is another area of increasing concern. Future research should focus on developing new strategies for preventing or delaying the development of resistance and improving herbicide efficacy within the context of climate change and emerging constraints such as water shortages, rising temperatures, and increasing CO 2 concentration.

Best management practices that optimize agronomic performance and make Brassica carinata production compatible with existing cropping systems are crucial for the establishment of a carinata supply chain in the southeastern United States. To this end, research was carried out to quantify land preparation method (conventional, no-till, broadcast-disc, and ripper-roller) and seeding rate effects on (1.12, 5.60, 10.09, and 14.57 kg seed ha(-1)) on B. carinata physiology, yield, and seed chemical composition. Data were collected on days to 50% flowering; canopy cover; gaseous exchange parameters (leaf net photosynthesis, stomatal conductance, transpiration, intercellular CO2, and water use efficiency); leaf area index; root weight; shoot weight; aboveground biomass; yield; and seed chemical composition. Leaf net photosynthesis was affected by land preparation treatment, being greater under the ripper-roller treatment, particularly during bolting. On the other hand, a decrease in photosynthesis, stomatal conductance, and water use efficiency was observed as seeding rate increased, especially during bolting. Carinata seed under the ripper-roller land preparation had the greatest oil content but lowest glucosinolates and protein contents. Yield did not respond to land preparation. Yield was minimized (732 kg seed ha(-1)) at the 1.12 kg ha(-1) seeding rate and maximized (1087 kg seed ha(-1)) at the 5.6 kg ha(-1) seeding rate. No land preparation by seeding rate interaction was observed for gas exchange parameters and LAI during any of the growth stages, nor was any interaction observed for yield. Carinata's physiological response to seeding rate did not depend on land preparation method employed.

The commercialization of crops that are resistant to 2,4-D plus glyphosate provided an opportunity to growers to apply the herbicide postemergence. However, the potential drift injury of these herbicides to peanuts grown near crops that are resistant to 2,4-D plus glyphosate is concerning. Field experiments were conducted in 2019 and 2020 to evaluate peanut response when exposed at 25, 50, and 75 d after planting (DAP) corresponding to vegetative, flowering, and pod development stages, respectively, to reduced rates of 1/512x, 1/128x, 1/32x, and 1/8x of the labeled rate of 2,4-D plus glyphosate (i.e., 1,077 + 1,132 g ae ha(-1), respectively). 2,4-D plus glyphosate was more injurious to peanuts when exposed at 25 DAP compared with 50 and 75 DAP. Similarly, greater canopy height (12%) and canopy width (16%) reductions were observed at 25 DAP compared with 50 and 75 DAP exposure timings (3% to 9%). This result indicates that peanut is more sensitive to 2,4-D plus glyphosate exposure at the vegetative growth stage than at the flowering and pod development stages. However, yield reductions (13% to 16%) were not different between 25, 50, or 75 DAT exposure timings. Regression analysis indicated a linear response for peanut injury, canopy height, width, and yield reduction with an increasing rate of 2,4-D plus glyphosate. The highest rate of 2,4-D plus glyphosate (1/8x of the label rate) resulted in 38%, 22%, and 23% peanut injury, canopy height, and width reduction at 4 wk after treatment, and 33% yield reduction. There was a correlation between peanut injury and yield reduction, with Pearson's rho values ranging from 0.70 to 0.73. The findings suggest that peanut injury rating data after 2,4-D plus glyphosate drift can be useful for estimating potential yield losses.

Growers in the United States (US) southeast are often recommended to reduce nitrogen (N) fertilization after peanut (Arachis hypogaea L.) by cooperative Extension services. However, these guidelines are not supported by the scientific literature. An experiment was conducted to quantify N contributions from peanut residues to a subsequent carinata (Brassica carinata) crop. A 3 (history: cotton [Gossypium hirsutum L.], peanut, fallow) x 5 (N rates: 0, 34, 67, 101,134 kg N ha(-1)) factorial randomized complete block split-plot design was conducted over four site-years during the 2018-2019 and 2019-2020 seasons at Jay, FL, USA. Peanut and cotton were planted under strip tillage, whereas carinata was drilled into peanut and cotton residues and weed-free fallow plots. Although peanut residues accumulated 54-78 kg N ha(-1), inorganic N content behind former peanut plots at the 0-15 cm depths, ranged from 6 to 8 and 8 to 11 kg N ha(-1) in 2018-2019 and 2019-2020 season, respectively. Cropping history differences for carinata normalized difference vegetation index (NDVI) were pronounced at lower N rates in one out of four site-years during which NDVI behind former cotton plots was lower than former peanut and fallow plots. Carinata seed yield behind former peanut plots was similar to unfertilized fallow based on four site-years of data. Nonlinear regression models predicted that N rates required to optimize carinata seed yield following peanut would exceed 134 kg N ha(-1) thereby indicating negligible peanut N credits. These results support a growing body of literature that suggests minimal N credits after peanut under humid southeastern US conditions.

The widespread adoption of dicamba-resistant crops has increased the applications of newer dicamba formulations for weed control. However, there is a concern for potential off-target injury and yield reduction to peanuts (Arachis hypogea L.) planted in close proximity to dicamba-resistant crops. Field experiments were conducted in the summer of 2019 and 2020 to evaluate peanut response to reduced rates (1/512X, 1/128X, 1/32X, and 1/8X the label rate, 564 + 1280 g ae ha(-1)) of dicamba plus glyphosate (XtendiMax plus Roundup PowerMax) at 25, 50, and 75 days after planting (DAP) which corresponds to vegetative, flowering, and pod development stages, respectively. Peanut exposure to dicamba plus glyphosate at 25 DAP resulted in 1.6-2.3 times greater injury and 3.4-8.5 times greater height and canopy width reductions compared to 50 and 75 DAP exposures. Peanuts suffered greater yield reduction (18%-19%) when exposed to dicamba plus glyphosate at 25 and 75 DAP than at 50 DAP (10%). Regression analysis indicated a significant linear response for peanut injury (except at 8 weeks after treatment), canopy width reduction, and yield reduction with an increasing rate of dicamba plus glyphosate. Dicamba plus glyphosate at 1/512X rate resulted in a 3% peanut yield reduction, whereas a 41% yield reduction was observed at 1/8X rate. Correlation analysis, with Pearson's rho values ranging from 0.81 to 0.86, showed that peanut injury can be a useful predictor for estimating yield reduction. Therefore, extreme care must be taken to prevent drift occurrence or spray-tank contamination when applying dicamba plus glyphosate on XtendFlex crops near peanut fields.

As a recently introduced crop in the United States, there are limited data regarding temporal nutrient accumulation and partitioning dynamics of Brassica carinata (carinata). A four site-year study was conducted in Jay, FL and Salisbury, NC during the 2018-2019 and 2019-2020 growing seasons. Three carinata genotypes (DH-157.715, M-01, and Avanza 641) proposed by the industry to represent early-, mid-, and full-season genotypes, respectively, were sampled at multiple growth stages and partitioned into leaves, stems, reproductive parts (flowers plus pods), and seed to determine biomass and nutrient accumulation across three genotypes in Florida and one full season genotype in North Carolina. Averaged over two site-years and genotypes in Florida, accumulation (per ha) of 169 kg N, 22 kg P, 160 kg K, 58 kg S, 475 g Zn, and 218 g B was required to produce 1635 and 10,872 kg ha(-1) of seed yield and biomass, respectively. Nutrients with high harvest index values included P (60%), N (55%), S (32%), and Mg (29%). Averaged over two site-years in North Carolina, accumulation (per ha) of 178 kg N, 26 kg P, 87 kg K, 24 kg S, 416 g Zn, and 127 g B produced 2428 and 9102 kg ha(-1) of seed yield and biomass, respectively. Nutrients with greatest harvest index values were P (57%), N (50%), S (32%), and Mg (26%). Internal efficiency of N, P, and K, measured as slopes of seed yield regressions over nutrient uptake across all genotypes and locations were 16, 83, and 8 kg seed yield per kg N, P, and K uptake, respectively. These results describe temporal nutrient accumulation and partitioning in carinata and are critical to refine nutrient management strategies and guide fertilizer application decisions.

The increased incidence of glyphosate-resistant weeds has led to an exponential increase in the use of glufosinate on glufosinate-resistant corn, cotton, and soybean crops. Field experiments were conducted in 2021 and 2022 to evaluate peanut response to glufosinate at 25 and 60 d after planting, corresponding to vegetative (V3) and reproductive (R4) growth stages, at 1.2, 4.7, 18.9, 75.5, and 302 g ai ha(-1 )representing 1/514 to 1/2 of the labeled rate of 604 g ha(-1 ). Peanut injury and canopy and yield reductions from glufosinate were <10% when applied at 1.2, 4.7, and 18.9 g ha(-1 ). However, at 75.5 and 302 g ha(-1 ) peanut injury ranged from 24% to 72% at the V3 exposure timing and 33% to 54% at the R4 exposure timing. Similarly, glufosinate applied at 75.5 and 302 g ha(-1 ) reduced peanut canopy width by 10% to 23% at the V3 exposure timing and by 43% to 57% at the R4 exposure timing. Averaged across exposure timing, peanut yield was reduced by 15% and 61% when glufosinate was applied at 75.5 and 302 g ha(-1 ), respectively. Averaged across rates, peanut yield reduction was 18% at the V3 exposure timing, with glufosinate at 298 g ha(-1 ) required to cause an estimated 50% reduction in yield. Peanut yield was reduced by 20% when glufosinate was applied at the R3 peanut growth stage, whereas glufosinate applied at 243 g ha(-1 ) caused an estimated 50% reduction in yield. There was no difference in normalized difference vegetative index (NDVI) values between untreated plants and peanut exposed to glufosinate at 1.2, 4.7, and 18.9 g ha(-1 ). However, peanut exposed to glufosinate at 75.5 and 302 g ha(-1 ) was distinguished from untreated plants with lower NDVI values. Based on the Pearson correlation coefficient, the best timing for assessing potential yield reduction based on injury was between 2 and 4 wk after treatment.

Weed interference is a major factor that reduces peanut (Arachis hypogaea L.) yield in the United States. Peanut growers rely heavily on herbicides for weed control. Although effective, herbicides are not a complete solution to the complex challenge that weeds present. Therefore, the use of nonchemical weed management options is essential. The literature on weed research in peanut in the past 53 yr in the United States was reviewed to assess the achievements and identify current research gaps and prospects for nonchemical weed management for future research. More than half (79%) of the published studies were from the southeastern United States. Most studies (88%) focused on weed management, while fewer studies (12%) addressed weed distribution, ecology, and competitive mechanisms. Broadleaf weeds were the most frequently studied weed species (60%), whereas only 23% and 19% of the published studies were relevant to grasses and Cyperus spp., respectively. Seventy-two percent of the published studies focused on curative measures using herbicides. Nonchemical methods using mechanical (5%) and preventive (13%) measures that influence crop competition and reduce the buildup of the weed seedbank, seedling recruitment, and weed seed production have received less attention. In most studies, the preventive weed management measures provided weed suppression and reduced weed competition but were not effective enough to reduce the need for herbicides to protect peanut yield. Therefore, future research should focus on developing integrated weed management strategies based on multiple preventive measures rather than one preventive measure combined with one or more curative measures. We recommend that research on mechanical weed management should focus on the role of cultivation when integrated with currently available herbicides. For successful weed management with lasting outcomes, the dominant weed communities of specific target locations should be addressed within the context of climate change and emerging constraints rather than focusing on single problematic species.

Row croppers in the Southeast United States (SE US) are interested in diversifying their cropping systems and increasing revenue by growing a winter cash crop between summer crops, especially cotton (Gossypium hirsutum L.) and peanut (Arachis hypogaea L.). Double-cropping carinata (Brassica carinata A. Braun) between summer crops has potential to boost grower revenue and increase land use efficiency in the region. Given that this winter crop is fairly new in the SE US, its successful establishment in the region depends on its rotational fit into current cropping systems. Since incorporating carinata into the existing cropping systems in the region could influence the productivity of those systems, it is crucial to determine what changes row croppers should expect. To resolve this, research was conducted to quantify the effects of summer cropping history on the performance of carinata as part of diversified crop rotations in the SE US. A randomized complete block design with eight replications was implemented in Jay, Florida, over three years. Summer cropping history treatments were peanut, cotton, and summer fallow. Data was collected on carinata aboveground biomass and stover [including carbon (C) and ni-trogen (N)]; yield (including yield components); and seed chemical composition (glucosinolates concentration, protein and oil contents, and oil composition). Carinata biomass, biomass C and N, stover residue, stover residue C and N, and stover residue C:N ratio were not affected by summer cropping history across years. Likewise, yield and seed chemical composition were not affected by summer cropping history. In terms of combined productivity of summer crops and carinata, cotton-carinata was the most productive system while fallow-carinata was the least productive. Overall, results from this study show that the insensitivity of carinata yield or seed quality to previous summer crops allows for flexibility in integrating carinata into existing rotations in the SE US. This simplifies the agronomic management of carinata as a biofuel crop since growers can concentrate on meeting yield goals without the risk of affecting yield or seed quality with different summer cropping histories.

Carinata (Brassica carinata A. Braun) is an emerging oilseed crop with potential as a dual use winter cover/cash crop in the southeastern US region. Although carinata is historically cultivated as a spring crop in northern latitudes, incorporating carinata into southeastern US cropping systems can provide winter/cover ecosystem services and a bio-feedstock for a high value, renewable aviation fuel without displacing feed and food crops. In this study, our major objective was to quantify the agronomic performance and stability of selected carinata genotypes across several locations in the southeastern US. Extensive field evaluations of twelve, elite carinata genotypes, arranged in a randomized complete block design with four replications, were conducted from 2016 to 2019 across Mississippi, Alabama, Georgia, Florida, South Carolina, and North Carolina. Data was collected on days to 50% bolting, days to 50% flowering, plant height, grain yield, and test weight. Results demonstrated the ability to produce viable grain yields across the region, but also highlighted the impact of freezing temperatures on winter production. In total 20% of all environments were lost to mortality due to freezing temperatures. Overall, genotype 15 produced the highest grain yield across individual environments, topping the trial in 74% of all environments. However, both crossover and non-crossover genotype x environment interactions were detected for agronomic traits, with problematic crossover interactions more prevalent for days to 50% bolting and days to 50% flowering. Our results also suggest the southeastern US be separated into three mega environments to include 1) northern Georgia, South Carolina, and North Carolina, 2) southern and central Georgia and Alabama, and 3) northern Florida. Future efforts to identify advanced breeding lines and/or commercial seed products with adaptation to the region should consider field testing in each of these mega environments.

CONTEXT: Brassica carinata is usually grown in a double-cropping system with cotton and peanut in southeastern USA. One challenge related to this system is to define the best sowing dates to minimize the climatic risk and improve the chances for increased farmers' profitability. OBJECTIVE: The objective was to determine the best sowing dates for carinata-cotton and carinata-peanut double-cropping systems in different locations of the southeastern USA. METHODS: A calibrated crop simulation DSSAT model was used to simulate carinata life cycle and cotton and peanut yields for 9 locations in 4 states, with carinata sowing dates ranging from early September to March using 38 years of weather data (1981-2018), assuming that cotton and peanut sowing occurred immediately after the carinata harvest. RESULTS AND CONCLUSIONS: Results indicated that the optimal sowing date varied according to location. For Shorter, AL, Midville, GA, and Florence, SC, the occurrence of extreme weather events during carinata cycle were minimized, and the summer crop yields were the highest when carinata was sown at the end of October. For Fairhope, AL, Jay, and Quincy, FL, results showed that carinata sowing must occur in early November to minimize the climatic risks of a double-cropping system. For Live Oak and Citra, FL, carinata sowing is recommended for mid- to late-November. Advancing carinata sowing to September resulted in an increased number of low temperature (LT) events during the carinata reproductive phase. The delay in carinata sowing to January or later exposed carinata crop to a larger number of High Temperature (HT), and considerably reduced the yield potential of the peanut and cotton summer crops. SIGNIFICANCE: Our results quantified the climatic risks to the off-season carinata crop itself and to the subsequent summer crop, contributing to the sustainable expansion and commercial production of carinata in the southeastern USA.

Soybean (Glycine max [L.] Merr.) is a major crop cultivated in the United States, and it is well adapted to different latitudes and weather conditions. However, grain yield variability of rainfed soybean across early and late sowing dates and among maturity groups (MGs) has not been well evaluated in the southeastern United States. This study uses crop variety trials and soil reports to calibrate the CROPGRO-Soybean model and then assesses yield variability for different sowing dates and MGs across sites within the southeastern United States. After model calibration and evaluation, soybean yield was simulated across 36 yr at eight sites for 16 sowing dates and three cultivars from MGs V, VI, and VII. The results show that the model accurately simulated soybean yield for MGs across different soil types, where the root mean square error (RMSE) across cultivars ranged from 423 to 589 kg ha(-1) and across sites ranged from 219 to 684 kg ha(-1). This facilitated the assessment of sowing date effects on yield variability, where simulations showed high yields for sowing dates from April to May, but with higher variability for April sowing dates and low grain yield for sowing dates in July. Our approach successfully illustrates the use of soil reports and crop variety trials to assess the best sowing dates and MGs for soybean production in this region.

This publication serves as a guide to assess freeze damage and discuss management issues related to freeze damage of carinata in the southeastern United States. Written by Michael J. Mulvaney, Mahesh Bashyal, Joseph E. Iboyi, Ramdeo Seepaul, Pratap Devkota, Ian Small, and David Wright, and published by the UF/IFAS Agronomy Department, revised July 2021.

The environmental consequences of using nonrenewable fossil fuels have motivated a global quest for sustainable alternatives from renewable sources. Carinata has been developed as a low carbon intensity, non‐food oilseed biomolecular platform to produce advanced drop‐in renewable fuels, meal, and co‐products. The crop is widely adaptable to grow in the humid subtropical and humid continental climatic regions of Asia, Africa, North America, South America, Europe, and Australia as a spring or winter crop. Carinata is heat tolerant, resistant to diseases and seed shattering with lower water‐use requirements than other oilseed brassicas. Adopting carinata in double‐cropping systems would require continuing research to integrate crop biology with agronomy, to understand growth and development and its interaction with agricultural inputs and management. Site‐specific best management agronomic practices and crop improvement research to develop frost‐tolerant, early‐maturing, nutrient use‐efficient, and high yielding varieties with desirable oil content and fatty acid profile will enhance the crop's adaptability and economic viability. The exploitation of intra‐ and interspecific and intra‐ and intergeneric diversity will further enhance carinata productivity and resistance to biotic and abiotic stresses. This review attempts to present a comprehensive description of carinata's biology, beginning with its origin and current state of distribution, availability of genetic and genomic resources, and a discussion of its morphology, phenology, and reproduction. A detailed analysis of the agronomy of the crop, including planting and germination and management practices, is presented in the context of crop growth and development. This will facilitate global adoption, sustainable production, and commercialization of carinata as a dedicated biofuel oilseed crop in diverse cropping systems and growing regions of the world, including the Southeast United States.