Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes.

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769 kg ha-1) and water productivity (1,050 to 1,445 kg m-3) with minimal application depths (< 150 mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Climate risk zoning for wheat crops in the southeastern region of Brazil.

BACKGROUND: Wheat (Triticum aestivum L.) is the second most consumed food in the world. One way to meet this demand is the expansion of wheat cultivation to the Brazilian Cerrado in the southeastern region. However, one of the major limitations is that there are few studies related to wheat climate risk zoning. Thus, this study aimed to determine the agroclimatic zoning of wheat by estimating the water needs satisfaction index (ISNA) in the southeastern region of Brazil. For this purpose, a 60-year historical series of meteorological data was used to calculate the potential evapotranspiration, crop evapotranspiration, and climatological water balance values. To define the agroclimatic zones of wheat and sowing date, the ISNA method was used. The data were analyzed using descriptive statistics to determine the variations. To obtain the agroclimatic zoning of wheat, the geostatistical method of kriging interpolation was used. RESULTS: The regions with the highest rainfall are the south of Minas Gerais and the coast of São Paulo. The sowing period directly impacts the development of the crop, the available water capacity and the ISNA values indicated the spring and summer had better cultivation conditions, and the best window for wheat cultivation is concentrated in the fall due to the limitation of biotic factors. CONCLUSION: In terms of altitude (>700 m), Minas Gerais has 39.4% of the area suitable for wheat cultivation. Thus, climatic variations within and between the states of the southeastern region should be considered for the positioning of wheat cultivars in these regions to obtain the maximum yield. © 2023 Society of Chemical Industry.

Agroclimatic zoning for bananas under climate change in Brazil.

BACKGROUND: Climate change is the main cause of biotic and abiotic stresses in plants and affects yield. Therefore, we sought to carry out a study on future changes in the agroclimatic conditions of banana cultivation in Brazil. The current agroclimatic zoning was carried out with data obtained from the National Institute of Meteorology related to mean air temperature, annual rainfall, and soil texture data in Brazil. The global climate model BCC-CSM1.1 (Beijing Climate Center-Climate System Model, version 1.1), adopted by the Intergovernmental Panel on Climate Change, corresponding to Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0, and 8.5 for the period 2050 (2041-2060) and 2070 (2061-2080), obtained through the CHELSA V1.2 platform, was chosen for the climate projections of the Coupled Model Intercomparison Project 5. Matrix images at a depth of 5-15 cm, obtained through the product of the SoilGrids system, were used for the texture data. ArcGIS version 10.8 was used to construct the maps. RESULTS: Areas favorable to the crop plantation were classified as suitable when air temperature TAIR was between 20 and 29 °C, annual rainfall RANNUAL between 1200 and 1900 mm, and soil clay content CSOIL between 30 and 55%. Subsequently, the information was reclassified, summarizing the classes into preferential, recommended, little recommended, and not recommended. The current scenario shows a preferential class of 8.1%, recommended of 44.6%, little recommended of 47.1%, and not recommended of 0.1% for the Brazilian territory. CONCLUSION: The results show no drastic changes in the total area regarding the classes, but there is a migration from these zones; that is, from tropical to subtropical and temperate regions. RCP 8.5-2070 (2061-2080) showed trends with negative impacts on arable areas for banana cultivation at the end of the century. © 2022 Society of Chemical Industry.

Coffee pest severity by agrometeorological models in subtropical climate.

This study aimed to estimate the number of generations and cycle duration of the southern red mite, coffee berry borer, and coffee leaf miner using the thermal index to assist in controlling these main coffee pests in the state of Paraná, Brazil. The data of maximum and minimum air temperature (°C) and precipitation (mm) of all municipalities in the state from 1984 to 2018 were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER). The reference evapotranspiration was estimated using the (Camargo Campinas IAC Boletim 116:9, 1971) method and the water balance was calculated using the method of ( Thornthwaite C, Mather J (1955) The water balance publications in climatology, 8 (1). DIT, Laboratory of climatology, Centerton, NJ, USA). The basal temperature of each pest minus the average temperature of the years was used to calculate the degrees-day, the duration of the pest cycle, and the number of generations per year. The influence of altitude on the development of coffee pests was measured using the Pearson correlation. The thermal index is able to estimate the damage caused by coffee pests in the state of Pará, Brazil. Coffee pests show greater severity in the north of Paraná, in the regions with the highest temperatures. It is the same region that concentrates most of the coffee production of the state. The results of the life cycle and number of generations were interpolated for the entire state using the kriging method. Coffee pests showed the highest severity in the north region of the state of Paraná, more specifically in the Northwest, North Central, and West Central mesoregions. These regions have concentrated most of the state's coffee production. Mesoregions with the highest coffee production in the state showed higher susceptibility to coffee pests. Altitude showed a high correlation (r > 0.6) with the cycle variability and number of generations of coffee pests. The average cycles of the coffee berry borer, coffee leaf miner, and southern red mite are 24.13 (± 8.34), 45.64 (± 18.61), and 21.51 (± 3.51) days, respectively. The average annual generation was 16.67 (± 4.77), 9.02 (± 2.75), and 17.32 (± 2.63) generations, for the coffee berry borer, the coffee red mite, and the southern red mite, respectively.

Can nonlinear agrometeorological models estimate coffee foliation?

BACKGROUND: The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25-year historical series (1995-2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987-2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four-parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees-day for 70 locations. RESULTS: The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error = 2.19%) and precise (R2 adj  = 0.99) and can be used to assist decision-making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax  = 97.63%, ymin  = 9%, Xo  = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION: Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of -0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry.

Climate risk to peanut cultivation in Brazil across different planting seasons.

BACKGROUND: Peanuts are widely grown in Brazil because of their great importance in the domestic vegetable oil industry and the succession of sugarcane, soybean and maize crops, contributing to soil conservation and improvement in agricultural areas. Thus, the present study aimed to determine the zoning of peanuts' climatic risk by estimating the water requirement satisfaction index (WRSI) for the crop in Brazil. We used a historical series of data on average air temperature and rainfall between 1980 and 2016. Reference evapotranspiration was estimated using the method of Thornthwaite, and we subsequently calculated crop evapotranspiration and maximum evapotranspiration. Water balances for all stations were calculated using the method of Thornthwaite and Mather, with an available water capacity in the soil of 15, 30 and 45 mm. The definitions of suitable, unfit and restricted areas and the planting season were performed using the WRSI. RESULTS: Brazil has low climatic risk areas for growing peanuts throughout the year, except for winter. The country reveals that 88.19%, 97.93%, 99.16% and 39.25% of its area is suitable for planting peanuts on planting dates in spring, summer, autumn and winter, respectively. CONCLUSION: Brazil has a large part of the areas favorable to the planting of peanuts. The maximum availability of soil water at a depth of 15, 30 and 45 mm does not influence regions with respect to peanut growing in Brazil. The states of Piauí, Ceará and Bahia are the most unsuitable on the winter planting date, with an average WRSI of 0.22. © 2021 Society of Chemical Industry.

Radiation Interception, Conversion and Partitioning Efficiency in Potato Landraces: How Far Are We from the Optimum?

Crop efficiencies associated with intercepted radiation, conversion into biomass and allocation to edible organs are essential for yield improvement strategies that would enhance genetic properties to maximize carbon gain without increasing crop inputs. The production of 20 potato landraces-never studied before-was analyzed for radiation interception ( ε i ), conversion ( ε c ) and partitioning ( ε p ) efficiencies. Additionally, other physiological traits related to senescence delay (normalized difference vegetation index (NDVI) s l p ), tuberization precocity ( t u ), photosynthetic performance and dry tuber yield per plant (TY) were also assessed. Vegetation reflectance was remotely acquired and the efficiencies estimated through a process-based model parameterized by a time-series of airborne imageries. The combination of ε i and ε c , closely associated with an early tuber maturity and a NDVI s l p explained 39% of the variability grouping the most productive genotypes. TY was closely correlated to senescence delay (r P e a r s o n = 0.74), indicating the usefulness of remote sensing methods for potato yield diversity characterization. About 89% of TY was explained by the first three principal components, associated mainly to t u , ε c and ε i , respectively. When comparing potato with other major crops, its ε p is very close to the theoretical maximum. These findings suggest that there is room for improving ε i and ε c to enhance potato production.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SOMG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1-10 d (from 1 to 10 days before the incidence evaluation), 11-20 d, and 21-30 d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R2) indices. The result of the more accurate algorithm was specialized for the SOMG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SOMG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SOMG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting.

A potato model intercomparison across varying climates and productivity levels.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

Estimativa do volume máximo de calda para aplicação foliar de produtos químicos na cultura de milho

With the objective of estimating the volume of water retained by corn leaves per unit area (ha) for chemigation purposes, a simple model is proposed. A field experiment was carried out to obtain the water interception on the aerial corn plant parts under a center pivot irrigation system. Three plant populations were used to obtain different designs, the measurements having been performed at three phenological stages: i) 4-5 leaves; ii) 8 leaves and iii) 14 leaves. The water mass retained by the corn leaves was obtained considering the total plant mass (measured after irrigation), the plant dry weight and the plant constitution water mass. Treatments were arranged in a split-plot design, with four replications. Main plot treatment was plant population and subplot treatment was sampling date. Total plant leaf area was evaluated for each sampling period. Phenology and the relative crop development were evaluated periodically. Results indicate that the proposed model can be used for this estimate.

Com o objetivo de estimar o volume de água por hectare retido na folhagem da cultura de milho para fins de quimigação (alvo folha), é proposto um modelo, tendo sido conduzido um experimento em que foi avaliada a interceptação de água pela parte aérea das plantas de milho sob diferentes lâminas de irrigação aplicadas via pivô central. Foi semeado um híbrido de milho, em três densidades, obtendo-se assim três arranjos espaciais diferentes. As avaliações foram realizadas em 3 estádios fenológicos: i) 4-5 folhas; ii) 8 folhas e iii) 14 folhas. A quantidade de água retida nas folhas das plantas foi estimada subtraindo à massa total da planta medida após a passagem do pivô, a massa de matéria seca e a água constitutiva das plantas. Para a análise estatística dos resultados, utilizou-se um delineamento experimental em parcelas subdivididas no tempo, com quatro repetições. O tratamento principal foi a população de plantas e o subtratamento a data de amostragem. A área foliar foi avaliada em cada amostragem. A fenologia e o desenvolvimento relativo da cultura foram avaliados periodicamente. Os resultados permitiram verificar que o modelo proposto é utilizável.

Estimativa do volume máximo de calda para aplicação foliar de produtos químicos na cultura de milho

With the objective of estimating the volume of water retained by corn leaves per unit area (ha) for chemigation purposes, a simple model is proposed. A field experiment was carried out to obtain the water interception on the aerial corn plant parts under a center pivot irrigation system. Three plant populations were used to obtain different designs, the measurements having been performed at three phenological stages: i) 4-5 leaves; ii) 8 leaves and iii) 14 leaves. The water mass retained by the corn leaves was obtained considering the total plant mass (measured after irrigation), the plant dry weight and the plant constitution water mass. Treatments were arranged in a split-plot design, with four replications. Main plot treatment was plant population and subplot treatment was sampling date. Total plant leaf area was evaluated for each sampling period. Phenology and the relative crop development were evaluated periodically. Results indicate that the proposed model can be used for this estimate.

Com o objetivo de estimar o volume de água por hectare retido na folhagem da cultura de milho para fins de quimigação (alvo folha), é proposto um modelo, tendo sido conduzido um experimento em que foi avaliada a interceptação de água pela parte aérea das plantas de milho sob diferentes lâminas de irrigação aplicadas via pivô central. Foi semeado um híbrido de milho, em três densidades, obtendo-se assim três arranjos espaciais diferentes. As avaliações foram realizadas em 3 estádios fenológicos: i) 4-5 folhas; ii) 8 folhas e iii) 14 folhas. A quantidade de água retida nas folhas das plantas foi estimada subtraindo à massa total da planta medida após a passagem do pivô, a massa de matéria seca e a água constitutiva das plantas. Para a análise estatística dos resultados, utilizou-se um delineamento experimental em parcelas subdivididas no tempo, com quatro repetições. O tratamento principal foi a população de plantas e o subtratamento a data de amostragem. A área foliar foi avaliada em cada amostragem. A fenologia e o desenvolvimento relativo da cultura foram avaliados periodicamente. Os resultados permitiram verificar que o modelo proposto é utilizável.

Principles of crop modelling and simulation: III. modeling of root growth and other belowground processes, limitations of the models, and the future of modeling in agriculture

The first models of temporal variation of root systems appeared over 20 years ago. The complex architectural geometry of root systems; the wide range in size and diameter and the rapid growth and decomposition of finest roots; the different physiological activity of roots of different ages; the complex microbial processes occurring at the root-soil interface; the symbiotic relationships in the rhizosphere; the variable soil environment (physical, chemical and biological) in which roots develop are the challenges of quantifying the root growth. The models are not simple mechanisms to archive information in order to produce forecasts. Modeling represents a better way of synthesizing knowledge about different components of a system, summarizing data, and transferring research results to users.

Os primeiros modelos que contemplaram a variação espacial e temporal de sistemas radiculares foram apresentados há mais de 20 anos. A complexa arquitetura dos sistemas radiculares; a ampla faixa de variação no tamanho e no diâmetro, bem como no rápido crescimento e decomposição de raízes finas; as diferentes atividades fisiológicas das raízes de diferentes idades; os complexos processos microbiológicos que ocorrem na interface raiz-solo; as relações simbióticas na rizosfera; as variações químicas, físicas e biológicas que ocorrem no solo (local onde as raízes se desenvolvem) são os desafios da quantificação do crescimento radicular. Os modelos não são simples mecanismos para arquivar informações, produzindo estimativas. Os modelos representam o melhor mecanismo de sintetizar conhecimento sobre diferentes componentes do sistema, arquivando dados de forma sintética, e transferindo resultados de pesquisa para os usuários.

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

With the purpose of presenting to scientists the implications of the objective in model development and a basic vision of modeling, with its potential applications and limitations in agriculture, an integration of crop modeling professionals with agricultural professionals is suggested. Models mean modernization of the information, of the measurement process and of an efficient way to learn more about complex systems. They are one of the best mechanisms of transforming information in useful knowledge and of transferring this knowledge to others. One of the problems that impede a larger progress in modeling is the lack of communication between modelers and a frequent appearance of modelers without a global vision of reality.

Com o propósito de apresentar aos pesquisadores a implicação do objetivo de desenvolvimento de modelos e uma visão da potencialidade do uso da modelagem em agricultura, com as respectivas limitações, é sugerida uma integração dos profissionais que trabalham com modelagem com os que trabalham com agricultura. Os modelos não são simples mecanismos para arquivar e sintetizar informações, produzindo estimativas. O modelo representa a modernização da informação, do processo de mensuração e de um eficiente meio de aprender mais sobre sistemas complexos. Os modelos representam o melhor mecanismo de transformar conhecimento em informação útil e transferi-lo para terceiros. Um dos problemas que impedem um maior avanço na área de modelagem é a falta de entrosamento entre modeladores e a freqüente existência de modeladores sem uma visão global da realidade.

Principles of crop modeling and simulation: I. uses of mathematical models in agricultural science

Modeling techniques applied to agriculture can be useful to define research priorities and understanding the basic interactions of the soil-plant-atmosphere system. Using a model to estimate the importance and the effect of certain parameters, a researcher can notice which factors can be most useful. The modeler should define his objectives before beginning his work and construct a model that fulfills the proposed objectives.

A técnica de modelagem em agricultura pode ser útil para definir as prioridades de pesquisa, bem como para melhor entender as interações que ocorrem no sistema solo-planta-atmosfera. O modelo pode ser utilizado para estimar a importância e o efeito de certos parâmetros no intuito de definir os fatores a serem considerados. O modelador deve definir seus objetivos antes de iniciar o trabalho experimental, bem como desenvolver um modelo que atenda o objetivo proposto.

Principles of crop modeling and simulation: I. uses of mathematical models in agricultural science

Modeling techniques applied to agriculture can be useful to define research priorities and understanding the basic interactions of the soil-plant-atmosphere system. Using a model to estimate the importance and the effect of certain parameters, a researcher can notice which factors can be most useful. The modeler should define his objectives before beginning his work and construct a model that fulfills the proposed objectives.

A técnica de modelagem em agricultura pode ser útil para definir as prioridades de pesquisa, bem como para melhor entender as interações que ocorrem no sistema solo-planta-atmosfera. O modelo pode ser utilizado para estimar a importância e o efeito de certos parâmetros no intuito de definir os fatores a serem considerados. O modelador deve definir seus objetivos antes de iniciar o trabalho experimental, bem como desenvolver um modelo que atenda o objetivo proposto.

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

With the purpose of presenting to scientists the implications of the objective in model development and a basic vision of modeling, with its potential applications and limitations in agriculture, an integration of crop modeling professionals with agricultural professionals is suggested. Models mean modernization of the information, of the measurement process and of an efficient way to learn more about complex systems. They are one of the best mechanisms of transforming information in useful knowledge and of transferring this knowledge to others. One of the problems that impede a larger progress in modeling is the lack of communication between modelers and a frequent appearance of modelers without a global vision of reality.

Com o propósito de apresentar aos pesquisadores a implicação do objetivo de desenvolvimento de modelos e uma visão da potencialidade do uso da modelagem em agricultura, com as respectivas limitações, é sugerida uma integração dos profissionais que trabalham com modelagem com os que trabalham com agricultura. Os modelos não são simples mecanismos para arquivar e sintetizar informações, produzindo estimativas. O modelo representa a modernização da informação, do processo de mensuração e de um eficiente meio de aprender mais sobre sistemas complexos. Os modelos representam o melhor mecanismo de transformar conhecimento em informação útil e transferi-lo para terceiros. Um dos problemas que impedem um maior avanço na área de modelagem é a falta de entrosamento entre modeladores e a freqüente existência de modeladores sem uma visão global da realidade.

Principles of crop modelling and simulation: III. modeling of root growth and other belowground processes, limitations of the models, and the future of modeling in agriculture

The first models of temporal variation of root systems appeared over 20 years ago. The complex architectural geometry of root systems; the wide range in size and diameter and the rapid growth and decomposition of finest roots; the different physiological activity of roots of different ages; the complex microbial processes occurring at the root-soil interface; the symbiotic relationships in the rhizosphere; the variable soil environment (physical, chemical and biological) in which roots develop are the challenges of quantifying the root growth. The models are not simple mechanisms to archive information in order to produce forecasts. Modeling represents a better way of synthesizing knowledge about different components of a system, summarizing data, and transferring research results to users.

Os primeiros modelos que contemplaram a variação espacial e temporal de sistemas radiculares foram apresentados há mais de 20 anos. A complexa arquitetura dos sistemas radiculares; a ampla faixa de variação no tamanho e no diâmetro, bem como no rápido crescimento e decomposição de raízes finas; as diferentes atividades fisiológicas das raízes de diferentes idades; os complexos processos microbiológicos que ocorrem na interface raiz-solo; as relações simbióticas na rizosfera; as variações químicas, físicas e biológicas que ocorrem no solo (local onde as raízes se desenvolvem) são os desafios da quantificação do crescimento radicular. Os modelos não são simples mecanismos para arquivar informações, produzindo estimativas. Os modelos representam o melhor mecanismo de sintetizar conhecimento sobre diferentes componentes do sistema, arquivando dados de forma sintética, e transferindo resultados de pesquisa para os usuários.