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Greenhouse plant science assays have been impacted by microclimates which causes significant level of noise to plant growth measurement data. Researchers and scientist have been randomizing pots locations, which helps to re-distribute the noise, but does not remove the noise. The impacts from microclimates can be eliminated by shuffling plants, but there has been no study on the optimization of shuffling pattern, such as the frequency and moving distance for the pots. It is important to quantitatively study the microclimates in the greenhouse, so we can optimize the shuffling pattern accordingly. The aim of this study was to propose a computer modeling approach for simulating microclimate in the greenhouse, and then use the simulation result to optimize pot movement distance and frequency. A computational greenhouse model was developed using inputs from real design, materials and location of a Purdue Lily greenhouse in West Lafayette, Indiana. Microclimate variables, including ambient temperature and lighting radiation over 24 h and 7 days were predicted with the simulation model. Thermometers and lighting sensors were also distributed in the greenhouse for the ground-truth measurements over a seven-day period. Comparison of measured microclimate variables with predicted variables obtained from the computational model demonstrated that the simulation model could precisely predict temperatures and light radiation at any time, and at different positions in the greenhouse. Optimized pot movement frequency and distance were then determined with the simulation result. The new shuffling pattern can remove over 90% of the microclimate variance but could save more than 95% shuffling efforts compared with non-stop movement.

Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

Optimal N application is critical to ensure profitable soft red winter wheat (Triticum aestivum L.) production and limit negative environmental effects. This study was conducted using 12 wheat lines grown in two Ohio and six Virginia environments to determine the effects of differentially split N rates on grain yield, yield components, and N-related traits. Upon analysis of wheat lines in two contrasting photoperiod groups, it was discovered that wheat lines containing the photoperiod sensitivity allele (Ppd-D1b) conferred a significant (P < 0.05) yield advantage under N-limited conditions in Ohio and across N treatments in half of the Virginia testing environments. This resulted from increased harvest index, grains per square meter, number of kernels per spike, and floret fertility in Ppd-D1b lines relative to lines with the insensitive allele (Ppd-D1a). However, wheat genotypes in the photoperiod-insensitive allelic group had significantly higher (P < 0.05) grain N concentration in all Virginia testing sites. A spring split N application of 33 kg N ha⁻¹ at Zadoks growth stage (GS) 25 and 101 kg N ha⁻¹ at Zadoks GS 30 produced the highest grain yields and grain N contents in five of the six Virginia environments. Nitrogen utilization efficiency was the primary contributor to variation in N use efficiency under higher N rates, whereas N uptake efficiency was the greatest contributor under low N rates. The contribution of N utilization and uptake efficiencies varied among wheat lines in the Ppd-D1a group when 67 kg N ha⁻¹ was applied at Zadoks GS 25 under the moderate and standard N rates.

Disease resistance is critical in soft red winter wheat (Triticum aestivum L.) cultivars. Leaf rust caused by Puccinia triticina Eriks and stripe rust caused by Puccinia striiformis Westend. f. sp. tritici Eriks. are destructive pathogens of wheat. Phenotypic data were collected at diverse locations for resistance to leaf rust (North Carolina, Texas, and Virginia) and stripe rust (Arkansas, North Carolina, Georgia, Texas, and Virginia) in a Pioneer '25R47' /'Jamestown' (P47/JT) population composed of 186 FDX5:9 recombinant inbred lines (RILs). The P47/JT RILs were geno-typed with a public 90K iSelect single-nucleotide polymorphism array. Analysis of the P47/ JT population identified two quantitative trait loci (QTL) for leaf rust resistance on chromosome 5B and two QTL for stripe rust resistance on chromosomes 3B and 6A. These QTL were associated with both infection type and disease severity. Phenotypic variation (%) explained by the putative leaf rust resistance QTL of Jamestown on 5B was as high as 22.1%. Variation explained by the putative stripe rust resistance QTL of Jamestown on 3B and 6A was as high as 11.1 and 14.3%, respectively. Introgression and pyramiding of these QTL with other genes conferring resistance to leaf and stripe rusts via marker-assisted selection will facilitate development of soft red winter wheat cultivars having more durable resistance. Virginia Small Grains Board; Virginia Agricultural Council; USDAUnited States Department of Agriculture (USDA) [58-6645-4-032] This study was supported with funding provided by the Virginia Small Grains Board and the Virginia Agricultural Council. This material is based on work supported by the USDA, under Agreement no. 58-6645-4-032. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the view of the USDA. The authors would like to thank the International Wheat Genome Sequencing Consortium for providing prepublication access to the IWGSC RefSeq v1.0. Public domain – authored by a U.S. government employee