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

Utilization of Reduced Haploid Vigor for Phenomic Discrimination of Haploid and Diploid Maize Seedlings

Potential benefits of incorporating embryo culture (EC) into a doubled haploid (DH) program, including shortening the breeding cycle and increasing chromosome doubling rates, make the laborious and tedious task of excising embryos worth the effort. Difficulties arise during embryo selection considering the marker gene , which is typically used in DH programs, is not expressed in early stages after pollination. Although transgenic approaches have been implemented to bypass this issue, there is so far no known non-transgenic method of selecting haploid embryos. The findings of this study reveal methods of selecting haploid embryos that allow the possibility of incorporating EC into a DH program without using transgenic inducers. The best performing method involves a machine-learning classifier, specifically a support vector machine, which uses primary root lengths and daily growth rates as traits for classification. Selection by this method can be achieved on the third day after germination. By this method, an average false negative rate of 2% and false positive rate of 9% was achieved. Therefore, the methods presented in this research allow efficient and non-transgenic selection of haploid embryos that is simple and effective

Irrigation decisions for basic model.

<p>Irrigation decisions for basic model.</p

Effect of decision unit.

<p>Effect of decision unit.</p

Schematic map (upper) and integrated map (lower) for soil types.

<p>Schematic map (upper) and integrated map (lower) for soil types.</p

Contour plot (upper) and surface plot (lower) for profit region.

<p>Region A is the non-profitable region and Region B is the profitable region, the darkness indicates the profit level.</p

Summary of regression analysis.

<p>Summary of regression analysis.</p

Notations for proposed model.

<p>Notations for proposed model.</p

Sensitivity analysis of model parameters on annual net profit.

<p>Sensitivity analysis of model parameters on annual net profit.</p

Effects of special irrigation patterns.

<p>Effects of special irrigation patterns.</p