Dale Bumpers Small Farms Research Center

The length of ewe productive life (LPL), defined as the number of days between the first and last lambing, is a key indicator of ewe longevity and is directly related to the sustainability of the sheep industry. Therefore, the primary objective of this study was to investigate systematic effects influencing LPL in Katahdin sheep. The LPL of 10,474 Katahdin ewes (69.5% with uncensored and 30.5% with right-censored observations) born between 1992 and 2021 in 58 flocks located across the United States were analyzed. The Kaplan-Meier (K-M) and Cox proportional hazard (Cox PH) methods were used to estimate survival probability. Four Cox PH models were evaluated. Model 1 included contemporary group (CG; flock-year-season of ewe birth) as a random effect and the ewe's dam's age (EDA), ewe's own birth-rearing type (BR; 1/1, 2/1, 2/2, 3/2, 3/3, with the digit-3 including lamb counts ≥ 3), and age at first lambing (AFL) as fixed effects. Models 2 to 4 were an extension of model 1. Model 2 also included average lamb birth weight (ABW) per ewe lifetime, while model 3 included average lamb weaning weight (AWW) per ewe lifetime. Both ABW and AWW were fitted as fixed effects. Model 4 fitted all previous effects together. The factors CG, BR, ABW, and AWW affected LPL (P < 0.05) in all models in which these effects were fitted. The EDA effect only influenced LPL (P < 0.05) in model 1, while AFL had no effect (P > 0.05) in any model. The median LPL ranged from approximately 2 to 3 yr, depending on the risk factors analyzed. In general, Katahdin ewes themselves born in multiple litters, and that produced lambs weighing approximately 5kg at lambing and 20 to 25kg at weaning (over their lifespan) had better survival probability. Although the LPL of Katahdin sheep is relatively low, it appears to be a consequence of voluntary culling due to its association with both ABW and AWW. Future studies should quantify the rate of involuntary culling in Katahdin ewes to identify whether longevity indicator traits should be included in more comprehensive breeding objectives.

Abstract Remote sensing tools, along with Global Navigation Satellite System cattle collars and digital soil maps, may help elucidate spatiotemporal relationships among soils, terrain, forages, and animals. However, standard computational procedures preclude systems‐level evaluations across this continuum due to data inoperability and processing limitations. Deep learning, a subset of neural network, may elucidate efficiency of livestock production and linkages within the livestock‐grazing environment. Consequently, we applied deep learning to environmental remote sensing data to (1) develop predictive models for yield and forage nutrition based on vegetation indices and (2) at a pixel‐level (per 55 m2), identify how grazing is linked to soil properties, forage growth and nutrition, and terrain attributes in silvopasture and pasture‐only systems. Remotely sensed data rapidly and non‐destructively estimated herbage mass and nutritive value for enhanced net and primary productivity management in livestock and grazing systems. Cattle grazed big bluestem (Andropogon gerardii ‘Vitman’) with 182% greater frequency than orchardgrass (Dactylis glomerata L.) in the pasture‐only system. Real‐time estimates of vegetative bands may assist in predicting grazing pressure for more efficient pasture resource management. Cattle grazing followed distinct soil‐landscape patterns, namely reduced cattle grazing preference occurred in areas of water accumulation, which highlights linkages among terrain, soil‐water movement, soil properties, forage nutrition, and animal grazing response spatially and temporally. Results from this study could be scaled up to improve grazing management among the largest land‐use category in the United States, that is, grasslands, which would allow for sustainable intensification of forage‐based livestock production to meet growing demands for environmentally responsible protein.

Abstract Background and ObjectivesFumonisin (FUM), a secondary metabolite from Fusarium spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois‐centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa‐centric model. Corn samples (n = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (n = 89).FindingsApplying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois‐ and Iowa‐centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois‐ and Iowa‐centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%.ConclusionsFUM‐GBM analyses determined the top influential predictor for the Illinois‐centric model was satellite‐acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa‐centric model was precipitation (PRCP) in October.Significance and NoveltyResults indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.

Forage sorghum is an alternative source for biofuel feedstock production and may also provide forage for livestock operations. Introducing biofuel feedstock as a dual-use forage to livestock operations has the potential to increase the adoption of biofuel feedstock production. However, additional technical agronomic information focusing on tillage, row arrangement, and harvest date for forage sorghum planted into pasturelands intended for dual use is needed. Three tillage treatments, disking and rototilling (RT), chisel plow (CP), and no tillage (NT), and two row arrangement treatments, single-row planting with 76.2 cm rows and twin rows of 17.8 cm on 76.2 cm centers, were tested for effects on forage sorghum yield in a 3-cut system. This study tested two sites in Booneville, AR, from 2010 to 2012. Several interactions with year were detected, likely due to large precipitation differences within and among years. The year greatly affected the yield, with greater (p &lt; 0.05) yields in year 1 compared to years 2 and 3 in both locations. No till resulted in lower yields in some years and harvest dates, though no clear trend was detected among tillage treatments over years. Twin rows generally did not improve yield, except for the third harvest date at one location. No strong trends for tillage or row arrangement effects were observed in this study. Inconsistencies may have resulted from the strong influence of year or interactions of multiple factors, which may challenge producers interested in utilizing forage sorghum for biofuels and livestock feed.

Abstract Economic selection indexes provide the best method to achieve genetic gain for multiple traits simultaneously. Hair sheep enterprises, particularly in environments with substantial gastrointestinal nematode challenge, may benefit from incorporating parasite resistance in a selection index. However, obtaining the relative economic value (REV) for parasite resistance is challenging; information on its impact on productivity is incomplete, as are the costs of treatment given anthelmintic resistance. The National Sheep Improvement Program (NSIP) currently provides producers with an index designed to increase total weight of lamb weaned per ewe lambing (TW). Our objective was to assess the effects of including fecal egg count, an indicator of parasite resistance, as an additional goal trait. Including the benchmark scenario with TW as its sole objective, 10 indexes were constructed using standard index theory. Post-weaning fecal egg count (PFEC) was added to this breeding objective with a REV of -1 and the REV for TW was varied from +1 to +5 at +0.5 increments. Weaning weight, number of lambs born and weaned, and PFEC were the selection criteria, modelled as either phenotypes or estimated breeding values (EBV). Phenotypes and EBV were simulated for 1,000 lambs by Cholesky decomposition and used to generate index scores. Ten replicates were run and the top 3% of males and 26% of females were retained on index scores. Annual responses in the goal traits were estimated. Responses in PFEC were expressed as a percentage, assuming a mean PFEC of 2,089 eggs/g as in NSIP. Concordances with those selected in the benchmark scenario were determined. Using EBV as selection criteria, TW increased by 1.7 kg/yr in the benchmark scenario (Table 1). However, unfavorably, PFEC increased by 2.3%/yr. When phenotypic criteria were used, TW increased by 0.5 kg/yr, and PFEC by 0.3%/yr. Once added to the breeding objective, PFEC decreased regardless of the REV of TW. However, response in TW also reduced, although less so as its REV increased. With EBV as the criteria, the REV of +3 for TW appeared preferable: PFEC reduced by 7.1%/yr with 98% of the response in TW retained. With phenotypic selection criteria, PFEC reduced by 5.2%/yr with 94% of the response in TW retained. With +3 REV for TW and EBV as the selection criteria, on average 65% and 82% of males and females, respectively, were chosen in common with the benchmark. When phenotypic criteria were used, those percentages were 60% and 80%, respectively. Implementing a more holistic index with REV of +3 for TW and -1 for PFEC would result in reasonable responses in both traits. However, producers will need to decide if reduced gains in TW is acceptable to improve parasite resistance in a hair sheep breeding program.

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg−1) for Cu and 23.4 (mg kg−1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.

Long-term continuous deposits and well-preserved thick loess sections are natural archives for the reconstruction of global climate change. However, the loess sections are often discontinuous owing to soil erosions and accumulations. This study aimed at reconstructing long-term continuous paleoclimatic records by integrating the target sections of two typical loess sequences in northeast (NE) China. One section, the Dajiugang (DJG) section, was investigated and sampled, and the Sanbahuo (SBH) section was used as the target for integrating and reconstructing the paleoclimate. Sedimentary, pedogenesis, and luminescence dating analyses were conducted to analyze the potentials for reconstructing the paleoclimate by integrating two typical loess sections. The results indicated that the paleoclimate records derived from the DJG section can be used to reconstruct the continuous paleoclimate of NE China by integrating them with the SBH section. The paleoclimate information inferred from the DJG section could impute paleoclimate data from 71 to 11.7 ka, which were missing in the SBH section. The paleoclimate of NE China generally consisted of three periods from 71 to 11.7 ka: cold and dry from 29 to 11.7 ka, alternately cold and warm from 57 to 29 ka, and warm and wet to cold and dry from 71 to 57 ka. Integrating the target loess sections can contribute to reconstructing regional long-term continuous paleoclimate records where discontinuous loess sequences occurred.

The U.S. Maternal Hair Index was designed by the National Sheep Improvement Program (NSIP) to increase total weight of lamb weaned per ewe lambing (TW). Producers are interested in adding gastrointestinal nematode (GIN) resistance to this breeding objective since parasitism causes substantial economic losses. The NSIP provides producers with estimated breeding values (EBV) for postweaning fecal egg count (PFEC), an indicator of GIN resistance. Our objective was to gauge the effects of including PFEC as another selection criterion and goal trait. Selection index theory was used to construct 11 indexes. First was a benchmark index with TW as the goal. Weaning weight, and number of lambs born and weaned, were selection criteria. An index was then designed with PFEC added as a selection criterion. In 9 more indices, PFEC was also included as a goal trait, where the economic value of TW was scaled relative to that of PFEC. PFEC received a scaled economic value of −1 with that of TW increasing from +1 to +5 at +0.5 increments. Selection criteria were modeled as EBV or phenotypes. Annual genetic responses in goal traits were predicted. The top 3% of males and 26% of females were selected. Breeding values and phenotypes were simulated for 200 lambs by Cholesky decomposition and used to generate index scores, with 100 replicates run. Concordances with the animals selected in the benchmark scenario were determined. Using EBV as the selection criteria, TW increased by 1.46 kg/yr in the benchmark scenario. However, unfavorably, PFEC increased by 2.24%/yr. When using phenotypic criteria, TW increased by 0.52 kg/yr and PFEC by 0.28%/yr. Once added as a goal trait, PFEC decreased regardless of the scaled economic value of TW. However, responses in TW were also reduced, although less so as its scaled economic value increased. A scaled economic value of +3 for TW appeared reasonable over other choices with 79% of the emphasis placed on TW in the breeding objective. With EBV as selection criteria, PFEC declined by 7.96%/yr with 98% of the response in TW retained. Also, on average, 64% of males and 80% of females were chosen in common with the benchmark. With phenotypic selection criteria, PFEC declined by 5.13%/yr and 94% of the response in TW was retained; 61% of males and 80% of females were chosen in common with the benchmark. Implementing an index with scaled economic values of +3 for TW and −1 for PFEC would balance gains in TW with reductions in PFEC.