Nonlinear regression and plot size to estimate green beans production

Abstract

The objectives of this work were to adjust nonlinear regression models for the green beans production and to identify the plot size which provides the best explanation and adjustment to the models. The authors used two field and two protected environment (plastic tunnel) trials in the autumn-winter and spring-summer seasons. The logistic and von Bertalanffy models were adjusted for average weight of green beans accumulated after multiple harvests and with different plot sizes. The models presented similar estimates and the same parameters estimates in all the plot sizes. The logistic model provided estimates closest to the reality, showing the best description performance for the average weight of pods during the productive cycle. In the autumn-winter season, plot size of 14 basic units (28 plants in the cultivation line direction) in the field and of two basic units (four plants in the cultivation line direction) under the plastic tunnel provide a good quality in the models adjustment. In the springsummer season, the plot sizes are made of six basic units (12 plants in the cultivation line) in the field and seven basic units (14 plants in the cultivation line) under the plastic tunnel.

An approach for experiment evaluations for multiple harvests crops based on non-linear regression

“…presence of zero values causing the database to be over dispersed (Carpes et al, 2010;Lucio et al, 2016;Sari et al, 2019b).…”

“…By interpreting the estimates of the parameters of the models, it is possible to estimate the total production, the rate of fruit production, and the moment when the crop reaches its maximum production potential. Also, using confidence interval estimates, it is possible to compare these estimates between genotypes or between different experimental treatments (Lucio et al, 2016;Sari et al, 2018;Diel et al, 2020a).…”

Production cycle and characterization of Italian zucchini genotypes by the logistic model

“…Examining experimental data from multiple harvest crops, the assumptions for analysis of variance are often not met, as the occurrence of observations with zero values for the number of fruits and mass of fruits inflates the residual variance and can lead to inadequate estimates due to high type II error rates till, and accumulating the values of these variables in each plant can reduce these null values and allow the use of nonlinear regression models (Lúcio et al, 2016).…”

“…Nonlinear regression models have been used in several analyses of vegetables, such as Allium sativum (Reis et al, 2014), Phaseolus vulgaris (Lucio et al, 2016), Fragaria x ananassa (Diel et al, 2019(Diel et al, , 2020, Lycopersicun esculentum var. cerasiforme (Lúcio et al, 2016), Capsicum chinense (Diel et al, 2020a) and Capsicum annuum (Lúcio et al, 2015). Lúcio et al (2015) modeled the zucchini culture cycle in protected cultivation.…”

Production of biquinho pepper in different growing seasons characterized by the logistic model and its critical points

“…For multiple-harvest crops, logistic regression models can efficiently describe fruit production which is the appropriate for crops such as Capsicum annuum, Cucurbita pepo, Solanum melongena, Phaseolus vulgaris and Fragaria ananassa (DIEL et al, 2019;LUCIO et al, 2016;LÚCIO;NUNES;REGO, 2015;SARI, et al, 2018;. For Fragaria ananassa, DIEL et al (2019) modeled the fruit production as a function of STa (accumulated thermal sum) for the logistic, Gompertz and von Bertalanffy models in different parameterizations and concluded that the Logisitic model described fruit production best while the models of Gompertz and von Bertalanffy overestimate the parameter that represent the production.…”