Forage Potential of Summer Annual Grain Legumes in the Southern Great Plains

Baath, Gurjinder S.; Northup, Brian K.; Rocateli, Alexandre C.; Gowda, Prasanna H.; Neel, J. P. S.

doi:10.2134/agronj2017.12.0726

Cited by 33 publications

(30 citation statements)

References 110 publications

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“…Although temperature and precipitation are highly variable, making it difficult to define a crop that performs well region-wide [17], the mild winter favors cool-season crops for grazing animals [18]. The region is known for its mixed farming practices, producing both livestock and grain.…”

Section: Agroecology and Farming Systems In The Southern Great Plainsmentioning

confidence: 99%

Small Grains as Winter Pasture in the Southern Great Plains of the United States

Kumssa¹,

Anderson²,

Butler³

et al. 2020

Grasses and Grassland Aspects

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Small-grain cereals are widely adapted and used as annual cool-season pastures in the Southern Great Plains (SGP) of the United States, where livestock and forage production are the largest contributors to agricultural income. The advantage of growing small grains in the region is evident due to the widespread adoption and flexibility of production for grain only, forage only, or both grain and forage (i.e., dual purpose). Farmers in the SGP often prefer the use of small grains for dual purpose mainly because of alternative income options from livestock and/or grain, ensuring stable income especially when product prices fluctuate with market demands. Small-grain forage is exceptionally important during autumn, winter, and early spring when forage availability from other sources is low. By providing nutritionally high-quality forage, small grains minimize the need for protein and energy supplements. Besides being used for winter pasture, small grains also serve as cool-season cover crops. While small grains offer different advantages in the integrated crop-livestock system in the region, farming management practices can play an important role to maximize the benefit. The objectives of this chapter are to summarize the significance of small grains as winter pasture and highlight the production status of each small-grain species in the SGP of the United States.

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Section: Agroecology and Farming Systems In The Southern Great Plainsmentioning

confidence: 99%

Small Grains as Winter Pasture in the Southern Great Plains of the United States

Kumssa¹,

Anderson²,

Butler³

et al. 2020

Grasses and Grassland Aspects

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“…It covers approximately 3 million hectares of Central Rolling Red Plains of the United States, including parts of Kansas, Oklahoma, and Texas (Bushong, Arnall, & Raun, 2014). In the state of Oklahoma, winter wheat encompasses 75% of the cropland (Patrignani, Lollato, Ochsner, Godsey, & Edwards, 2014) and is produced under rain‐fed conditions (Baath, Northup, Rocateli, Gowda, & Neel, 2018; Vitale, Godsey, Edwards, & Taylor, 2011).…”

Section: Introductionmentioning

confidence: 99%

Nitrogen management impact on winter wheat grain yield and estimated plant nitrogen loss

et al. 2020

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Method of N application in winter wheat (Triticum aestivum L.) and its impact on estimated plant N loss has not been extensively evaluated. The effects of the pre‐plant N application method, topdress N application method, and their interactions on grain yield, grain protein concentration (GPC), nitrogen fertilizer recovery use efficiency (NFUE), and gaseous N loss was investigated. The trials were set up in an incomplete factorial within a randomized complete block design and replicated three times for 5 site‐years. Data collection included normalized difference vegetation index (NDVI), grain yield, and forage and grain N concentration. The NDVI before and after 90 growing degree days (GDD) were correlated with final grain yield, grain N uptake, GPC, and NFUE. At Efaw location, NDVI after 90 GDDs accounted for 58% of variation in grain yield and 51% variation in grain N uptake. However, NDVI was found to be a poor indicator of both GPC and NFUE. Grain yield was not affected by the method and timing of N application at Efaw. Alternatively, at Perkins, topdress applications resulted in higher yields. The GPC and NFUE were improved with the topdress applications. Generally, topdress application enhanced GPC and NFUE without decreasing the final grain yield. The difference method used in calculating gaseous N loss did not always reveal similar results, and estimated plant N loss was variable by site‐year, and depended on daily fluctuations in the environment.

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“…Tepary bean is a vining, warm-season legume species originated from the areas of the southwestern United States and northwestern Mexico, that may have value for multiple uses in dryland agricultural systems. Due to its spreading growth habit, and the ability to generate high N biomass with limited soil moisture, tepary bean could be an ideal summer forage for the Southern Great Plains [14]. This first study investigating the application of NIRS to attributes of forage quality in tepary bean showed that the technique could aid in quantifying its role in meeting animal nutrition needs.…”

Section: Parametermentioning

confidence: 99%

“…All three techniques can be employed for rapid NIR-based predictions of CP and IVTD in soybean forage samples, with SVM would be the best choice. Soybean was initially introduced as a forage into the US in the 19th Century, but is now one of the most widely grown grain legumes in the Southern Great Plains [14]. In the last two decades, there has been increased interest from researchers in utilizing soybean as a summer forage in the US [28][29][30].…”

Section: Soybeanmentioning

confidence: 99%

“…This work includes species such as guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), soybean (Glycine max), and pigeon pea (Cajanus cajan), given past research, and the potential for expansion of use of these species across the southern US and other similar environments. There are also other warm-season legumes that may be capable of providing high-quality forage for summer grazing [3,13,14]. However, developing NIR calibration equations for every species can become challenging for public or private laboratories that test forage quality.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Baath

Gowda

et al. 2020

Sensors

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Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2cv/R2v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cv/R2v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.

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Forage Potential of Summer Annual Grain Legumes in the Southern Great Plains

Cited by 33 publications

References 110 publications

Small Grains as Winter Pasture in the Southern Great Plains of the United States

Small Grains as Winter Pasture in the Southern Great Plains of the United States

Nitrogen management impact on winter wheat grain yield and estimated plant nitrogen loss

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

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