Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled “Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Spatio-temporal risk assessment models for Lobesia botrana in uncolonized winegrowing areas

The objective of this work was to generate a series of equations to describe the voltinism of Lobesia botrana in the quarantine area of the main winemaking area of Argentina, Mendoza. To do this we considered an average climate scenario and extrapolatedthese equations to other winegrowing areas at risk of being invaded. A grid of 4 km2was used to generate statistics on L. botrana captures and the mean temperature accumulation for the pixel. Four sets of logistic regression were constructed using the percentage of accumulated trap catches/grid/week and the degree-day accumulation above7°C, from 1st July. By means of a habitat model, an extrapolation of the phenologicalmodel generated to other Argentine winemaking areas was evaluated. According to ourresults, it can be expected that 50% of male adult emergence for the first flight occurs at248.79 ± 4 degree-days (DD), in the second flight at 860.18 ± 4.1 DD, while in the thirdand the fourth flights, 1671.34 ± 5.8 DD and 2335.64 ± 4.3 DD, respectively. Subsequentclimatic comparison determined that climatic conditions of uncolonized areas of Cuyo Region have a similar suitability index to the quarantine area used to adjust the phenologicalmodel. The upper valley of Río Negro and Neuquén are environmentally similar. Valleys ofthe northwestern region of Argentina showed lower average suitability index and greatervariability among SI estimated by the algorithm considered. The combination of two models for the estimation of adult emergence time and potential distribution, can provide greater certainties in decision-making and risk assessment of invasive species.Fil: Heit, Guillermo Eugenio. Ministerio de Agricultura, Ganadería, Pesca y Alimento. Servicio Nacional de Sanidad y Calidad Agroalimentaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; ArgentinaFil: Aceñolaza, Pablo Gilberto. Universidad Nacional de Entre Ríos; Argentina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentin

Signatures of Environmental Adaptation During Range Expansion of Wild Common Bean (Phaseolus vulgaris)

Abstract Landscape genomics integrates population genetics with landscape ecology, allowing the identification of putative molecular determinants involved in environmental adaptation across the natural geographic and ecological range of populations. Wild Phaseolus vulgaris , the progenitor of common bean ( P. vulgaris ), has a remarkably extended distribution over 10,000 km from northern Mexico to northwestern Argentina. Earlier research has shown that this distribution represents a range expansion from Mesoamerica to the southern Andes through several discrete migration events and that the species colonized areas with different temperature and rainfall compared to its core area of origin. Thus, this species provides an opportunity to examine to what extent adaptation of a species can be broadened or, conversely, ecological or geographical distribution can be limited by inherent adaptedness. In the current study, we applied a landscape genomics approach to a collection of 246 wild common bean accessions representative of its broad geographical and climatic distribution and genotyped for ∼20K SNPs. We applied two different but complementary approaches for identifying loci putatively involved in environmental adaptation: i) an outlier-detection method that identifies loci showing strong differentiation between sub-populations; ii) an association method based on the identification of loci associated with bio-climatic variables. This integrated approach allowed the identification of several genes showing signature of selection across the different natural sub-populations of this species, as well as genes associated with specific bio-climatic variables related to temperature and precipitation. The current study demonstrates the feasibility of landscape genomics approach for a preliminary identification of specific populations and novel candidate genes involved in environmental adaptation in P. vulgaris . As a resource for broadening the genetic diversity of the domesticated gene pool of this species, the genes identified constitute potential molecular markers and introgression targets for the breeding improvement of domesticated common bean. Author Summary The ancestral form of common bean has an unusually large distribution in the Americas, extending over 10,000 km from ∼35° N. Lat. to ∼35° S. Lat. This wide distribution results from discrete long-range dissemination events to the Andes region from the original environments in Mesoamerica. It also suggests adaptation to new environments that are distinct from those encountered in Mesoamerica. In this research, we identified genes that may be involved in adaptation to climate variables in these new environments using two methods. A first method – outlier detection – was used to identify genome regions that differentiated the wild bean groups in the Andes resulting from discrete dissemination events among themselves and the different groups in Mesoamerica. The second method – genome-wide association – was used to identify candidate genome regions correlated with these same variables across the entire distribution from Mesoamerica to the southern Andes. The two methods identified two sets of candidate genes, several of which were related to the water status of plants, and illustrate how the genetic architecture of adaptation following long-range dissemination. This study provides sets of candidate genes as well as candidate wild bean populations that need to be corroborated for their use in increasing the water use efficiency of domesticated beans

Prediction of gene–phenotype associations in humans, mice, and plants using phenologs

All authors are with the Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA. -- Ulf Martin Singh-Blom is with the Program in Computational and Applied Mathematics, The University of Texas at Austin, Austin, TX 78712, USA, and th Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Stockholm 171 76, Sweden. -- Kriston L. McGary is with the Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA.Background: Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such “orthologous phenotypes,” or “phenologs,” are examples of deep homology, and may be used to predict additional candidate disease genes. Results: In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data — from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans — establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene–phenotype associations, as for the Arabidopsis response to vernalization phenotype. Conclusions: We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.Center for Systems and Synthetic BiologyInstitute for Cellular and Molecular [email protected]

Técnicas de otimização na agricultura : o problema de rotação de culturas

Orientadores: Akebo Yamakami, Priscila Cristina Berbert RampazzoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Rotação de culturas é o futuro da agricultura sustentável. Diversidade na sequência de rotação melhora as propriedades físicas e químicas do solo sem demandar todas as exaustivas práticas convencionais de manejo do solo ou grandes quantidades de insumos agrícolas. Cultivar plantas de cobertura ao longo da rotação também desempenha um papel fundamental no controle de pestes e ervas daninhas, melhora a fertilidade do solo e reduz os processos erosivos. Embora esta pesquisa concentre-se na promoção de práticas agrícolas mais sustentáveis, as propriedades rurais precisam ser lucrativas e resilientes para prosperar num futuro incerto. Então, o planejamento das rotações de culturas precisa equilibrar os cenários econômicos potenciais e a conservação ambiental, sendo que as técnicas de otimização conseguem realizar este balanço naturalmente. Após considerar o fluxo de nutrientes nos campos cultiváveis e muitas vantagens do cultivo das plantas de rotação, foram propostos novos modelos para o Problema de Rotação de Culturas (PRC). A pesquisa prosseguiu com a avaliação das técnicas de otimização disponíveis para o PRC e com a proposta de novos métodos. Das abordagens clássicas, foram analizados métodos de otimização multiobjetivo, tais como o método da soma ponderada e as técnicas de escalarização. Em busca de métodos mais eficientes, os algoritmos evolutivos (AE), que são baseados na evolução biológica, tais como herança genética e mutação, são alternativas interessantes. Foram desenvolvidos algoritmos genéticos para otimização mono-objetivo e para otimização multi-objetivo. Após a realização de diversos testes utilizando dados reais do PRC, os resultados encontrados confirmam que os algoritmos propostos têm desempenho satisfatório. Esta pesquisa contribuiu para os campos da Agricultura, com os modelos propostos para o PRC, e da Otimização, com o desenvolvimento de algoritmos evolutivosAbstract: Crop rotation is the future of sustainable agriculture. Diversity in the cropping sequence can improve soil physical and chemical properties without demanding all the conventional tillage practices or large amounts of agricultural chemicals. Growing cover crops along the rotation also plays a fundamental role in controlling pests and weeds, improving soil fertility and reducing erosion. Although we have focused on bringing about more sustainable agrarian practices, farms ought to be profitable and resilient to thrive in an uncertain future. Therefore, planning crop rotations needs to balance the potential economic scenarios and the environmental conservation, which optimization techniques can manage this balance naturally. Our main effort in this research is to develop the crop rotation¿s concepts in the optimization perspective. After carefully considering the nutrient flow in agricultural fields and many advantages of seeding cover crops, we have proposed new models for the Crop Rotation Problem (CRP). Our research proceeds with evaluating optimization techniques for the CRP and proposing new alternatives. From classical methodologies, we have analyzed multiobjective optimization methods such as the weighted sum and the achievement scalarizing function technique. Looking for more efficient methods, evolutionary algorithms (EAs), which are based on biological evolution, such as genetic inheritance and mutation, are interesting alternatives. We have developed a mono-objective genetic algorithm and a multiobjective one. After running several tests using real data of the CRP, the achieved results confirm that the proposed algorithms have satisfactory performance. This research contributed to the fields of Agriculture, with the proposed models of CRP and Optimization, with the development of evolutionary algorithmsMestradoAutomaçãoMestre em Engenharia Elétrica88882.329362/2019-01CAPE