Determining day length and temperature regulation of flowering: a molecular and modelling approach

Thesis (Ph.D.)--University of Washington, 2016-08

Optimization of microwave-assisted extraction of hydrophilic and lipophilic antioxidants from a surplus tomato crop by response surface methodology

Tomato is the second most important vegetable crop worldwide and a rich source of industrially interesting antioxidants. Hence, the microwave-assisted extraction of hydrophilic (H) and lipophilic (L) antioxidants from a surplus tomato crop was optimized using response surface methodology. The relevant independent variables were temperature (T), extraction time (t), ethanol concentration (Et) and solid/liquid ratio (S/L). The concentration-time response methods of crocin and β-carotene bleaching were applied, since they are suitable in vitro assays to evaluate the antioxidant activity of H and L matrices, respectively. The optimum operating conditions that maximized the extraction were as follows: t, 2.25 min; T, 149.2 ºC; Et, 99.1 %; and S/L, 45.0 g/L for H antioxidants; and t, 15.4 min; T, 60.0 ºC; Et, 33.0 %; and S/L, 15.0 g/L for L antioxidants. This industrial approach indicated that surplus tomatoes possess a high content of antioxidants, offering an alternative source for obtaining natural value-added compounds. Additionally, by testing the relationship between the polarity of the extraction solvent and the antioxidant activity of the extracts in H and L media (polarity-activity relationship), useful information for the study of complex natural extracts containing components with variable degrees of polarity was obtained.

Estimation of water use and crop coefficients for an intensive olive orchard using sap flow measurements and modeled data

Olive tree sap flow measurements were collected in an intensive orchard near Évora, Portugal, during the irrigation seasons of 2013 and 2014, to calculate daily tree transpiration rates (T_SF). Meteorological variables were also collected to calculate reference evapotranspiration (ETo). Both data were used to assess values of basal crop coefficient (Kcb) for the period of the sap flow observations. The soil water balance model SIMDualKc was calibrated with soil, biophysical ground data and sap flow measurements collected in 2013. Validated in 2014 with collected sap flow observations, the model was used to provide estimates of dual e single crop coefficients for 2014 crop growing season. Good agreement between model simulated daily transpiration rates and those obtained with sapflow measurements was observed for 2014 (R2=0.76, RMSE=0.20 mm d-1), the year of validation, with an estimation average absolute error (AAE) of 0.20 mm d-1. Olive modeled daily actual evapotranspiration resulted in atual ETc values of 0.87, 2.05 and 0.77 mm d-1 for 2014 initial, mid- and end-season, respectively. Actual crop coefficient (Kc act) values of 0.51, 0.43 and 0.67 were also obtained for the same periods, respectively. Higher Kc values during spring (initial stage) and autumn (end-stage) were published in FAO56, varying between 0.65 for Kc ini and 0.70 for Kc end. The lower Kc mid value of 0.43 obtained for the summer (mid-season) is also inconsistent with the FAO56 expected Kc mid value of 0.70 for the period. The modeled Kc results are more consistent with the ones published by Allen & Pereira [1] for olive orchards with effective ground cover of 0.25 to 0.5, which vary between 0.40 and 0.80 for Kc ini, 0.40–0.60 for Kc mid with no active ground cover, and 0.35–0.75 for Kc end, depending on ground cover. The SIMDualKc simulation model proved to be appropriate for obtaining evapotranspiration and crop coefficient values for our intensive olive orchard in southern Portugal.

AgroDB - integration of database management systems with crop models.

2016

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

The Basic Reproduction Number Obtained From Jacobian And Next Generation Matrices - A Case Study Of Dengue Transmission Modelling.

The basic reproduction number is a key parameter in mathematical modelling of transmissible diseases. From the stability analysis of the disease free equilibrium, by applying Routh-Hurwitz criteria, a threshold is obtained, which is called the basic reproduction number. However, the application of spectral radius theory on the next generation matrix provides a different expression for the basic reproduction number, that is, the square root of the previously found formula. If the spectral radius of the next generation matrix is defined as the geometric mean of partial reproduction numbers, however the product of these partial numbers is the basic reproduction number, then both methods provide the same expression. In order to show this statement, dengue transmission modelling incorporating or not the transovarian transmission is considered as a case study. Also tuberculosis transmission and sexually transmitted infection modellings are taken as further examples.

Validation of the Deardorff model for estimating energy balance components for a sugarcane crop

The quantification of the available energy in the environment is important because it determines photosynthesis, evapotranspiration and, therefore, the final yield of crops. Instruments for measuring the energy balance are costly and indirect estimation alternatives are desirable. This study assessed the Deardorff's model performance during a cycle of a sugarcane crop in Piracicaba, State of São Paulo, Brazil, in comparison to the aerodynamic method. This mechanistic model simulates the energy fluxes (sensible, latent heat and net radiation) at three levels (atmosphere, canopy and soil) using only air temperature, relative humidity and wind speed measured at a reference level above the canopy, crop leaf area index, and some pre-calibrated parameters (canopy albedo, soil emissivity, atmospheric transmissivity and hydrological characteristics of the soil). The analysis was made for different time scales, insolation conditions and seasons (spring, summer and autumn). Analyzing all data of 15 minute intervals, the model presented good performance for net radiation simulation in different insolations and seasons. The latent heat flux in the atmosphere and the sensible heat flux in the atmosphere did not present differences in comparison to data from the aerodynamic method during the autumn. The sensible heat flux in the soil was poorly simulated by the model due to the poor performance of the soil water balance method. The Deardorff's model improved in general the flux simulations in comparison to the aerodynamic method when more insolation was available in the environment.

Importance of considering a material micro-failure criterion in the numerical modelling of the shot peening process applied to parabolic leaf springs

Shot peening is a cold-working mechanical process in which a shot stream is propelled against a component surface. Its purpose is to introduce compressive residual stresses on component surfaces for increasing the fatigue resistance. This process is widely applied in springs due to the cyclical loads requirements. This paper presents a numerical modelling of shot peening process using the finite element method. The results are compared with experimental measurements of the residual stresses, obtained by the X-rays diffraction technique, in leaf springs submitted to this process. Furthermore, the results are compared with empirical and numerical correlations developed by other authors.

Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil

Since 2000, the southwestern Brazilian Amazon has undergone a rapid transformation from natural vegetation and pastures to row-crop agricultural with the potential to affect regional biogeochemistry. The goals of this research are to assess wavelet algorithms applied to MODIS time series to determine expansion of row-crops and intensification of the number of crops grown. MODIS provides data from February 2000 to present, a period of agricultural expansion and intensification in the southwestern Brazilian Amazon. We have selected a study area near Comodoro, Mato Grosso because of the rapid growth of row-crop agriculture and availability of ground truth data of agricultural land-use history. We used a 90% power wavelet transform to create a wavelet-smoothed time series for five years of MODIS EVI data. From this wavelet-smoothed time series we determine characteristic phenology of single and double crops. We estimate that over 3200 km(2) were converted from native vegetation and pasture to row-crop agriculture from 2000 to 2005 in our study area encompassing 40,000 km(2). We observe an increase of 2000 km(2) of agricultural intensification, where areas of single crops were converted to double crops during the study period. (C) 2007 Elsevier Inc. All rights reserved.

Economic viability of doses and split-applications of nitrogen fertilization in corn crop in a eutrophic Red Latosol

Nitrogen is the nutrient that is most absorbed by the corn crop, with the most complex management, and has the highest share on the cost of corn production. The objective of this work was to evaluate the economic viability of different rates and split-applications of nitrogen fertilization, as such as urea, in the corn crop in a eutrophic Red Latosol (Oxisol). The study was carried out in the Experimental Station of the Regional Pole of the Sao Paulo Northwest Agribusiness Development (APTA), in Votuporanga, State of Sao Paulo, Brazil. The experimental design was randomized complete blocks with nine treatments and four replications, consisting of five N rates: 0, 55, 95, 135 and 175 kg ha(-1), 15 kg ha-l applied in the seeding and the remainder in top dressing: 40 and 80 kg ha(-1) N at forty days after seeding (DAS), or 1/2 + 1/2 at 20 and 40 DAS; 120 kg ha-1 N split in 1/2 + 1/2 or 1/3 + 1/3 + 1/3 at 20, 40 or 60 DAS; 160 kg ha(-1) N split in 1/4 + 3/8 + 3/8 or 114 + 1/4 + 1/4 + 1/4 at 20, 40, 60 and 80 DAS. The application of 135 kg ha-l of N split in three times provided the best benefit/cost ratio. The non-application of N provided the lowest economic return, proving to be unviable.

A contribution for thermoeconomic modelling: A methodology proposal

This work presents a thermoeconomic optimization methodology for the analysis and design of energy systems. This methodology involves economic aspects related to the exergy conception, in order to develop a tool to assist the equipment selection, operation mode choice as well as to optimize the thermal plants design. It also presents the concepts related to exergy in a general scope and in thermoeconomics which combines the thermal sciences principles (thermodynamics, heat transfer, and fluid mechanics) and the economic engineering in order to rationalize energy systems investment decisions, development and operation. Even in this paper, it develops a thermoeconomic methodology through the use of a simple mathematical model, involving thermodynamics parameters and costs evaluation, also defining the objective function as the exergetic production cost. The optimization problem evaluation is developed for two energy systems. First is applied to a steam compression refrigeration system and then to a cogeneration system using backpressure steam turbine. (C) 2010 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.