Data assimilation has been widely used for improvement of crop models’ estimates, for example to incorporate the effects of external events or compensate calibration errors in large areas. The term describes multiple approaches for those who want to take advantage of satellite imagery to reduce uncertainty or improve accuracy of model estimates. Kalman Filters are among the most used methods for achieving these goals. But their use in new contexts, i.e., from open field to protected environments, requires untangling aspects of the pipeline that are often performed in many different ways without guidelines, such as which variables to assimilate or how to ascribe uncertainty to observations or model estimates. This study is then divided in two parts. In the first, we review details on how uncertainty is ascribed on crop model estimates and in observations for applications of the Kalman Filter and three variations of the method, i.e., the Extended, Unscented and Ensemble, as well as which state variables are often updated and the frequency with which assimilation may occur, as well as how these aspects are connected to each other. In the second part, we apply different approaches from the reviewed literature in a greenhouse tomato crop model. We use artificial data with controlled noise levels as well as artificial data generated by simulation using other tomato crop model. We assess the impacts of using different methods and different approaches for ascribing uncertainty in model estimates and in observations, by assimilating artificial observations of fruit and of mature fruit biomass. We note that covariances should not be fixed values, that there are trade-offs between ascribing model uncertainty to the state itself and to other elements of the process, that observation covariance may have been considered disproportionality higher when using some ensemble generation approaches in the EnKF, and that bias in model estimates may lead to worse outcomes even when observations are high-quality ones. While we discussed aspects that should be considered in a new environment, many of them are also important for field crops, and we concluded assimilation should follow an assessment of which variables could be useful for assimilation.

RESUMO O morangueiro, cultivado em solo, torna-se suscetível a fungos nele presentes. Rotações de áreas de plantio são fundamentais para o manejo fitossanitário, realidade impraticável para alguns produtores brasileiros com áreas reduzidas para o cultivo. Essa problemática incentivou o uso de substratos, padrão técnico já bem estabelecido na Europa. O objetivo foi avaliar a resposta do morangueiro (‘Oso Grande’) em três tipos de substratos (fibra de coco granulada, casca de arroz pura e a mistura dos dois em iguais proporções), volumes (1,0; 1,5 e 2,0 L) e frequências de irrigação (duas, três e quatro vezes por dia). O experimento foi realizado em casa de vegetação, em Jundiaí (SP), no período de 2 de julho a 4 de outubro de 2010. Os dados foram submetidos à análise conjunta de experimentos, em esquema fatorial triplo (frequências de irrigação, tipos de substratos e volumes de substrato). O melhor resultado foi para a mistura de substratos, irrigada três vezes por dia, em 1,0 L do material, com produtividade média de 223 g por planta. A casca de arroz é ineficiente para o cultivo em vaso. Entretanto, na forma de misturas, esse substrato torna-se adequado ao cultivo, possibilitando melhores resultados quando comparado à fibra de coco granulada.

ABSTRACT The strawberry, when grown in soil, becomes susceptible to fungi present on it. Rotations of planted areas are essential to control disease, reality unworkable for some Brazilian farmers who have small areas for cultivation. This problem encouraged the use of substrates, a technical pattern already well-established in Europe. The objective was to assess the response of strawberry growth (‘Oso Grande’) on three substrates (granulated coconut fiber, pure rice husk and a mixture of both in equal proportions), volumes (1.0; 1.5 and 2.0 L) and irrigation frequencies (twice, three and four times a day). The experiment was carried out in a greenhouse in Jundiaí (SP), from July 2 to October 4, 2010. The data were submitted to the joint analysis of experiments, in a triple factorial scheme (irrigation frequencies, substrate types and substrate volumes). The best result obtained occurred for substrate mixture, with the irrigation frequency of three times a day, in 1.0 L of the material, with an average yield of 223 g per plant. Rice husk is ineffective for cultivation in pots. Nevertheless, in the form of mixtures, this substrate is suited for cultivation, allowing better results when compared to granulated coconut fiber.

A caracterização física de substratos para plantas no Brasil ainda é incipiente, embora, apenas com a definição adequada de métodos confiáveis seja possível padronizar insumos e fornecer as recomendações necessárias aos produtores. Objetivou-se caracterizar fisicamente seis substratos para plantas e comparar métodos prescritos pelo Ministério da Agricultura, Pecuária e Abastecimento brasileiro e pelo Comitê Europeu de Normatização. Os substratos avaliados foram: fibra de coco granulada, dois tipos de casca de pínus, casca de arroz estabilizada e duas misturas (50:50, v:v), uma de casca de arroz com fibra de coco granulada e a segunda de casca de pínus e casca de arroz. O projeto, realizado em 2009, teve delineamento experimental inteiramente casualizado. Os substratos apresentaram diferenças quanto aos atributos físicos: umidade, granulometria, densidade volumétrica, capacidade de retenção de água, densidade de partícula e porosidade, características com as quais se pode inferir sobre sua utilização. Quanto aos parâmetros físicos recomendados em cada um dos métodos, sugere-se: para determinação da umidade da amostra, secagem à temperatura de 65 ± 5 °C; para granulometria, tempo de 10 min de agitação das peneiras; para densidade volumétrica, método da autocompactação; para capacidade de retenção de água, a base do anel volumétrico da amostra como referência e a adoção da dupla saturação e da pré-tensão em 50 hPa; para determinar a curva de retenção de água dos substratos, os pontos de tensão em 10, 30, 50 e 100 hPa; para caracterização dos substratos quanto à sua origem, inclusão da densidade de partícula pela determinação da matéria orgânica e de cinzas.

Physical characterization of substrates in Brazil is still incipient and, only with the proper definition of reliable methods, substrates can be standard izedand recommendations can be provided for the producer. The objective was to physically characterize six types of substrates for plants and to compare the methods prescribed by Ministry of Agriculture, Livestock and Supply and by European Committee for Standardization. The six substrates evaluated were: granulated coconut fiber, two types of pinus bark, stabilized rice rusk and two mixtures (50:50, v:v), one of rice rusk with granulated coconut fiber and the second pinus bark with rice husk. The project, realized in 2009, has a completely randomized experimental design. The substrates showed differences as to the physical attributes studied: humidity, particle size distribution, bulk density, water retention capacity, particle density and porosity, characteristics with which one can infer on their use. For the physical parameters for each of the recommended methods are suggested: for the determination of the sample humidity, drying at 65 °C ± 5 °C; for particle size distribution, time of 10 minutes of stirring sieves; for the bulk density, the method of self-compaction; for the water retention capacity, the base of sample ring as reference and the adoption of the double saturation and the pre-tension of 50 hPa; for the determination of water retention curve of substrates, the tension points of 10, 30, 50 and 100 hPa; for the characterization of substrates according to its origin, the inclusion of particle density by the determination of the organic matter and ash.