Abstract
The temporal variation of cumulative dry matter can be represented by a sigmoidal curve and the temporal variation of nutrient uptake follows this characteristic shape. Therefore, modeling the temporal variation of cumulative dry matter allows estimating nutrient uptake along the crop cycle. The objective of this study was to propose a sine model to estimate dry matter and macronutrient uptake, and to estimate the moments of maximum N and K uptake rates for the rice crop. The field experiment was carried out on a wetland area of Piracicaba, SP, Brazil, consisting of an Humic Haplaquept. The chosen rice variety was IAC 103, a middle season cycle and high yield. Observed variables were dry matter of different plant parts (root, leaf and stem, and panicle) and macronutrient contents per unit dry matter. A sine model was proposed for the cumulative variation of these variables, based on biological events that occurred during the crop cycle. The temporal variation of nutrient uptake was estimated and a lower accuracy was observed for K uptake. The maximum absorption rate for N and K was found at 56% of the relative development of the crop, corresponding to 60 days after emergence in this experiment. The proposed model presented a satisfactory behavior to define the order of magnitude of estimated dry matter and macronutrient uptake by the rice crop and maximum N and K uptake rates.
Highlights
An understanding of the processes involving crop growth and development, in addition to the adoption of adequate decision-making tools, is essential for better results to be obtained in the production process, and modeling is one tool utilized for the integration of the processes that take place along the crop cycle
The maximum absorption rate for N and K was found at 56% of the relative development of the crop, corresponding to 60 days after emergence in this experiment
Dry phytomass and macronutrient accumulation The temporal variation of the total dry phytomass accumulation corresponds to the sum of dry phytomass in the root and aerial part of the plant (Figure 2), which
Summary
An understanding of the processes involving crop growth and development, in addition to the adoption of adequate decision-making tools, is essential for better results to be obtained in the production process, and modeling is one tool utilized for the integration of the processes that take place along the crop cycle. Simulation models are utilized to verify theories and test hypotheses, improve the knowledge on a given process, feeding databases with the acquired information and allowing grain yield estimates to be obtained (Munakata, 1995; Boote et al, 1996)
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