Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

A Lot Aggregation Optimization Model for Minimizing Food Traceability Effort

This paper proposes a lot aggregation optimization model for minimizing the traceability effort at a grain elevator. The problem involves blending of bulk grain to meet customer specifications. A mathematical multi-objective mixed integer programming (MIP) model is proposed with two objective functions. The objective functions allow in calculating the minimum levels of lot aggregation and minimum discounts that need to be applied to a shipment when the customer contract specifications are not met. Constraints on the system include customer contract specifications, availability of grain at the elevator and the blending requirements. The solutions include the quantities of grain lots from different bins to be used for blending for a shipment while using the minimum number of storage bins and the total discounts to be applied. The numerical results are presented for two shipment scenarios to demonstrate the application of this model to bulk grain blending. The Pareto optimal solutions were calculated that represent the different optimal solutions for the blending problem. This provides the elevator management with a set of blending options. This model provides an effective method for minimizing the traceability effort by minimizing the food safety risk. Besides minimizing the lot aggregation, this model also allows in using the maximum volume of grain present in a given bin which leads to emptying of the storage bins and the extent of aggregation of old grain lots with the new incoming lots can decrease considerably. Use of fewer bins for blending shipments is also easier logistically and can lead to additional savings in terms of grain handling cost and time

BSG alignment of SDSS galaxy groups

We study the alignment signal between the distribution of brightest satellite galaxies (BSGs) and the major axis of their host groups using SDSS group catalog constructed by Yang et al. (2007). After correcting for the effect of group ellipticity, a statistically significant (~ 5\sigma) major-axis alignment is detected and the alignment angle is found to be 43.0 \pm 0.4 degrees. More massive and richer groups show stronger BSG alignment. The BSG alignment around blue BCGs is slightly stronger than that around red BCGs. And red BSGs have much stronger major-axis alignment than blue BSGs. Unlike BSGs, other satellites do not show very significant alignment with group major axis. We further explore the BSG alignment in semi-analytic model (SAM) constructed by Guo et al. (2011). We found general good agreement with observations: BSGs in SAM show strong major-axis alignment which depends on group mass and richness in the same way as observations; and none of other satellites exhibit prominent alignment. However, discrepancy also exists in that the SAM shows opposite BSG color dependence, which is most probably induced by the missing of large scale environment ingredient in SAM. The combination of two popular scenarios can explain the detected BSG alignment. The first one: satellites merged into the group preferentially along the surrounding filaments, which is strongly aligned with the major axis of the group. The second one: BSGs enter their host group more recently than other satellites, then will preserve more information about the assembling history and so the major-axis alignment. In SAM, we found positive evidence for the second scenario by the fact that BSGs merged into groups statistically more recently than other satellites. On the other hand, although is opposite in SAM, the BSG color dependence in observation might indicate the first scenario as well.Comment: 8 pages, 11 figures, ApJ accepte

An Optimization Approach To Assessing the Self-Sustainability Potential of Food Demand in the Midwestern United States

Conventional agriculture faces significant challenges as world population grows, food demand increases, and mobility becomes increasingly constrained. Reducing the distance food needs to travel is an important goal of sustainability and resiliency, particularly in the context of a variety of transportation challenges. In this study, we developed a linear programming optimization method to assess the potential of regions to meet dietary requirements with more localized and diversified agricultural systems. Emphasis is on minimizing the distance between population centers and available cropland, accounting for variations in yield among 40 of the most marketable food crops that can be grown in the Midwestern United States. We also derived two new metrics to guide strategic planning toward more localized systems: the per capita cropland requirement and the regional self-sustainability index. Overall, we conclude that the eight-state study region would require an average of 0.49 acres (0.2 ha) per consumer with an average absolute deviation of 0.09 acres (.04 ha). The self-sustainability index is estimated at 9.3, which indicates that the region has 9.3 times the cropland needed to become self-sustaining. Targeted dietary recommendations could potentially be met within a population-weighted average distance of 13.6 miles (21.9 km)

Effect of various dietary fats on fatty acid profile in duck liver: Efficient conversion of short-chain to long-chain omega-3 fatty acids

Citation: Chen, X., Du, X., Shen, J., Lu, L., & Wang, W. (2016). Effect of various dietary fats on fatty acid profile in duck liver: Efficient conversion of short-chain to long-chain omega-3 fatty acids. Experimental Biology and Medicine, 1535370216664031. https://doi.org/10.1177/1535370216664031Omega-3 fatty acids, especially long-chain omega-3 fatty acids, have been associated with potential health benefits for chronic disease prevention. Our previous studies found that dietary omega-3 fatty acids could accumulate in the meat and eggs in a duck model. This study was to reveal the effects of various dietary fats on fatty acid profile and conversion of omega-3 fatty acids in duck liver. Female Shan Partridge Ducks were randomly assigned to five dietary treatments, each consisting of 6 replicates of 30 birds. The experimental diets substituted the basal diet by 2% of flaxseed oil, rapeseed oil, beef tallow, or fish oil, respectively. In addition, a dose response study was further conducted for flaxseed and fish oil diets at 0.5%, 1%, and 2%, respectively. At the end of the five-week treatment, fatty acids were extracted from the liver samples and analyzed by GC-FID. As expected, the total omega-3 fatty acids and the ratio of total omega-3/omega-6 significantly increased in both flaxseed and fish oil groups when compared with the control diet. No significant change of total saturated fatty acids or omega-3 fatty acids was found in both rapeseed and beef tallow groups. The dose response study further indicated that 59Ð81% of the short-chain omega-3 ALA in flaxseed oil-fed group was efficiently converted to long-chain DHA in the duck liver, whereas 1% of dietary flaxseed oil could produce an equivalent level of DHA as 0.5% of dietary fish oil. The more omega-3 fatty acids, the less omega-6 fatty acids in the duck liver. Taken together, this study showed the fatty acid profiling in the duck liver after various dietary fat consumption, provided insight into a dose response change of omega-3 fatty acids, indicated an efficient conversion of short- to long-chain omega-3 fatty acid, and suggested alternative long-chain omega-3 fatty acid-enriched duck products for human health benefits

The Watermelon Algorithm for The Bilevel Integer Linear Programming Problem

This paper presents an exact algorithm for the bilevel integer linear programming (BILP) problem. The proposed algorithm, which we call the watermelon algorithm, uses a multiway disjunction cut to remove bilevel infeasible solutions from the search space, which was motivated by how watermelon seeds can be carved out by a scoop. Serving as the scoop, a polyhedron is designed to enclose as many bilevel infeasible solutions as possible, and then the complement of this polyhedron is applied to the search space as a multiway disjunction cut in a branch-and-bound framework. We have proved that the watermelon algorithm is able to solve all BILP instances finitely and correctly, providing either a global optimal solution or a certificate of infeasibility or unboundedness. Computational experiment results on two sets of small- to medium-sized instances suggest that the watermelon algorithm could be significantly more efficient than previous branch-and-bound based BILP algorithms

Three Essays on Decision Making under Uncertainty in Electric Power Systems

This thesis consists of three essays, discussing three different but connected problems on decision making under uncertainty in electricpower systems.The first essay uses a system model to examine how various factors affect the market price of electricity, and decomposes the price toquantitatively evaluate the contributions of individual factors as well as their interactions. Sensitivity analysis results from a parametric quadratic program are applied in the computation.The second essay formulates the well studied security constrained economic dispatch (SCED) problem as a Markov decision process model,where the action space is a polyhedron defined by linear generation and transmission constraints. Such a model enables the decision maker to accurately evaluate the impact of a dispatch decision to the entire future operation of the electric power system.The third essay examines the effect of demand and supply side uncertainties on the exercise of market power. Solutions under Bertrand, Cournot, and linear supply function equilibrium (LSFE)models are derived and compared.The three problems studied in the essays are a unique representation of different levels of the decision making process in a sophisticated deregulated electric power system, using techniques from both mathematical programming and probability/statistics

A cadherin-like protein influences Bacillus thuringiensis Cry1Ab toxicity in the oriental armyworm, Mythimna separata

Cadherins comprise a family of calcium-dependent cell adhesion proteins that act in cell–cell interactions. Cadherin-like proteins (CADs) in midguts of some insects act as receptors that bind some of the toxins produced by the Bacillus thuringiensis (Bt). We cloned a CAD gene associated with larval midguts prepared from Mythimna separata. The full-length cDNA (MsCAD1, GenBank Accession No. JF951432) is 5642 bp, with an open reading frame encoding a 1757 amino acid and characteristics typical of insect CADs. Expression of MsCAD1 is predominantly in midgut tissue, with highest expression in the 3rd- to 6th-instars and lowest in newly hatched larvae. Knocking-downMsCAD1 decreased Cry1Ab susceptibility, indicated by reduced developmental time, increased larval weight and reduced larval mortality. We expressed MsCAD1 in E. coli and recovered the recombinant protein, rMsCAD1, which binds Cry1Ab toxin. Truncation analysis and binding experiments revealed that a contiguous 209-aa, located in CR11 and CR12, is the minimal Cry1Ab binding region. These results demonstrate that MsCAD1 is associated with Cry1Ab toxicity and is one of the Cry1Ab receptors in this insect. The significance of this work lies in identifying MsCAD1 as a Cry1Ab receptor, which helps understand the mechanism of Cry1Ab toxicity and of potential resistance to Bt in M. separata