In silico design of crop ideotypes under a wide range of water availability

Given the changing climate and increasing impact of agriculture on global resources, it is important to identify phenotypes which are global and sustainable optima. Here, an in silico framework is constructed by coupling evolutionary optimization with thermodynamically sound crop physiology, and its ability to rationally design phenotypes with maximum productivity is demonstrated, within well‐defined limits on water availability. Results reveal that in mesic environments, such as the North American Midwest, and semi‐arid environments, such as Colorado, phenotypes optimized for maximum productivity and survival under drought are similar to those with maximum productivity under irrigated conditions. In hot and dry environments like California, phenotypes adapted to drought produce 40% lower yields when irrigated compared to those optimized for irrigation. In all three representative environments, the trade‐off between productivity under drought versus that under irrigation was shallow, justifying a successful strategy of breeding crops combining best productivity under irrigation and close to best productivity under drought

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use

Shared genetic control of root system architecture between Zea mays and Sorghum bicolor

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed CREAMD (Core Root Excavation using Compressed-air), a high-throughput pipeline for the cleaning of field-grown roots, and COFE (Core Root Feature Extraction), a semi-automated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from \u3e3,300 maize roots and \u3e1,470 sorghum roots. SNP-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via eRD-GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are co-regulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves trans-eQTL for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa

MODELING AND SIMULATION OF ELECTROKINETIC MANIPULATION OF BIOLOGICAL PARTICLES

Recent experimental studies show that electrophoretic and dielectrophoretic these two electrokinetic forces can manipulate biological particles efficiently in micro/nanofluidic devices. Electrokinetic forces in those devices depend on various parameters related to properties of the particle, surrounding fluid and device. Therefore, to design an effective micro/nanofluidic platform for a specific manipulation, it is necessary to analyze the effect of the above parameters using mathematical models and numerical simulations. To reduce computational cost, a point based method or smaller domain or single particle is used in the conventional modeling and simulation. However, these methods produce erroneous results when particle and device sizes are comparable, and often ignore detail physics in the devices.To overcome the limitations of existing methods, two mathematical models and numerical algorithms have been developed considering presence of multiple particles in an actual device. The first model is based on distributed Lagrange multiplier based fictitious domain approach for flow field and motion of particles, and on a multi-domain method for electric potential. This model is suitable for those electrokinetic manipulation devices where dielectrophoretic force dominates. Dielectrophoretic force in this model is calculated using Maxwell stress tensor. The capability of the proposed model is demonstrated by simulating trajectories of two biological particles of different electrical properties. Next, based on this model a new microfluidic device was designed to improve the efficiency of continuous separation of particles. A hybrid and periodic truncated trapezoidal electrodes configuration has been suggested in that device to increase inter particle distance while applying dielectrophoretic force in two directions. The second mathematical model is developed based on Poisson-Nernst-Planck equations along with Navier-Stokes equations for fluid flow and on the Langevin equation for particle translocation. This model is suitable for electrophoretic manipulation of nanoparticles. Separation of nano-bioparticles through solid state nanopore has been studied using this model. Our numerical study suggests that membrane pore surface charge density is a more important parameter than pore diameter and length for particle separation through a nanopore

A farm-level precision land management framework based on integer programming.

Farmland management involves several planning and decision making tasks including seed selection and irrigation management. A farm-level precision farmland management model based on mixed integer linear programming is proposed in this study. Optimal decisions are designed for pre-season planning of crops and irrigation water allocation. The model captures the effect of size and shape of decision scale as well as special irrigation patterns. The authors illustrate the model with a case study on a farm in the state of California in the U.S. and show the model can capture the impact of precision farm management on profitability. The results show that threefold increase of annual net profit for farmers could be achieved by carefully choosing irrigation and seed selection. Although farmers could increase profits by applying precision management to seed or irrigation alone, profit increase is more significant if farmers apply precision management on seed and irrigation simultaneously. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture