Principles of crop modelling and simulation: II. the implications of the objective in model development

Neto, Durval Dourado; Teruel, Dioger Alexandre; Reichardt, Klaus; Nielsen, D. R.; Frizzone, José Antônio; Bacchi, Osny Oliveira Santos

doi:10.1590/s0103-90161998000500009

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…The important role of precision agriculture (PA) for tackling issues like global food supply, adaptation of crops to climatic changes or serving energy needs with renewable biomass is often emphasized [ 1 – 5 ]. Of central importance in PA are geodata of crops because they allow for site-specific management of crop fields [ 6 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture

Hämmerle

Höfle

2014

Sensors

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3D geodata play an increasingly important role in precision agriculture, e.g., for modeling in-field variations of grain crop features such as height or biomass. A common data capturing method is LiDAR, which often requires expensive equipment and produces large datasets. This study contributes to the improvement of 3D geodata capturing efficiency by assessing the effect of reduced scanning resolution on crop surface models (CSMs). The analysis is based on high-end LiDAR point clouds of grain crop fields of different varieties (rye and wheat) and nitrogen fertilization stages (100%, 50%, 10%). Lower scanning resolutions are simulated by keeping every n-th laser beam with increasing step widths n. For each iteration step, high-resolution CSMs (0.01 m2 cells) are derived and assessed regarding their coverage relative to a seamless CSM derived from the original point cloud, standard deviation of elevation and mean elevation. Reducing the resolution to, e.g., 25% still leads to a coverage of >90% and a mean CSM elevation of >96% of measured crop height. CSM types (maximum elevation or 90th-percentile elevation) react differently to reduced scanning resolutions in different crops (variety, density). The results can help to assess the trade-off between CSM quality and minimum requirements regarding equipment and capturing set-up.

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Section: Introductionmentioning

confidence: 99%

Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture

Hämmerle

Höfle

2014

Sensors

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“…The results are of application in diverse, climatebased models of dynamic simulation in plant growth (Acock, 1991), with climate as a direct influence on gross photosynthetic rate at a given time (Pereira and Machado, 1986;Dourado-Neto et al, 1998). A direct application of monthly RH data can be performed by the program ClimGen (Stöckle et al, 1999), an extension of the crop growth simulator Cropsyst.…”

Section: Discussionmentioning

confidence: 99%

Development of relative humidity models by using optimized neural network structures

et al. 2010

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Climate has always had a very important role in life on earth, as well as human activity and health. The influence of relative humidity (RH) in controlled environments (e.g. industrial processes in agro-food processing, cold storage of foods such as fruits, vegetables and meat, or controls in greenhouses) is very important. Relative humidity is a main factor in agricultural production and crop yield (due to the influence on crop water demand or the development and distribution of pests and diseases, for example). The main objective of this paper is to estimate RH [maximum (RH max ), average (RH ave ), and minimum (RH min )] data in a specific area, being applied to the Region of Castilla-La Mancha (C-LM) in this case, from available data at thermo-pluviometric weather stations. In this paper Artificial neural networks (ANN) are used to generate RH considering maximum and minimum temperatures and extraterrestrial solar radiation data. Model validation and generation is based on data from the years 2000 to 2008 from 44 complete agroclimatic weather stations. Relative errors are estimated as 1) spatial errors of 11.30%, 6.80% and 10.27% and 2) temporal errors of 10.34%, 6.59% and 9.77% for RH min , RH max and RH ave , respectively. The use of ANNs is interesting in generating climate parameters from available climate data. For determining optimal ANN structure in estimating RH values, model calibration and validation is necessary, considering spatial and temporal variability.Additional key words: artificial neural networks; climate data; limited data. Resumen Desarrollo de modelos de humedades relativas mediante el uso de redes neuronales optimizadasEl clima ha tenido siempre un gran impacto en la vida en la Tierra, la salud, actividad y cultura del hombre. La influencia de la humedad relativa (HR) tiene especial relevancia en ambientes controlados (por ejemplo, procesos industriales de elaboración y conservación de productos agroalimentarios, o el control en invernaderos). En el ámbito agrario también es un elemento fundamental, ya que puede modificar el rendimiento de los cultivos y la incidencia de plagas y enfermedades, sin olvidar su influencia directa en la demanda evaporativa de la atmósfera. El objetivo principal de este estudio es estimar valores de HRs ambientales (máximas, medias, y mínimas) en un territorio concreto, en este caso aplicado a Castilla-La Mancha (España) a partir de registros disponibles en estaciones termopluviométricas. Para ello, se ha planteado el uso de herramientas basadas en redes neuronales artificiales (RNAs), que permitan generar datos de HRs a partir de registros de temperaturas máximas y mínimas, y datos de radiación solar extraterrestre. Para generar y validar estos modelos se utilizan registros de los años 2000-2008, procedentes de 44 estaciones agroclimáticas completas ubicadas en esta comunidad autónoma. La metodología propuesta ofrece errores relativos espaciales del 11,30%, 6,80% y 10,27%, y temporales del 10,34%, 6,59% y 9,77% para las HRs mínimas, máximas y medi...

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“…Essas simplificações normalmente envolvem diminuição no nível de detalhes e desconsideração de processos que quantitativamente são de menor importância, o que diminui bastante a quantidade de informações exigidas para alimentar o modelo (Rao et al, 1982;Geypens & Vandendriessche, 1996). Isso torna o modelo suficientemente simples, para permitir seu entendimento e utilização, mas suficientemente complexo, para permitir sua utilização mais abrangente (Dourado Neto et al, 1998b Um método eficiente e de boa exatidão para estimar a necessidade de adubação de uma cultura, o qual satisfaz os princípios das leis gerais da adubação, é o do balanço nutricional. Esse método tem sido recomendado para calcular a necessidade de adubação nitrogenada para a cultura do milho nos Estados Unidos (Stanford, 1973) e, mais recentemente, no Brasil, para calcular a necessidade de adubação para eucalipto (Barros et al, 1995), milho (Carvalho, 2000), tomate (Mello, 2000), arroz irrigado (Raffaeli, 2000), café (Prezotti, 2001;Novais et al, 2002), cana-de-açúcar (Freire, 2001), soja (Santos, 2002), coqueiro (Rosa, 2002), bananeira (Oliveira, 2002), algodão (Possamai, 2003) e pastagem (Santos, 2003).…”

Section: Introductionunclassified

Desenvolvimento de um sistema para recomendação de adubação para a cultura da bananeira

Oliveira

Novais

et al. 2005

Rev. Bras. Ciênc. Solo

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RESUMOAs recomendações de adubação para a cultura da bananeira devem ser mais confiáveis do ponto de vista técnico e, principalmente, mais propensas a ajustes com bases científicas, em relação às tabelas. O objetivo deste trabalho foi desenvolver um sistema para estimar doses de nutrientes a serem recomendadas para a cultura da bananeira (FERTICALC ® ® ® ® ® -Bananeira), por meio da modelagem do balanço nutricional. O balanço nutricional é obtido pela diferença entre o requerimento do nutriente pela cultura e seu suprimento pelo solo e resíduos vegetais. Se o requerimento for maior que o suprimento, recomenda-se a aplicação de fertilizante; se for inferior ou igual ao suprimento, não se recomenda a aplicação de fertilizante. Simulações mostraram que as doses de nutrientes a serem recomendadas pelo FERTICALC ® ® ® ® ® -Bananeira aumentam continuamente com o aumento da produtividade esperada e com a diminuição dos teores desses nutrientes no solo, sendo maiores no primeiro ciclo e menores a partir do segundo ciclo. Considerando a lógica envolvida em sua constituição e as variáveis utilizadas na modelagem do FERTICALC ® ® ® ® ® -Bananeira, este sistema constitui importante alternativa para recomendação de adubação para a cultura da bananeira.Termos de indexação: banana, balanço nutricional, requerimento de nutrientes, suprimento de nutrientes, modelagem.

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Principles of crop modelling and simulation: II. the implications of the objective in model development

Cited by 9 publications

References 10 publications

Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture

Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture

Development of relative humidity models by using optimized neural network structures

Desenvolvimento de um sistema para recomendação de adubação para a cultura da bananeira

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