Pre-harvest forecast of kharif rice yield using PCA and MLR technique in Navsari district of Gujarat

In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model for rice yield in the Navsari district of south Gujarat. The weather indices were developed and utilized for development of pre-harvest forecast models. The data of rice yield and weather parameters from 1990 to 2012 were utilized. The cross validation of the developed forecast model were confirmed using data of the years 2013 to 2016. It was observed that value of Adj. R2 varied from 89 to 96. The appropriate forecast model was selected based on high value of Adj. R2. Based on the outcomes in Navsari district, MLR techniques found to be better than PCA for pre harvest forecasting of rice crop yield. The Model-2 found competent to forecast rice yield in Navsari district before eight weeks of actual harvest of crop (37th SMW) i.e during reproductive stage of the crop growth period.

Population Studies of Deer (Odocoileus virginianus, Mazama americana) in Southern Yucatán, Mexico

Aims: Estimate the population density of deer in the municipality of Tzucacab, Yucatán in the periods of 2003-2004, 2007-2008 and 2008-2009, determine the use of the habitat by these populations and the sustainability of the deer harvest from the estimated population densities. Study Design: A descriptive and vertical free-living deer population study was carried out in southern Yucatan, Mexico over a three-year period. Methodology: The map of the municipality of Tuzcacab was zoned in quadrants of 36 km2, completing a total of 36 quadrants; Unrestricted random sampling was applied to select seven quadrants in the period from 2003 to 2004 and 18 in each annual period between 2007 and 2009. Population samplings were carried out by applying three population estimation methods: direct sighting in a linear transect of 5 km in length, count of tracks in transect except period 2003-2004 and faecal pellets group count in plots. The evaluation of the use of habitat was carried out using the Bonferroni intervals, from the data of faecal pellets count. The evaluation of the deer harvest was carried out using the sustainable harvest model. Results: The population densities were different in each method, the density by the excreta count was 4.63 ± 2.49 deer / km2 in 2003-2004, 0.294 ± 0.198 deer / km2 in 2007-2008, and in the year 2008-2009 was 0.419 ± 0.0000085 deer / km2. Habitat use in 2007-08 and 2008-2009 was higher in the tropical forest, lower in agriculture and similar to that expected in secondary succession forest (acahual). The values of sustainable harvest, taking as a value the density per count of excreta in the plot because it showed the highest statistical precision, in the period 2003-04 it is sustainable, but in the period from 2007 to 2009 it is not sustainable. Conclusion: The population densities of deer (O. virginianus and M. americana) in Tuzcacab by means of the excreta count method, have decreased significantly. The habitat use preference is the tropical forest. The deer harvest in the period from 2007 to 2009 is not sustainable.

A logistic-harvest model with allee effect under multiplicative noise

This work is devoted to the study of a stochastic logistic growth model with and without the Allee effect. Such a model describes the evolution of a population under environmental stochastic fluctuations and is in the form of a stochastic differential equation driven by multiplicative Gaussian noise. With the help of the associated Fokker–Planck equation, we analyze the population extinction probability and the probability of reaching a large population size before reaching a small one. We further study the impact of the harvest rate, noise intensity and the Allee effect on population evolution. The analysis and numerical experiments show that if the noise intensity and harvest rate are small, the population grows exponentially, and upon reaching the carrying capacity, the population size fluctuates around it. In the stochastic logistic-harvest model without the Allee effect, when noise intensity becomes small (or goes to zero), the stationary probability density becomes more acute and its maximum point approaches one. However, for large noise intensity and harvest rate, the population size fluctuates wildly and does not grow exponentially to the carrying capacity. So as far as biological meanings are concerned, we must catch at small values of noise intensity and harvest rate. Finally, we discuss the biological implications of our results.

Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms

The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines exposed to different levels of smoke: (i) Control (C), i.e., no misting or smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting, but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2, and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R2 = 0.97; b = 1) or at harvest (Model 2: R = 0.98; R2 = 0.97; b = 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R2 = 0.95; b = 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R = 0.99; R2 = 0.99; b = 1.00), while Model 5 used wine NIR absorbance spectra (R = 0.99; R2 = 0.97; b = 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions about grape harvest and smoke taint mitigation techniques in the winemaking process.

Conservation of forest based on a fuelwood substitute as well as considering the cultural and spiritual values: an optimal fuelwood harvest model

Excessive fuelwood harvest is a major cause of deforestation in the developing countries. To mitigate this, various preventive measures have been introduced in different countries. Availability of affordable substitute to the community dependent on the forest for domestic energy consumption may prevent further forest degradation. A stock dependent optimal control model of fuelwood harvest from a natural forest is presented here and comparative statics has been used to show that the presence of a fuelwood substitute will reduce its harvest and increase the forest stock. The model indicates that availability of cheaper and high energy content alternative for fuelwood can substantially reduce fuelwood extraction from a forest. Also, a lower discount rate and higher cultural and spiritual values (CSV) will keep the optimal forest stock close to its carrying capacity and reduce fuelwood harvest. The model reveals that the maximum sustainable yield of forest stock and the ratio of energy content per unit mass of fuel play a central role in the fate of forest stock and level of fuelwood harvest. Empirical example of the Southeast Asian forest growth model along with Liquid Petroleum Gas (LPG) as substitute has been used to illustrate the results. The outcomes of this study can incorporated into forest conservation polices.

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.

Simulation of the Forage Harvest Cycle for Asset Allocation

Harvesting corn () for silage requires the coordination of multiple pieces of equipment to ensure rapid and economical production of silage. A model of corn harvest for silage production, capable of predicting machine work states and total harvest time for an entire field, using a single harvester, and any number of specific transport vehicles, as a function of machine specifications and field properties was developed. Three forage harvesting systems were observed using Global Positioning System (GPS) and the collected data used for model validation. The harvest model predicted harvest times within 10% of observed data and yielded similar results to a previously published cycle analysis. Model scenarios were used to explore the effect of differently sized transport vehicles on harvest time and it was found that placing transport vehicles with longer cycle times at the end of the rotation has the potential to reduce harvest time. This model can be used to determine the optimal number of transport vehicles and their dispatch order to reduce total harvest time. Keywords: Cycle analysis, Forage harvesting, Harvesting, Machinery selection, Silage.