The effect of nitrogen fertilization in sugarcane yield evaluated with machine learning models

Orientador: Luiz Henrique Antunes RodriguesTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: Na produção de cana-de-açúcar, é frequentemente reportada a falta de resposta para adubação com nitrogênio (N), fazendo com que sua efetividade e necessidade sejam questionadas. Dado o potencial de uso ineficiente dos recursos e de potenciais efeitos ambientais negativos, a recomendação adequada da quantidade de N aplicado é essencial para sustentabilidade financeira e ambiental da produção. Neste estudo, modelos de aprendizado de máquina foram desenvolvidos e aplicados para avaliação do efeito da adubação nitrogenada na produtividade da cana-de-açúcar, assim como dos fatores que interagem com essa prática. Para isso, modelos de produtividade de cana-de-açúcar foram desenvolvidos com técnicas de aprendizado de máquina aplicados a dados de produção comercial de cana-de-açúcar. Foi conduzida a análise de sensibilidade de primeira ordem, que foi comparada com a importância das variáveis nos conjuntos de dados, e a análise de sensibilidade de segunda ordem para estudo das interações de outras variáveis com a fertilização com N. Os resultados foram analisados com base em gráficos de resposta parcial, priorizados pela importância ou sensibilidade das variáveis. Gráficos de resposta condicional foram utilizados para distinguir o padrão de resposta geral (resposta marginal) do padrão de resposta local (resposta condicional para cada condição encontrada nos dados). Foi constatado que o padrão de respostas individuais não apresenta respostas consistentes para a produção de cana-de-açúcar, embora as respostas gerais sejam mais coerentes. Considera-se então que não é recomendável utilizar a saída de modelos gerados utilizando as técnicas empregadas neste trabalho para análises de respostas individuais, o que seria por exemplo, necessário para recomendação de adubação para cada talhão de cana-de-açúcar. Usos pautados pela resposta geral parecem não ser afetados e devem ser avaliados em trabalhos futurosAbstract: Lack of response to nitrogen fertilization is often reported for sugarcane production, leading to questions regarding its necessity and effectiveness. Given the potential for inefficient resource usage and potential negative environmental impacts, properly recommending the amount of N fertilizer is essential for a financial and environmental sustainable sugarcane production. In this thesis, machine learning models of sugarcane yield were developed and applied to evaluate the effects of Nitrogen fertilization in sugarcane yield, as well as factors that interacts with this practice. First order sensitivity analysis was performed and compared with feature importance measured in the datasets used for modeling, and second-order sensitivity analysis was performed to evaluate interactions with N fertilization in the model. Results were evaluated based on the partial response plots, prioritized by feature importance and variable sensitivity. Independent conditional expectancy graphics are also used to evaluate the individual response of plots (conditioned response in each condition modeled) and to evaluate the differences from the general response pattern (marginalized response). From the results of the visual analysis, it can be seen that individual responses are not consistent with common knowledge for sugarcane production, even though some of the general responses are more coherent. Based on these results, the use of such models for individual analysis and recommendations, such as needed for nitrogen fertilization recommendation, are not recommended. The use based on the general response may not be affected and could be further evaluated in future worksDoutoradoGestão de Sistemas na Agricultura e Desenvolvimento RuralDoutor em Engenharia Agrícola140615/2017-2CAPESCNP

Identification of patterns for increasing production with decision trees in sugarcane mill data

Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments

When do I want to know and why? Different demands on sugarcane yield predictions

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOThe production planning processes of sugarcane mills require quantitative information to support decisions on sugarcane yield and the effects of decisions made during planning. An exploratory study was conducted at a sugarcane mill with the goals of identifying the main decisions influenced by the prospects of future yield and of evaluating the manner in which those forecasts affect planning. Key decisions and their characteristics were identified based on a series of interviews and activity monitoring. These decisions are presented and discussed in relation to various solutions proposed by the scientific community for planning, as well as within the concept of Advanced Planning Systems. The yield forecasts used to inform budgeting and harvesting plans are of critical importance because actions taken based on those forecasts affect the entire value chain, highlighting the need for a decision-making framework that assess the effects of decisions on subsequent processes. Advanced Planning Systems design to the sugar value chain should incorporate the use of yield forecasts for production and must address the uncertainties throughout the entire system. These improvements can enhance the performances of Advanced Planning Systems by producing an integrated planning approach that is based on a comprehensive assessment of the sugar value chain. (C) 2014 Elsevier Ltd. All rights reserved.The production planning processes of sugarcane mills require quantitative information to support decisions on sugarcane yield and the effects of decisions made during planning. An exploratory study was conducted at a sugarcane mill with the goals of ident1354856FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP [12/50049-3]12/50049-

How accurate are pedotransfer functions for bulk density for Brazilian soils?

The aim of this study was to evaluate the performance of pedotransfer functions (PTFs) available in the literature to estimate soil bulk density (ρb) in different regions of Brazil, using different metrics. The predictive capacity of 25 PTFs was evaluated using the mean absolute error (MAE), mean error (ME), root mean squared error (RMSE), coefficient of determination (R2) and the regression error characteristic (REC) curve. The models performed differently when comparing observed and estimated ρb values. In general, the PTFs showed a performance close to the mean value of the bulk density data, considered as the simplest possible estimation of an attribute and used as a parameter to compare the performance of existing models (null model). The models developed by Benites et al. (2007) (BEN-C) and by Manrique and Jones (1991) (M&J-B) presented the best results. The separation of data into two layers according to depth (0-10 cm and 10-30 cm) demonstrated better performance in the 10-30 cm layer. The REC curve allowed for a simple and visual evaluation of the PTFs

PATOLOGIA DO QUADRIL DAS CRIANÇAS: UMA REVISÃO INTEGRATIVA

Many of the pathologies that affect the hip in adults have their origins in childhood, making the early diagnosis of these conditions through specific pediatric orthopedic evaluation extremely important. Research shows that late diagnoses are associated with a considerable increase in the number of sequelae. This article consists of an integrative review, which aims to analyze and discuss the main hip pathologies in children, in order to expand the knowledge of students and professionals in the area about the subject in question. The work consists of an integrative literature review, in which a basic, qualitative, exploratory and bibliographic research was carried out in the databases. Hip pathology in children is a field of medical study and treatment that encompasses a variety of conditions and problems that affect the hip joint in younger individuals. These conditions can range from congenital problems to disorders acquired over time. It is critical to understand that healthy hip development is crucial for children's mobility and quality of life, and any issues in this area must be addressed with care and attention. In summary, hip pathologies in children encompass a variety of conditions, from developmental dysplasia of the hip to proximal epiphysis of the femur. Early diagnosis and adequate treatment are essential to avoid complications and long-term sequelae.Muitas das patologias que afetam o quadril de adultos têm suas origens na infância, tornando o diagnóstico precoce dessas condições por meio de avaliação ortopédica pediátrica específica de extrema importância. Pesquisas demonstram que diagnósticos tardios estão associados a um aumento considerável no número de sequelas. O presente artigo consiste em uma revisão integrativa, no qual tem como objetivo analisar e discutir acerca das principais patologias de quadril das crianças, no intuito de ampliar os conhecimentos de estudantes e profissionais da área acerca do tema em questão. O trabalho consiste em uma revisão de literatura do tipo integrativa, na qual foi realizada uma pesquisa dos tipos básica, qualitativa, exploratória e bibliográfica, nas bases de dados. A patologia do quadril em crianças é um campo de estudo e tratamento médico que abrange uma variedade de condições e problemas que afetam a articulação do quadril em indivíduos mais jovens. Essas condições podem variar desde problemas congênitos até distúrbios adquiridos ao longo do tempo. É fundamental entender que o desenvolvimento saudável do quadril é crucial para a mobilidade e a qualidade de vida das crianças e qualquer problema nessa área deve ser abordado com cuidado e atenção. Em suma, as patologias do quadril em crianças abrangem uma variedade de condições, desde a displasia do desenvolvimento do quadril até a epifisiólise proximal do fêmur. O diagnóstico precoce e o tratamento adequado são fundamentais para evitar complicações e sequelas a longo prazo

Sugarcane yield : characteristics of decision contexts and data mining techniques application for modeling

Orientador: Luiz Henrique Antunes RodriguesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: A tomada de decisão e o planejamento de uma usina de cana-de-açúcar têm como principal variável a produtividade dos cultivos, que em conjunto com a área permite estimar a produção. A cana-de-açúcar, uma cultura semi-perene, nas condições brasileiras, possui um ciclo inicial que pode variar de 12 a 18 meses e, após a primeira colheita, é colhida anualmente até que o decréscimo de produtividade leve ao replantio da área. Considerando o tamanho das áreas de cultivo, e o horizonte temporal, projeções de produtividade são fornecidas em diferentes contextos de decisão para cultivos que se encontram em diferentes momentos do ciclo de crescimento. Foi conduzida uma pesquisa exploratória junto a uma usina com intuito de contextualizar as principais decisões que são influenciadas pela perspectiva de produtividade futura, bem como a forma que essas predições afetam o planejamento. Tomando por base o resultado de entrevistas semiestruturadas e acompanhamento de atividades, foi possível identificar decisões chave e suas características, que foram relacionadas a soluções propostas pela comunidade científica e enquadradas dentro de uma proposta de framework para tomada de decisão e planejamento. Entre as decisões, chamou atenção as que são tomadas nos elos iniciais da cadeia de valor, que terão efeitos em todos os processos posteriores e que são tomadas na maior situação de incerteza, sendo consideradas pontos críticos no planejamento. No framework, baseado no uso de modelos empíricos de produtividade, é possível explorar o potencial das informações climáticas para projeção da produtividade e também explorar o potencial dos dados acumulados pelo setor. Para tal, foram desenvolvidos modelos empíricos de produtividade utilizando diferentes técnicas de mineração de dados. Os modelos de produtividade possuíam como atributos preditores os dados referentes aos talhões e seu manejo, em conjunto com os dados do clima ocorrido. Foi possível reduzir a magnitude de erro para menos da metade do encontrado em uma abordagem anterior. Entre as técnicas utilizadas, a SVM e a Random Forest obtiveram os melhores desempenhos, embora o modelo utilizando SVM tenha utilizado significativamente menos atributos. A estratégia de modelagem baseada em dados permitiu a criação de modelos específicos para o contexto produtivo da própria unidade, na escala da menor unidade de gestão, os talhões. Os modelos de produtividade criados possuem potencial para projeção de produtividade se utilizados em conjunto com projeções de climaAbstract: Decision making and planning of sugarcane production have as main variable the crop yield, which in conjunction with the field area allows us to estimate production. Sugarcane, a semi-perennial crop, in Brazilian conditions, has an initial cycle that varies from 12 to 18 months and after the first harvest, is harvested annually until yield reduction lead to replanting the area. Considering the size of cultivated areas, and the time horizon, yield projections are provided in different contexts of decision for crops that are in different stages of the growth cycle. An exploratory study was conducted within a sugarcane mill to contextualize the main decisions that are influenced by the perspective of future yield, as well as how those predictions affect planning. Based on the result of semi-structured interviews and activities follow-up, it was possible to identify key decisions and their characteristics, which were related to the solutions proposed by the scientific community and framed within a proposed framework for decision making and planning. Decisions made in the first echelons of the value chain demanded early predictions and have effects in the whole value chain, being considered a critical point for planning. In the framework, based on the use of empirical models of yield, it is possible to exploit the potential of climate information to forecast yield and also explore the potential of data accumulated by the sector. Empirical yield models were developed using different data mining techniques. The models used and data from the blocks and their management, coupled with the climatic data as predictive variables. Error magnitude was reduced by half from a previous approach. Among the techniques used, SVM and Random Forest got the best performance, although the SVM model has significantly fewer attributes. The modeling strategy based on data enabled the creation of specific models for the production context of the mill, on the scale of the smallest management unit. The yield models created have potential for yield forecast if used in conjunction with weather forecastsMestradoPlanejamento e Desenvolvimento Rural SustentávelMestre em Engenharia Agrícola12/50049-3FAPES

Identification of patterns for increasing production with decision trees in sugarcane mill data

ABSTRACT: Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments

How accurate are pedotransfer functions for bulk density for Brazilian soils?

ABSTRACT: The aim of this study was to evaluate the performance of pedotransfer functions (PTFs) available in the literature to estimate soil bulk density (ρb) in different regions of Brazil, using different metrics. The predictive capacity of 25 PTFs was evaluated using the mean absolute error (MAE), mean error (ME), root mean squared error (RMSE), coefficient of determination (R2) and the regression error characteristic (REC) curve. The models performed differently when comparing observed and estimated ρb values. In general, the PTFs showed a performance close to the mean value of the bulk density data, considered as the simplest possible estimation of an attribute and used as a parameter to compare the performance of existing models (null model). The models developed by Benites et al. (2007) (BEN-C) and by Manrique and Jones (1991) (M&J-B) presented the best results. The separation of data into two layers according to depth (0-10 cm and 10-30 cm) demonstrated better performance in the 10-30 cm layer. The REC curve allowed for a simple and visual evaluation of the PTFs