Regression tree analysis to predict body weight of South African non-descript goats raised at Syferkuil farm, Capricorn District of South Africa

Regression tree is the data mining algorithm method which contains a series of calculations that creates a model from collected data. Present study aimed to develop model to estimate body weight (BW) from biometric traits viz. withers height (WH), sternum height (SH), body length (BL), heart girth (HG) and rump height (RH). A total of eighty-three (n = 83) South African non-descript indigenous goats ( 54 females and 29 males) aged three months and above were used in the study. Pearson?s correlations and classification and regression tree (CART) as statistical techniques were used for data analysis. Correlation results indicated that there was a positive highly statistical significant (P < 0.01) correlation between BW and all biometric traits in both males and females, the positive highly statistical significant correlation was observed between BW and WH (r = 0.82) in female goats while in males the highest positive statistical significant correlation was detected between BW and BL (r = 0.83). CART model indicated that the BW mean was 29.868 kilograms (kg) as dependent variable and BL had the highest remarkable role in BW followed by SH, RH while the age had the least remarkable role in BW. This study suggests that BL, SH and RH might be used by South African non-descript goats? farmers as a selection criterion during breeding to improve BW of animal. More completive studies and experiments need to be done using CART to predict BW in more sample size of South African nondescript goats or other goat breeds.

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Forecasting Site-Specific Leaf Wetness Duration for Input to Disease-Warning Systems

Plant Disease ◽

10.1094/pd-90-0650 ◽

2006 ◽

Vol 90 (5) ◽

pp. 650-656 ◽

Cited By ~ 18

Author(s):

K. S. Kim ◽

M. L. Gleason ◽

S. E. Taylor

Keyword(s):

Regression Tree ◽

Empirical Models ◽

Classification And Regression Tree ◽

Weather Data ◽

Leaf Wetness ◽

Warning Systems ◽

Site Specific ◽

Leaf Wetness Duration ◽

Regression Tree Analysis ◽

Cart Model

Empirical models based on classification and regression tree analysis (CART model) or fuzzy logic (FL model) were used to forecast leaf wetness duration (LWD) 24 h into the future, using site-specific weather data estimates as inputs. Forecasted LWD and air temperature then were used as inputs to simulate performance of the Melcast and TOM-CAST disease-warning systems. Overall, the CART and FL models underpredicted LWD with a mean error (ME) of 2.3 and 3.9 h day-1, respectively. The CFL model, a corrected version of the FL model using a weight value, reduced ME in LWD forecasts to -1.1 h day-1. In the Melcast and TOM-CAST simulations, the CART and CFL models predicted timing of occurrence of action thresholds similarly to thresholds derived from on-site weather data measurements. Both models forecasted the exact spray dates for approximately 45% of advisories derived from measurements. When hindcast and forecast estimates derived from site-specific estimates provided by SkyBit Inc. were used as inputs, the CART and CFL models forecasted spray advisories within 3 days for approximately 70% of simulation periods for the Melcast and TOM-CAST disease-warning systems. The results demonstrate that these models substantially enhance the accuracy of commercial site-specific LWD estimates and, therefore, can enhance performance of disease-warning systems using LWD as an input.

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