Productivity and penalty in sugarcane from three meteorological databases in Jataí-GO

Abstract

The agroecological zone model proposed by FAO (ZAE/FAO) is an important tool in agricultural planning and can be used in Jataí, which has been gaining space in the production of sugarcane. The objective of this study was to compare the observed meteorological databases (BDMEP) with the estimated ones (Xavier and NASA POWER), in the estimation of potential productivity (PP) and attainable (PA) of sugarcane and its penalties for management (PMJ) and water deficit (PDF), using the ZAE/FAO model in the municipality of Jataí - GO. To this end, the BDMEP, Xavier and NASA POWER database, 33 years old (1984-2017), were used. Using BDMEP, Xavier and NASA POWER as input in the ZAE model, we observed an average PP of 259±11, 275±10 and 267±11 Mg ha-1 as, respectively. PA reaches 200 Mg ha-1 when using BDMEP in ZAE, with 50% of the crop year with PA above 180 Mg ha-1. There was an average penalty of 114.01±6.9 for management and 85.67±4.08 Mg ha-1 in stem yield. The meteorological variables of Xavier's database can be used in the ZAE model in the prediction of productivity penalty for water deficit and sugarcane management, with 63% and 88% adjustment to BDMEP, respectively. NASA POWER can be used in the simulation of penalty for failures in sugarcane management, with up to 87% approximation of those observed. Both sources when used in the prediction model assist in agricultural planning.

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

“…DSSAT and APSIM were globally used, while FAO-AZM was used exclusively in Brazil. An explanation cited by the authors is that FAO-AZM does not demand so much input data and has a simpler methodology, and is suited for supporting decision making in countries with continental size, limited data coverage (e.g., agrometeorological), and low-scale mapping (e.g., soils), such as Brazil [58,61,[72][73][74][75][76]. The spatial distribution of research groups accounting for the 72 selected papers shows a predominance of tropical regions (Figure 4), especially Brazil, India, and Australia, with 28, 12, and 7 papers, respectively.…”

“…Regarding the models' names, FAO-AZM, DSSAT, and APSIM were very commonly used keywords, and this can be explained by the fact that a great number of the selected models that used mechanistic approaches made use of them. For example, in relation to the use of these models, we can cite the selected studies of [74,85], which, respectively, used FAO-AZM and DSSAT.…”

“…Ref. [74] compared the potential and attainable productivity estimated for sugarcane in a Brazilian municipality using three different meteorological datasets ((i) Xavier; (ii) NASA/POWER, and (iii) a meteorological station) as input for the FAO-AZM model. In conclusion, the authors recommend the use of the Xavier database in the prediction of productivity penalty for water deficit and the management of the studied crop, with an adjustment range varying between 63% and 88% with the meteorological station.…”