Contribution of agrometeorology to the simulation of crop production and its applications

Hoogenboom, Gerrit

doi:10.1016/s0168-1923(00)00108-8

Cited by 437 publications

(262 citation statements)

References 160 publications

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“…The average values of solar radiation for the entire growing season were 19.9, 16.7, 15.9, 15.4, 15.1, 14.9, 14.8 and 14.8 A high solar radiation value for the entire growing season contributes to the high level of crop yield (Banterng et al, 2010). Solar radiation provides the energy for the processes that drive photosynthesis, affecting carbohydrate partitioning and biomass growth of the individual plant components (Hoogenboom, 2000). Considering the transplanting date, therefore, farmers are recommended to grow rice under both rainfed and irrigated conditions on 15 Jan, 30 July, 15 Aug, 30 Aug and 15 Sept to attain acceptable grain yield.…”

Section: Simulation For Different Management Scenariosmentioning

confidence: 99%

CSM-CERES-Rice model to determine management strategies for lowland rice production

Vilayvong

Banterng

Patanothai

et al. 2015

Sci. agric. (Piracicaba, Braz.)

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The cropping system model, namely, the crop environment resource synthesis-rice (CSM-CERES-Rice) model, is a decision supporting tool for the design of crop management. This study aimed to determine management practices for increasing rice (Oryza sativa L.) production in Laos by using the CSM-CERES-Rice model. The model was evaluated with data sets from the TDK8 and TDK11 cultivars in farmers' fields in the Vientiane plain in 2012. Anthesis and harvesting dates, growth and yield for various management scenario combinations (eight transplanting dates × two levels of plant densities × three rates of nitrogen (N) fertilizer application) for both cultivars were simulated by the model from 1980 to 2012. The model evaluation results showed strong agreement between simulated and observed data for days to harvest with a difference within four days. The model provided acceptable accuracy for grain yields with normalized root mean square error values ranging between 1 and 16 %. The results from the model application indicated that TDK8 and TDK11 produced similar yields. Transplanting TDK8 with two plant densities produced similar yields. The highest yield for both cultivars was achieved on the transplanting date of 15 Jan. N-fertilizer application at 60 and 120 kg N ha −1 was able to increase yield for TDK8 by 50 and 87 %, respectively, and for TDK11 by 54 and 70 %, respectively. Rice transplanted on 15 Jan with 5 seedlings hill −1 and N-fertilizer at 120 kg N ha −1 had the highest average yield for both cultivars with 6,460 and 6,351 kg ha −1 for TDK8 and TDK11, respectively. The CSM-CERES-Rice model is an alternative tool in determining crop management practices for rice production.

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Section: Simulation For Different Management Scenariosmentioning

confidence: 99%

CSM-CERES-Rice model to determine management strategies for lowland rice production

Vilayvong

Banterng

Patanothai

et al. 2015

Sci. agric. (Piracicaba, Braz.)

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“…Crop yield forecasting is useful at the farm level for crop management and marketing decisions, and at the government level for policy issues and food security actions (Hoogenboom, 2000;Hansen, 2005).…”

Section: Introductionmentioning

confidence: 99%

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Monteiro

Assad

Sentelhas

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to develop and evaluate a method for estimating corn yield using a minimum number of parameters and limited information about crop management. The proposed method estimates potential and attainable yields based on the technological level of the production systems and on relatively simple agrometeorological models. Corn yield was estimated for the crop seasons from 2000/2001 to 2007/2008, considering several locations and regions in Brazil, and was compared with the actual yield data from official surveys. There was a high correlation between the estimated and observed yield (0.76≤R 2 <0.92; p<0.01), with model efficiency (E1') ranging from 0.45 to 0.73; mean relative error (MRE) between -0.9 and 2.4%; and mean absolute error (MAE) of less than 70 kg ha -1 , depending on the technological level adopted. Based on these results, the proposed yield model can be recommended to forecast yields all over the country, contributing to make this process more precise and accurate.Index terms: Zea mays, large-area crop modeling, paremetrization, risk analysis, technological potential yield, yield forecast. Modelagem da produtividade de milho no Brasil em função das condições meteorológicas e do nível tecnológicoResumo -O objetivo deste trabalho foi desenvolver e avaliar um método para estimar a produtividade de milho com uso de um número mínimo de parâmetros e de informações limitadas sobre o manejo da cultura. O método proposto estima rendimentos potenciais e atingíveis com base no nível tecnológico dos sistemas de produção e em modelos agrometeorológicos relativamente simples. A produtividade de milho foi estimada para as safras de 2000/2001 a 2007/2008, tendo-se considerado vários locais e regiões do Brasil, e comparada aos dados de produção reais de levantamentos oficiais. A produtividade estimada apresentou alta correlação com a observada (0,76≤R 2 <0,92; p<0,01), com eficiência do modelo (E1') entre 0,45 e 0,73; erro médio relativo (MRE) entre -0,9 e 2,4%; e erro médio absoluto (MAE) inferior a 70 kg ha -1 , de acordo com o nível tecnológico considerado. Com base nestes resultados, este modelo pode ser recomendado para estimativas de produtividade em todo o País, e contribuir para tornar este processo mais preciso e exato.Termos para indexação: Zea mays, modelagem de cultura em grandes áreas, parametrização, análise de risco, produtividade potencial tecnológica, previsão de safra.

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“…At present, a high level of agreement among the scientific community has been achieved in terms of the development and application of crop models [39]. However, due to the complexity of the soil-plant-atmosphere system, many of the relationships incorporated in the models are partially or entirely unable to reflect extreme weather impacts properly, e.g., heat or drought [40,41]. It is extremely difficult to analyze the uncertainties in a process-based model [42], and this difficulty is not simply a consequence of multiple sources of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

et al. 2014

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Abstract:The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under-or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously OPEN ACCESSAtmosphere 2014, 5 1021 associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical ("event scenario") approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

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Contribution of agrometeorology to the simulation of crop production and its applications

Cited by 437 publications

References 160 publications

CSM-CERES-Rice model to determine management strategies for lowland rice production

CSM-CERES-Rice model to determine management strategies for lowland rice production

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

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