Edward T. Schafer Agricultural Research Center

Plants emit hundreds, if not thousands, of different volatile chemical compounds, although the function of most individual volatiles remains elusive. Individual volatiles, as well as blends of many chemicals, are likely multifunctional in regulating plant interactions with different groups of insects, including herbivores, natural enemies, and pollinators. However, research on these insect groups has historically been siloed, limiting our understanding of connections between different volatile-mediated ecological processes and how to apply this knowledge to agroecosystems. Here, we review recent literature on volatile multifunctionality in mediating plant interactions with insect herbivores, natural enemies, and pollinators. Ultimately, we propose that future research shifts towards a holistic approach in the study of volatile-mediated interactions between plants and insect communities. By elucidating how specific volatiles, chemical classes, and blends regulate behaviors across different groups of insects, we will uncover new semiochemical tools for controlling pests and protecting beneficial insects in agroecosystems.

Sugar beet (Beta vulgaris L.) storage rots significantly reduce sugar quality and economic viability of the industry. Early storage rot detection can mitigate sugar loss through timely interventions, with volatile organic compounds (VOCs) serving as potential biochemical markers. However, conventional static headspace gas chromatography-mass spectrometry (HS-GC-MS) methods for VOC detection typically require elevated incubation temperatures, unsuitable for accurately replicating real storage conditions (4°C-20°C), thus limiting their practical applicability. This study aimed to develop and optimize a low-temperature static HS-GC-MS analytical method for accurate VOC profiling in stored sugar beets. A comparison between healthy and rotted sugar beet samples identified ethanol and ethyl acetate as two promising VOC markers for early detection of storage rots. Through systematic optimization of sample preparation, calibration strategies, and static headspace sampling parameters, a sensitive and reliable low-temperature detection protocol was established. The optimized method effectively addressed matrix effects and enhanced analyte detection, yielding limits of detection (LOD) of 0.03 ppm for ethyl acetate and 1.4 ppm for ethanol, with recovery rates of 105% and 101%, respectively. This optimized approach provides a robust analytical foundation essential for integrating VOC profiling with sensor and machine learning technologies, significantly advancing real-time sugar beet storage rot monitoring and management.

Heat stress can reduce the production potential of wheat (Triticum aestivum L.) by affecting the various developmental stages of wheat including the seedling stage. Understanding the genetic basis of heat stress tolerance can help in breeding resilient wheat cultivars with improved productivity. Here, evaluation of a diverse panel of spring wheat landraces and cultivars under non‐heat stress (23°C) and heat stress (36°C) treatments in a controlled environment revealed large phenotypic and genetic variations. Heat stress negatively affected all seedling traits with the maximum reduction in root length (85.6%) and the least reduction in coleoptile length (15.44%). Moreover, based on seedling performance, we identified six highly heat tolerant (PI 366905, Kzyl Sark, Rang, Perico S, Bohr Gamh, and PI 620689) and six highly heat susceptible (CItr 17470, CItr 13270, Coeruleum, Shashi, Hallany, and Currawa) genotypes. Genome‐wide association analysis using 302,524 single nucleotide polymorphisms identified 23 marker‐trait associations (MTAs), of which 16 were associated with various seedling traits under heat stress. Gene annotation and expression analysis indicated 35 differentially expressed genes, of which 13 were considered as high‐confidence genes with functional relevance to heat stress including protein kinase, basic‐leucine zipper, UDP‐glucosyltransferase, pyrophosphate‐energized proton pump, fatty acid hydroxylase, and other classes of proteins. The MTAs and candidate genes identified in this study hold promise for developing heat‐resilient wheat cultivars through the selection of favorable alleles with gene‐specific molecular markers.

Basal stalk rot (BSR) of cultivated sunflower (Helianthus annuus) is caused by the necrotrophic fungal pathogen Sclerotinia sclerotiorum. This disease is economically significant and limits sunflower production in the northern Great Plains region of the United States. Resistance to BSR is quantitative, controlled by many genes exerting small effects on the level of resistance. This genetic complexity hinders efforts to develop sunflower hybrids with sufficient resistance. Field-based disease trials have successfully identified cultivated sunflower genotypes with partial BSR resistance but failed to determine the degree of resistance. Consequently, the objectives of this study were to (i) distinguish between highly and moderately resistant genotypes by reevaluating 60 cultivated sunflower genotypes exhibiting partial resistance in field trials using a newly developed greenhouse inoculation method with improved resolution; (ii) determine if selected genotypes identified in objective (i) are broadly resistant to diverse S. sclerotiorum isolates; (iii) assess potential host genotype-pathogen isolate interactions between sunflower genotypes and S. sclerotiorum isolates; and (iv) determine if resistant genotypes carry alleles of molecular markers previously associated with BSR resistance loci and assess the frequency of these alleles in resistant germplasm. The results of this study identified sunflower inbred lines HA 124 and HIR 34 exhibiting high levels of BSR resistance against all tested S. sclerotiorum isolates. Additionally, significant host genotype-pathogen isolate interactions were detected between sunflower lines and S. sclerotiorum isolates. This information will guide breeding efforts to improve BSR resistance and facilitate prioritizing highly resistant lines for genetic mapping and further characterization.

Abstract Parasitic infections in livestock, particularly gastrointestinal nematode infections, pose a significant threat to animal health and productivity, leading to economic losses in dairy goat operations. Early and accurate detection of parasitic infections is crucial for mitigating these impacts and preventing anemia and weight loss in infected animals. Current diagnostic methods, such as fecal egg counts (FEC) and FAMACHA scoring, are time-consuming, labor-intensive, and require skilled personnel, limiting their practical application in large-scale operations. To address this challenge, bioelectrical impedance analysis (BIA) presents a promising alternative as a rapid, non-invasive tool for assessing animal health by measuring the electrical properties of tissues. Combining BIA measurements with machine learning algorithms can enhance the accuracy of detecting parasitic infections and predicting anemia severity in dairy goats. This study collected BIA parameters, including electrical resistance and reactance, packed cell volume (PCV), FEC, and FAMACHA scores from dairy goats housed at the USDA rRsearch Station in Byron, GA. A comprehensive machine learning pipeline was developed to evaluate the predictive performance of Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), and Random Forest models for regression and classification tasks to predict relative FAMACHA score based on previously mentioned parameters. Regression models yielded varying performance levels, with Random Forest achieving the highest R² score of 0.7287, indicating a strong predictive capability compared to BPNN (R² = 0.2512) and Support Vector Regression (SVR, R² = 0.2326). Classification models demonstrated improved accuracy, with Random Forest achieving a notable accuracy of 78.74%, outperforming BPNN (54.90%) and SVM (52.65%). These results suggest that BIA-derived parameters (resistance and reactance), particularly when analyzed using Random Forest algorithms, hold significant potential for predicting and classifying parasitic load and anemia-related conditions in dairy goats. The correlation analysis revealed a non-significant negative association between FAMACHA scores and BIA electrical resistance (r = -0.08) and reactance (r = -0.11), but a strong negative association between PCV and FAMACHA scores (r = -0.93), suggesting the critical role of PCV as an indicator of anemia severity. The FEC values showed moderate positive correlations with FAMACHA scores (r = 0.41) and goat parasitic infection level (r = 0.45), indicating that parasitic loads significantly influence health outcomes. Using machine learning models in livestock health monitoring presents a promising avenue for improving early detection and management strategies for parasitic infections. Future work will integrate a larger sample size and additional physiological and environmental factors to enhance predictive accuracy and provide a holistic decision-support tool for dairy goat management.

Abstract Installation of utility-scale solar arrays for energy production has created long-term changes to landscapes that were once used for timber, pasture, or row-crop production. Vegetation under panels is necessary for runoff control and it is becoming increasingly common for sheep to be utilized in combination with mechanical mowing for vegetation management. Variation in vegetation type and biomass production may impact the effectiveness of grazing as an alternative to mechanical vegetation management. The aim of this study was to evaluate botanical composition under different grazing conditions in a fixed panel solar array that was established in 2014. The Roxboro Farm solar project (Roxboro, NC) was utilized for the study. The entire site was mowed in late April 2024 to an equal vegetation height and subdivided into three treatment areas. A control area (1.5 ha) was isolated and only mechanical vegetation management used to maintain contract specifications. The second area (Low Stock Density, LS) was 4.8 ha and managed with ewes at 368 kg/ha. Another 2.1 ha area (High Stock Density, HS) was designated and further subdivided into three paddocks (0.7 ha each) to increase stock density to 2569 kg/ha. Ewes were moved between treatment areas at the discretion of farm management to meet contract specifications, minimize overgrazing, and maintain animal health. Grazing began May 18 and concluded November 3. Botanical composition was measured April 18 before grazing and October 9 near the end of the grazing season using step-point methodology and presented as a percentage of the total plants counted. Statistical analyses were performed using the Mixed Model procedure of SAS. Vegetation yield was determined based on sheep kg*days per ha. The three HS paddocks averaged 41,899 kg*days/ha whereas the LS allocation yielded 37,089 kg*days/ha. Plant species were categorized as desirable or undesirable. In the spring, there was no difference in the proportion of undesirable plant species under panels vs. between panels (P = 0.14). However, in the fall, a greater proportion of undesirable species were present between the panels vs. under (92% vs. 85%, respectively; P < 0.05). Before grazing, HS had greater undesirables compared to LS areas (82% vs. 65%, respectively; P < 0.01). However, there was no difference between these treatments in the fall. The most common undesirable species was Broomsedge Bluestem (BS, Andropogon virginicus). Regardless of season, there was a greater proportion of BS between vs. under panels (Spring: 46% vs. 7%, Fall: 64% vs. 10%, respectively; P < 0.01). Grazing treatment had no effect on BS proportions. Low density grazing resulted in a shift to more undesirable species by the end of the grazing season. Opportunities to improve soil health through high density grazing may aid in mitigating undesirable species while increasing grazing days.

Increasing the energy density of porridges could help meet the needs of moderately malnourished, stunted children. However, it is not clear whether stunted toddlers can adequately digest and obtain energy from energy-dense porridges with thick texture. A clinical study was conducted in Bamako, Mali, using 13C-labeled substrates and serial breath sampling to determine whether stunted toddlers differed from healthy toddlers in their capacity to digest thick and thin sorghum porridges. Experimental porridges, including a traditional porridge (control), a starch-enriched calorie-dense thick porridge, and an α-amylase-thinned calorie-dense porridge, were fed to stunted (n = 24) and healthy (n = 24) 18-30-month toddlers. Breath test results were expressed as Percent Dose Recovery and curve fit using the Weibull function to determine the kinetics of starch digestion. The stunted and healthy toddlers were able to digest and oxidize the starch from traditional porridge equally well, with no statistically significant differences between the kinetic parameters of the two groups. After consumption of thickened porridge, healthy toddlers had slightly faster starch digestion kinetics with PDR curves rising more rapidly (p < 0.05) and peaking earlier in the postprandial period (p < 0.01) for healthy individuals than for stunted individuals, yet these groups did not have differences in the overall extent of starch digestion, as their final CPDR values were not significantly different. Gastric emptying rate did not differ significantly between the healthy and stunted groups. Overall, we found that thick porridge supplied digestible carbohydrates to stunted and healthy toddlers, as well as thinned calorie-dense porridge.