ABSTRACT Reducing water consumption by crops in semi-arid regions is an important factor for the sustainability of agriculture in these locations. In this sense, this study aims to evaluate the neuro-fuzzy inference method as a support for decision-making in irrigated coriander cultivation. The experiment was performed in two cultivation cycles in Pentecoste-CE, Brazil. The experiment was conducted in randomized blocks arranged in a split-plot design with five primary treatments, consisting of irrigation depths (50, 75, 100, 125, and 150% of the localized evapotranspiration, ETcloc), and five secondary treatments, consisting of different levels of bagana mulch (0, 25, 50, 75, and 100%, equivalent to 16 t ha−1). Neuro-fuzzy models with two input variables and eight output biometric variables were developed to evaluate growth (plant height, number of roots, and root length) and yield variables (productivity and shoot and root fresh and dry mass). In the first cycle, the best results occurred close to 55% ETcloc and between 40 and 50% of mulch; in the second cycle, water consumption returned results between 50 and 80% ETcloc. The fuzzy and multiple regression models showed MAE, MSE, and RMSE errors of 9, 22, and 10% lower, respectively. The neuro-fuzzy model might be a viable option for decision-making in irrigated crops, being able to optimize the use of natural resources and available water in semi-arid regions. The use of 55% of irrigation depth and a range of 40 to 50% of mulch can be a strategy for a higher water use efficiency.

ABSTRACT The increasing electricity consumption, inflated costs, and environmental restrictions drive the need for more efficient energy usage and the development of energy conservation programs. Many farms in Brazil produce their animal feed by crushing grains and cereals using electric motor-equipped devices. However, these motors are often oversized and operate during peak times, leading to increased production costs. This study aimed to mathematically model electricity billing under the green tariff (group A) for an egg production farm. The model incorporated mathematical equations based on selected electrical parameters such as load, power factors, energy demand, and active consumption. The Mathematica software was used to implement the proposed model. The farm provided the past twelve electricity bills, which were inputted into the software, generating three-dimensional surface graphics and contour maps. These visualizations revealed an inversely proportional relationship between bill prices and power/load factor indicators. This study concludes that the presented methodology can be employed by agribusiness companies to assess performance, explore alternatives, avoid potential fines, and optimize energy consumption. consumption costs programs motorequipped motor equipped devices However times group A load factors demand bills threedimensional three dimensional maps powerload indicators performance alternatives fines

ABSTRACT Agribusiness and commerce are two major sectors that consume electricity. Agribusiness can generate energy but not enough to meet its needs. Thus, energy efficiency is fundamental for the sector’s sustainability. This study aimed to apply PROCEL energy evaluation methods in a Brazilian agribusiness company, specifically in the meat retail sector, located in the Alta Paulista region. This research has a quantitative approach, and PROCEL’s RTQ-C manual, bibliographical research on the energy issue, and the functioning of the company’s environment were used. The commercial establishment received the B classification index, which can be considered good but has several points of improvement, mainly on air conditioning, which provides better energy efficiency to the company. Therefore, the company can obtain economic and environmental returns by improving such areas, and the present research serves as a basis for evaluating energy efficiency in other companies in the same sector. electricity needs Thus sector s sustainability region approach PROCELs RTQC RTQ C manual issue companys used index improvement conditioning Therefore areas

ABSTRACT The objective of this study was to develop a Fuzzy Rule-Based System (FRBS) for the mathematically modeling of the irrigation level effect on beet cultivars ( Beta vulgaris L.). From an agronomic experiment carried out in a protected environment (greenhouse), it was defined as input variables, for each cultivar, the irrigation levels (depths of 25, 40, 55, 70, 85, and 100% of ETc), in which ETc is the crop evapotranspiration [mm d−1], while the output variables were productivity, root diameter, and plant height. Was adopted the inference method of Mamdani and centroid method for fuzzification. The fuzzy models performed better than polynomial regression models, when compared with the coefficients of determination (R2), mean absolute error (MAE) and root mean square error (RMSE). In addition, the excellent performance of the model is highlighted due to the curve containing all the points of the treatments means. As agronomic results, concluded that the Early Wonder cultivar has higher yields when submitted to water deficit.

ABSTRACT The increase of water usage for food production in recent years has triggered researches on ways to optimize the use of water and to reuse saline water. The present study analyzes the effects of reused saline water in the irrigation of radish culture and the construction of a fuzzy logical mathematic model so that producers may evaluate their production. The experiment, measuring the development of radish bulb at five saline water levels, was developed in a greenhouse in the Botucatu, Campus of the São Paulo State University (UNESP), Botucatu/SP, Brazil. Results showed that salinity caused reduction in fresh and dry matter of the bulb and affected ratings throughout the culture cycle. From the developed fuzzy model, it was possible to verify that the fuzzy modeling helps in the analysis of the experimental data and makes it possible to perform simulations capable of inferring points that were not experimentally determined.

ABSTRACT The low quality of the water currently used for irrigation purposes harms the quality of the products and can lead to a reduction in production. Thus, the objective of this work was to verify the effect of saline water in irrigation water, on biometric variables of pumpkin crop using a system based on fuzzy rules. The agronomic experimental part of this work was carried out in a greenhouse. The experimental design was in randomized blocks, with 5 doses of salinity (0, 1.25, 2.5, 3.75, and 5 dS m-1) and with 5 repetitions. The salinity doses and evaluations carried out throughout the cycle (days after transplanting) were defined as input variables in the mathematical model. For the output variables, the collected biometric responses were defined: number of leaves, number of flowers, leaf fresh mass, leaf dry mass, stem fresh mass, stem length, root fresh mass, and root length. After evaluation, the mathematical model was developed and its validation was carried out using statistical methods and Receiver Operating Characteristic Curves (ROC). It was observed that salinity affects the bush pumpkin crop with a reduction in the evaluated parameters. The mathematical model proved to be efficient for this evaluation.

ABSTRACT Optimal solutions derived from linear programming models depend entirely on input parameters, which may present some imprecision because they come from estimates. Fuzzy linear programming allows the incorporation of these uncertainties in linear models, which can include the flexibility of resources, costs, goals, and constraints. This paper aimed to show new optimal solutions for a model to minimize the equivalent annual cost of micro-irrigation systems on sloping terrains. The Zimmermann-Werner fuzzy linear programming method, whose objective function is diffuse due to the restrictions of the hydraulic network being dispersed, was used. Sixty models were created and all solutions were satisfactory, with an annual cost of the irrigation system lower than the original model. The lowest value was US$ 238.74 ha-1, which occurred on the 3% slope. A reduction was observed in the annual cost due to the increased use of pipes with a 50-mm nominal diameter in the secondary line. Thus, fuzzy linear programming provided better solutions with small modifications to the irrigation system, while maintaining all hydraulic network requirements for proper system operation.

ABSTRACT In order to estimate the response of biometric variables in different irrigation depths in radish crop, as well as their relations in the development of the crop, a fuzzy mathematical analysis was carried out from irrigation with depths of different percentages of the crop evapotranspiration (ETc), using Gaussian pertinence functions for the input variable and triangular for the biometric output variables. Validations were performed using neural network models, smoothing splines and polynomial regression. The relation among the biometric variables was measured applying the Pearson correlation coefficient. The results showed that the fuzzy modeling presented superiority in the crop development estimate over the quadratic polynomial regression model, neural network and smoothing splines, because it achieved an average reduction of errors among the biometric variables, of 7.8% 94.6% and 9.2% for the RMSE in the respective models, as well as a better adjustment of the data with average R2 of the variables. The modeling with neural network showed inadequate agronomic behavior in data representation. Regarding biometric variables, the length and diameter of the tuberous root are inversely correlated, and the fresh phytomass of the tuberous root is correlated only with the fresh phytomass of the root.

ABSTRACT The productivity of a crop is related to the water demand inserted in its development. The measurement of water and its optimization directly influences the final costs of crop production for agricultural producers. In this sense, the objective of this study is evaluating the fuzzy modeling in estimating the productivity of the radish crop (fresh phytomass of the tuberous root) affected by different irrigation depths (25%, 50%, 75%, 100%, and 125%), based on evapotranspiration of the crop (ETc). To measure the results, two fuzzy systems (with triangular and Gaussian membership functions respectively) and a polynomial regression model were developed to perform model validation comparisons. The fuzzy modeling showed a better fit of the data compared to the polynomial regression model, with reduced errors (RMSE with values 6.3 and 6.9 in the fuzzy models versus 8.8 in the regression model) and higher correlation coefficient (0.54 and 0.5 fuzzy versus 0.1 regression). The triangular fuzzy model estimated the best crop yield (31.9 g of fresh phytomass) when using a 100% ETc depth. Also, the curve generated by the fuzzy model accurately represents all the productivity averages in each depth, in addition to this model presenting the smallest errors (compared to the triangular model and the regression model) and the highest R2. However, the Gaussian fuzzy model proved to be more efficient in representing the agronomic reality, as it does not have peaks and valleys, and it is a smooth model in both growth and degrowth.

ABSTRACT The present work aimed to develop mathematical fuzzy models to evaluate the effects of different doses of sewage sludge and irrigation with wastewater and potable water. Such models were elaborated from an experiment carried out at the Faculty of Agronomic Sciences, in the Department of Soil and Environmental Resources, from the Sao Paulo State University, in Brazil. The experiment was carried out in a randomized block design, in a 6 × 2 factorial scheme, with 6 doses of sewage sludge (0, 25, 50, 75, 100 and 125 of the recommended dose of N), and in the presence and absence of wastewater. In the development of the fuzzy model, the Mamdani method was used for the defuzzification. As input variables, the doses of sewage sludge and the types of water were used. For the output variables, it was sought to evaluate the biometric and developmental components of the culture. It can be inferred that the model developed presented a good fit when compared to the regression model, and that the use of sewage sludge may prove to be a potential future replacement of mineral nitrogen.

ABSTRACT Tomato, the most popular greenery, is characterized by being a demanding crop in water and when in prolonged and severe drought, has limitations in its growth and reduction in productivity. In addition, this vegetable is affected by excess salinity in the water, which causes leaf wilting, apex and leaf edges burn until their death. Such effects generally are studied using statistical analysis, but there are mathematical theories that allow finer adjustments, highlighting among them, the fuzzy logic. The objective of this study was to evaluate the effects on the growth and yield of hybrid tomato from different water tensions in the soil and different salinity doses in the irrigation at 120 days after sowing using fuzzy modeling. It was used data from an experiment that was conducted in the experimental area at the School of Agriculture of São Paulo State University (UNESP) in Botucatu-SP. The input variables of the fuzzy systems were irrigation and salinity, while the output variables elected were the biometric analysis and productivity that showed statistically significant differences. Analyses of the effects of irrigation and salinity were performed by means of three-dimensional graphics and the output variables contour maps. The variables studied here showed higher values, with exception of plant height for treatment with irrigation in field capacity and water salinity zero.

ABSTRACT It was used statistical techniques for the evaluation of agricultural experiments, but there are mathematical theories that allow finer adjustments, highlighting among them, the fuzzy logic. The objective of the study was characterizing a method of fuzzy modeling from an agronomic experiment. For this study it was used data from an experiment conducted at the School of Agriculture of São Paulo State University (UNESP) in Botucatu-SP. The system input variables based in fuzzy rules were soil water tension and doses of water salinity, being defined three fuzzy sets. The output variables was elected from the biometric and productivity analysis that showed statistically significant differences, namely, plant height, stem diameter, leaf area, green biomass, dry weight, number of fruits, average fruit weight and percentage of disabled fruits. For output variables 9 fuzzy sets were defined. From the adopted methodology, the model allowed extract directly from the data set a base of rules without the use of questionnaires to experts for its preparation. In addition, it will analyze intermediate regions at trial levels and weave other conclusions of the tomato growth and productivity, not limiting in this way only those observed with statistical analysis.

ABSTRACT: This study aimed to evaluate the effects of fertilization with composted sewage sludge and irrigation with drinking water (DW) and fertigation with wastewater (WW) in wheat crop using fuzzy rule-based system. The experiment was conducted in the Department of Soil and Environmental Resources, of FCA, UNESP - Botucatu, with factorial 6 × 2, which were applied 6 doses of sewage sludge (0, 50, 100, 150, 200 and 250% of nitrogen recommendation) and two types of effluents (treated water and sewage). In the developing of the system based in fuzzy rules, it was used the Mamdani inference method, where the input variables were sewage sludge doses and water types and the output variables used were the number of tillers, length and number spike per plant; number of spikelet per plant, grain mass per spike and dry mass of the aerial parts. It can be seen that the sewage sludge and the effluent contributed to the higher increase of production, and at 150% sludge dose occurred higher production.

RESUMO O Índice de Massa Corporal (IMC) pode ser utilizado por pecuaristas para auxiliar na determinação do momento da avaliação de ganho corporal do animal. No entanto, o cálculo desse índice não revela imediatamente se o animal está apto ao abate ou se necessita de cuidados especiais para recuperação. O objetivo deste trabalho foi desenvolver um software utilizando a lógica fuzzy para a comparação da massa corporal de bovinos entre si e identificação dos grupos para abate, e dos que necessitam de alimentação mais intensa, utilizando-se das variáveis “massa” e “altura”, e a saída IMCFuzzy. Para a elaboração do software, utilizou-se de um sistema fuzzy com aplicações em um rebanho de 147 vacas nelore, localizado em Santa Rita do Pardo-MS, e um banco de dados gerado pelo software Matlab.

ABSTRACT The Body Mass Index (BMI) can be used by farmers to help determine the time of evaluation of the body mass gain of the animal. However, the calculation of this index does not reveal immediately whether the animal is ready for slaughter or if it needs special care fattening. The aim of this study was to develop a software using the Fuzzy Logic to compare the bovine body mass among themselves and identify the groups for slaughter and those that requires more intensive feeding, using "mass" and "height" variables, and the output Fuzzy BMI. For the development of the software, it was used a fuzzy system with applications in a herd of 147 Nellore cows, located in a city of Santa Rita do Pardo city – Mato Grosso do Sul (MS) state, in Brazil, and a database generated by Matlab software.

Este trabalho apresenta um sistema baseado em regras fuzzy que resultou em um índice que indica o nível de incerteza relacionada com as transações comerciais entre produtores de mandioca e de seus compradores, com base na abordagem da Economia dos Custos de Transação. O sistema fuzzy foi desenvolvido utilizando como variáveis de entrada "Previsão de Demanda/compra", "Previsão de Produção" e "Compartilhamento de informações acerca de inovações de produção". A variável de saída consiste no nível de incerteza da transação entre vendedor e agente comprador, que pode servir como um sistema para a detecção de ineficiências. Entrevistas realizadas com 27 produtores de mandioca registrados nos Escritórios de Desenvolvimento Regionais de Tupã e Assis, São Paulo, Brasil, e 48 de seus compradores sustentaram o desenvolvimento do sistema. O modelo matemático aplicado na avaliação destes produtores indicou que 55% dos produtores têm relações comerciais com nível de incerteza Muito Alta, 25% Média ou Alta; e os demais, classificados entre Baixa ou Média. A partir dos resultados encontrados, simulações de ações poderiam ser implementadas para reduzir o grau de incerteza e os custos de transação envolvidos.

A fuzzy ruled-based system was developed in this study and resulted in an index indicating the level of uncertainty related to commercial transactions between cassava growers and their dealers. The fuzzy system was developed based on Transaction Cost Economics approach. The fuzzy system was developed from input variables regarding information sharing between grower and dealer on “Demand/purchase Forecasting”, “Production Forecasting” and “Production Innovation”. The output variable is the level of uncertainty regarding the transaction between seller and buyer agent, which may serve as a system for detecting inefficiencies. Evidences from 27 cassava growers registered in the Regional Development Offices of Tupa and Assis, São Paulo, Brazil, and 48 of their dealers supported the development of the system. The mathematical model indicated that 55% of the growers present a Very High level of uncertainty, 33% present Medium or High. The others present Low or Very Low level of uncertainty. From the model, simulations of external interferences can be implemented in order to improve the degree of uncertainty and, thus, lower transaction costs.