Precision farming : an economic and environmental analysis of within-field variability

This simulation study was conducted to investigate the role of within-field variability in realizing economic and environmental benefits from precision farming. The objectives of the study were to (i) illustrate analytically the influence of within-field variability on the economic outcomes of a given sampling intensity and therefore, the choice of the most economical sampling scheme, (ii) develop a method to determine the minimum spatial variability (distribution of land within a field with different production capabilities) needed so the additional returns from precision farming would at least cover the costs of using the technology, (iii) illustrate the role of weather expectations in precision farming, (iv) test the hypothesis that precision farming holds the promise of environmental benefits, and (v) examine policy options to motivate farmers to adopt precision farming, if the new technology is found to reduce environmental degradation. The objectives were accomplished assuming that the farmers\u27 main objective was profit maximization and that the technology was adopted by custom hiring the necessary services from the farm service sector. The study created four hypothetical com fields with different degrees of within-field variability on which nitrogen (N) was applied at variable rates based on soil sample tests. The results suggested, for each sampling intensity considered, that the more variability, the higher the returns above N costs with Variable Rate Technology (VRT) than with Uniform Rate Technology (URT). Further, it was indicated by the results that higher sampling intensity was economically optimal for the fields with higher variability, over a range of sampling costs considered. Precision farming need not necessarily imply grid sampling. The technology could be used to apply inputs at spatially variable rates on different land types (classified, for example, according to soil series, slopes, landscape positions, etc.) with their oAvn yield responses to applied inputs. Under such circumstances, economic feasibility of adopting VRT depends upon the existing land mix on the field. Given input and product prices, custom charges, and knowledge of yield response to applied inputs on two or more land types, the study developed a method to identify the required land proportions so the additional returns from VRT could at least cover custom charges. These proportions were referred to as spatial break-even variability proportions. It is not just economic benefits that are claimed of precision farming. The new technology is also expected to benefit the environment by matching input application to plant and soil needs. The study investigated the potential of precision farming to reduce N loading into the environment. The Environmental Policy Integrated Climate (EPIC) crop growth model was used to estimate com yield responses to applied N and predict total N losses on different soils under different rainfall scenarios. The results indicated potential of the new technology to help reduce environmental degradation. The analysis suggested increasing importance of well-informed and accurate weather expectations under precision farming. In the majority of cases examined, farmers\u27 decisions to adopt VRT meant economic losses when their rainfall expectations went wrong. Given the evidence of environmental benefits from being precise in input application, the study analyzed policy options to motivate farmers to adopt VRT. Subsidizing custom charges and restricting N use were the two options analyzed and found to help reduce N loss. The results showed totally different effects on production and farm incomes of input use restriction with and without VRT. With farmers having access to VRT, the fall in returns due to N restriction was much less than the fall that would have occurred with the same N use restriction without precision technology. Interestingly, when N use was restricted and farmers were forced to adopt VRT, production actually increased compared to the amount produced with URT under conditions of unconstrained N supply. To sum up the findings of this study, the economic benefits from grid sampling depend upon the extent of variability; highly intensive sampling is beneficial for the fields with high variability. Farmers often have a broad idea of variability across the field based on characteristics like soil series, slope, soil depth and yield variability shown by yield monitors. Planned sampling needs to be guided by such prior experience. The land mix on the field impacts the economic outcome of VRT. The method developed here helps find the minimum spatial variability needed on fields with two or more land types so the farmer can at least offset the custom charges with VRT adoption. The method is flexible and incorporates changing input and product prices as well as custom charges. VRT holds environmental promise. However, a farmer\u27s motive to adopt the technology is purely economic. As such, efforts are needed to make the technology attractive to farmer. Where the technology proves beneficial for the environment, government can subsidize custom charges to promote VRT adoption. Restricting input use could also promote technology adoption without much adverse effect on income and production. Farmers need to be more informed in formulating weather expectations under precision farming; the adverse effects on their economic interests due to wrong expectations can be more severe with VRT than with URT

Epidemic spreading on preferred degree adaptive networks

We study the standard SIS model of epidemic spreading on networks where individuals have a fluctuating number of connections around a preferred degree $\kappa$. Using very simple rules for forming such preferred degree networks, we find some unusual statistical properties not found in familiar Erd\H{o}s-R\'{e}nyi or scale free networks. By letting $\kappa$ depend on the fraction of infected individuals, we model the behavioral changes in response to how the extent of the epidemic is perceived. In our models, the behavioral adaptations can be either `blind' or `selective' -- depending on whether a node adapts by cutting or adding links to randomly chosen partners or selectively, based on the state of the partner. For a frozen preferred network, we find that the infection threshold follows the heterogeneous mean field result $\lambda_{c}/\mu =/$ and the phase diagram matches the predictions of the annealed adjacency matrix (AAM) approach. With `blind' adaptations, although the epidemic threshold remains unchanged, the infection level is substantially affected, depending on the details of the adaptation. The `selective' adaptive SIS models are most interesting. Both the threshold and the level of infection changes, controlled not only by how the adaptations are implemented but also how often the nodes cut/add links (compared to the time scales of the epidemic spreading). A simple mean field theory is presented for the selective adaptations which capture the qualitative and some of the quantitative features of the infection phase diagram.Comment: 21 pages, 7 figure

CHIKUNGUNYA DRUG TARGET DATABASE: A COMPREHENSIVE DATABASE OF CHIKUNGUNYA DRUG TARGETS

ABSTRACTObjective: Chikungunya is a viral infection transmitted by the Aedes mosquito to humans. Currently, there is no approved treatment for the infection.Clinical trials have been carried out to identify candidate drug molecules. There is no existing repository for determining the information pertainingto drug targets of chikungunya. The current study aims to develop a comprehensive database of chikungunya drug targets.Methods: Literature review is carried out to determine the available drug targets for chikungunya with the help of several open source biologicalrepositories. The database consists of 23 viral strains, 19 drugs targets, and 7 drugs. Further manual annotation is performed to identify the relevantdrug targets. For each of these drug targets identified, an illustrative pathway is constructed depicting the role of drug targets in the viral infection.Results: The chikungunya drug target database (CDTD) was constructed based on the three-tier architecture in XAMPP (Windows Apache MySQL PerlPHP) platform. The database comprised 23 viral isolates, 19 drug targets, and 7 currently used drugs specific to chikungunya infection. For each of thedrug targets, a mechanistic pathway is designed to illustrate the role of drug targets and the mechanical pathway in chikungunya infection.Conclusion: CDTD is a specialized database which is designed to provide comprehensive information pertaining to viral drug targets. The databasecan be utilized from researchers interested in the virology of chikungunya further providing drug target information.Availability: The database is freely available at http://www.biocdtd.org/.Keywords: Chikungunya virus, Drug targets, Viral isolates, Drugs, Pathways

Early Disease Detection Through Nail Image Processing Based on Ensemble of Classifier Models

Medical science has progressed in many ways and different methods have been developed for the diagnosis of diseases in the human body and one of the ways to identify the diseases is through the close examination of nails of the human palm. The main aim of this study is to compare the performance of various classifier models that are used for the prediction of various diseases. The Performance analysis is done by applying image processing, different data mining and machine learning techniques to the extracted nail image through our proposed system which does nail analysis using a combination of 13 features (Nail Color, Shape and Texture) extracted from the nail image. In this paper we have compared different machine learning classifiers like Support Vector Machine, Multiclass SVM and K-Nearest Neighbor through ensemble of these classifiers with different features so as to classify patients with different diseases like Psoriasis, Red Lunula, Beau�s Lines, Clubbing, etc. These approaches were tested with data images from Hospitals and workplaces. The performance of the different classifiers have been measured in terms of Accuracy, Sensitivity and Specificity

Effect of fungal biosorbed and nonbiosorbed copper and zinc metal solutions on growth and metal uptake of leguminous plants

Effect of Zn, Cu and Cu + Zn at 10, 25, 50, 100, 200 and 500ppm concentrations of fungal untreated and treated metal solutions on seed germination and seedling vigor of Cicer areietinum (Chick pea), Macrotyloma uniflorum (Horse gram), Vigna radiata (Green gram) and Vigna unguiculata (Cowpea) were evaluated. Heavy metal solutions were prepared in increasing concentration up to the concentration critical to the soil. Increased metal concentrations reduced the seed germination and growth of test plants. Low metal concentrations of 10, 25, 50 ppm, stimulated the shoot, root and seed germination in test plants. Untreated and treated effluent was not acutely toxic to the seed germination and plant growth. In Aspergillus niger and Aspergillus flavus biosorbed metal ions, reduced metal toxicity with increased seedling vigor was observed. Efficiency of metal biosorption by fungal biomass and metal ions tolerance and accumulation ability in test plants were analyzed by Atomic Absorption Spectrophotometer (AAS).  Â

Impact of Guinea Grass, Congo Signal and \u3cem\u3eStylosanthes hamata\u3c/em\u3e on Soil Physico-Chemical Properties and Beneficial Micro Fauna in Mango and Sapota Plantations

Farming systems are considered to be most important upcoming enterprises to reduce soil erosion and land degradation besides provide timber, fruits, nutritious fodder for live stock population in the poor soil areas (Roy et.al., 2000). Horticulture based farming systems have been recommended as alternate land use systems for sustainable agriculture in semi arid ecosystem for efficient soil plant management and soil fertility management. Studies on micro flora and micro fauna under farming system are required to increase the farming system productivity. Higher species diversity of soil arthropods was observed in grassland system closely followed by the silvipasture systems when compared to areas having no vegetation in central India. Abundance, diversity and species richness decreased along the gradient, with the agricultural site presenting an impoverished community. Diversity descriptors were positively and significantly correlated with habitat diversity, measured on the basis of the proportion of the different soil-use types present at each land-use unit (Sousa et.al., 2004). The influence of three spatially hierarchical factors like local depth of the soil, ground cover type on the soil samples (bare ground, grass tufts, dead trees lying on the ground), dimensions of the grass tufts sampled (size and shape), significantly affected the morphospecies richness and/or density of the soil macrofauna. The type of ground cover had the strongest influence, affecting the total richness and density of the soil macro fauna and of almost all the groups represented. In the present study efforts were made to know the impact of guinea grass Panicum maximum, Congo signal grass Brachiaria ruziziensis and a legume Stylosanthes hamata on thephysico-chemical properties of mango and sapota based hortipasture systems

A Hybrid Bacterial Swarming Methodology for Job Shop Scheduling Environment

Optimized utilization of resources is the need of the hour in any manufacturing system. A properly planned schedule is often required to facilitate optimization. This makes scheduling a significant phase in any manufacturing scenario. The Job Shop Scheduling Problem is an operation sequencing problem on multiple machines with some operation and machine precedence constraints, aimed to find the best sequence of operations on each machine in order to optimize a set of objectives. Bacterial Foraging algorithm is a relatively new biologically inspired optimization technique proposed based on the foraging behaviour of E.coli bacteria. Harmony Search is a phenomenon mimicking algorithm devised by the improvisation process of musicians. In this research paper, Harmony Search is hybridized with bacterial foraging to improve its scheduling strategies. A proposed Harmony Bacterial Swarming Algorithm is developed and tested with benchmark Job Shop instances. Computational results have clearly shown the competence of our method in obtaining the best schedule

Farmer to Farmer Spread of Fodder Crops--An Analysis on Mango Orchards in South India

India ranks first among world mango (Mangifera indica L.) producing countries, accounting for about 50% of world production. Karnataka in southern India is one of the important mango producing provinces of the country. The total area under mango crops in Karnataka is 160,000 ha of which 90,000 ha is in prime fruit yielding stage. In the year 2011-12 alone the area under mango crops has gone up by 10,000 ha (DoH, 2013). The usual planting distance followed by most farmers for mango is 10 m by 10 m. Intercropping, mainly with small millet is practiced until the mango trees attain a suitable height and develop canopy (at 5-6 years of age). The space between the mango tree rows which is not cultivated is estimated to be 67,500 ha (75%) and this provides ample scope for introduction of improved fodder crops in mango orchards through non-competitive land use. Formal methods of diffusion of fodder technologies in India are not only few but are also inefficient. In such cases diffusion can be enhanced through participation of farmers (Kormawa et al., 2004). However farmer to farmer dissemination of technologies is a neglected area of research (Grisley, 1994). A study on diffusing fodder technologies in interspaces of mango orchards of farmers in a participatory mode was conducted in Karnataka. One objective was to develop a method to improve the fodder availability using mango orchards by encouraging farmers to be partners for better feeding of livestock in the region