Managing Prior Converted Hydric Soils to Support Agriculture Production and Maintain Ecosystem Services: A Dedicated Outreach to the Agriculture Community

Hydric soils and prior converted soils are frequently used for agricultural production. Agriculture production and their associated agribusinesses are the chief economic sector; thus, agriculture is critical for rural prosperity. However, the continuous production of grain crops increases the risk of disease and insect outbreaks, which may lead to soil nutrient exhaustion or substantial usage of annual fertilizer amendments, loss of soil carbon, and soil structure degradation attributed primarily to tillage, decrease in biodiversity, and increased soil compaction. At the David M. Barton Agriculture Research Center at Southeast Missouri State University, our focus has been to support profitable agriculture production and environmental stewardship. We have developed a decade-long research program specializing in subsurface controlled irrigation with the gradual development of edge-of-field technologies. We further developed a constructed wetland to address nutrient pollution concerns with confined feeding operations. Pastures associated with the confined feed facility and the constructed wetland have initiated a soil health program. Our evolution has now permitted the David M. Barton Agriculture Research Center to become a regional center to showcase the relationships that support both profitable agriculture and environmental stewardship

Unoccupied aerial systems temporal phenotyping and phenomic selection for maize breeding and genetics

Emerging tools in plant phenomics and high throughput field phenotyping are redefining possibilities for objective decision support in plant breeding and agronomy as well as discoveries in plant biology and the plant sciences. Unoccupied aerial systems (UAS, i.e. drones) have allowed inexpensive and rapid remote sensing for many genotypes throughout time in relevant field settings. UAS phenomics approaches have iterated rapidly, mimicking genomics progression over the last 30 years; the progression of UAS equipment parallels that of DNA-markers; while UAS analytics parallels progression from single marker linkage mapping to genomic selection. The TAMU maize breeding program first focused on using UAS to automate routine traits (plant height, plant population, etc.) comparing these to ground reference measurements. Finding success, we next focused on developing novel measurements impractical or impossible with manual collection such as plant growth and vegetation index curves. UAS plant growth curves measured in a genetic mapping populations has allowed discovery of temporal variation in quantitative trait loci (QTL). Now, phenomic selection approaches are being tested using temporal UAS, as first described using near infrared reflectance spectroscopy (NIRS) of grain. Phenomic selection is similar to genomic selection but uses a multitude of plant phenotypic measurements to identify relatedness and predict germplasm performance. Phenotypic measurements are thus treated as random markers with the underlying genetic or physiological cause remaining unknown. Using multiple extracted image features from multiple time points, genotype rankings have been successfully predicted for grain yield. Among the most exciting aspects have been identifying novel segregating physiological phenotypes important in prediction, which occur in growth stages earlier than previously evaluated. Similarly, UAS have allowed investigating plant responses to biotic and abiotic stress over time. UAS findings and approaches permit new fundamental plant biology and physiology research, which is catalyzing a new era in the plant sciences

Development of Perennial Grain Sorghum

Perennial germplasm derived from crosses between Sorghum bicolor and either S. halepense or S. propinquum is being developed with the goal of preventing and reversing soil degradation in the world’s grain sorghum-growing regions. Perennial grain sorghum plants produce subterranean stems known as rhizomes that sprout to form the next season’s crop. In Kansas, breeding perennial sorghum involves crossing S. bicolor cultivars or breeding lines to S. halepense or perennial S. bicolorn × S. halepense breeding lines, selecting perennial plants from F2 or subsequent populations, crossing those plants with S. bicolor, and repeating the cycle. A retrospective field trial in Kansas showed that selection and backcrossing during 2002–2009 had improved grain yields and seed weights of breeding lines. Second-season grain yields of sorghum lines regrowing from rhizomes were similar to yields in the first season. Further selection cycles have been completed since 2009. Many rhizomatous lines that cannot survive winters in Kansas are perennial at subtropical or tropical locations in North America and Africa. Grain yield in Kansas was not correlated with rhizomatousness in either Kansas or Uganda. Genomic regions affecting rhizome growth and development have been mapped, providing new breeding tools. The S. halepense gene pool may harbor many alleles useful for improving sorghum for a broad range of traits in addition to perenniality

Effect of grain coverage disruption on aflatoxins in maize and sorghum

Abstract Aflatoxins are dangerous mycotoxins in crop production, demonstrated to reduce crop quality and value through both pre‐ and post‐harvest contamination. Aflatoxin contamination is encountered much more frequently in maize (Zea mays L.) than its close relative sorghum [Sorghum bicolor (L.) Moench]. This study focused on evaluating the effect of grain coverage on aflatoxin accumulation in both crops; comparing coverage of the female inflorescence (exposed sorghum grain vs. husk covered maize grain) as well as the impact of paper bag coverage during pollination activities. The maize and sorghum female inflorescence were further manipulated by either decreasing or increasing the grain coverage. Two specific hypotheses were statistically tested for aflatoxin accumulation in grain from inoculated inflorescences: aflatoxin accumulation changes in maize and sorghum inflorescences when they are modified from their natural state, and aflatoxin accumulation varies between natural and artificial covering of inflorescences. Aflatoxin data collected from 2 yr of inoculating Aspergillus flavus into maize ears and sorghum panicles indicated that modifying the inflorescences of both species had the largest effects in increasing aflatoxin amounts. Substituting natural coverage of maize and bagging sorghum panicles increased aflatoxin accumulation significantly. This study demonstrated that sorghum may accumulate substantial aflatoxins when it is inoculated and the inflorescence is modified by covering with a paper pollinating bag. Substituting maize husks with paper pollinating bags also substantially increased aflatoxin accumulation in maize

Results of Group-I models.

The results include results from regressions for each model, and most important variables for each model for each regression type, and accuracies of each model for each regression type.</p

Accuracies generated by different models with different regressions from Group-III models, for vegetative reproductive stage.

(a) Model-III-1: ΣVI-SUMs and time series CHM for vegetative reproductive stage, (b) Model-III-2: ΣVI-AUCs and time series CHM for vegetative reproductive stage, (c) Model-III-3: ΣVI-SUMs and CHM of DAP 117th for vegetative reproductive stage, (d) Model-III-4: ΣVI-AUCs and CHM of DAP 117th for vegetative reproductive stage, (e) Model-III-5: ΣVI-SUMs for vegetative reproductive stage, (f) Model-III-6: ΣVI-AUCs for vegetative reproductive stage, and (g) Model-III-7: Time series of CHM for reproductive stage. Accuracies from elastic net (EN), lasso (Lasso), ridge (Ridge), and random forest (RF) models have been shown along the Y-axes.</p

Accuracies generated by different models with different regressions from training data optimization test.

The X-axes show “Percentage” of data used as training data and the Y-axes show the corresponding accuracies. For each model results from elastic net (EN), lasso (Lasso), ridge (Ridge), and random forest (RF) have been shown. The models tested were for–(a) time series CHM for vegetative growth stage, (b) time series CHM for reproductive stage, (c) ΣVI-SUMs for vegetative growth stage, (d) ΣVI-SUMs for reproductive stage, (e) ΣVI-AUCs for growth stage, and (f) ΣVI-AUCs for reproductive stage.</p

Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression

Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest