Skip to main content

Table 9 Kharif cotton yield forecast in 2023 at the F1 stage using SMLR

From: Comparative analysis of machine learning and statistical models for cotton yield prediction in major growing districts of Karnataka, India

District

Equation

R2

F-value

SE

Forecast

yield (kg·hm–2)

Ballari

Y=–48.35 + 6.764*Time + 0.317*Z51 + 0.013*Z351

0.79

33.31

71.44

316

Belagavi

Y=–2.66 + 0.05* Z131 + 0.08*Z151

0.52

15.53

83.61

329

Chitradurga

Y = 239.17–40.53*Z21–0.66*Z50 + 0.39*Z141 + 0.07*Z451

0.74

17.81

52.13

181

Dharwad

Y = 35.24 + 3.18*Time + 0.04*Z131

0.78

50.01

44.16

262

Haveri

Y=–4.91 + 0.05*Z131 + 0.007*Z150

0.75

30.57

66.62

446

Kalaburagi

Y = 18.39 + 15.22*Time–1.64*Z41 + 0.04*Z451

0.76

27.87

112.16

734

Koppal

Y = + 3.94*Z20 + 101.24*Z21 + 0.02*Z341 + 0.05*Z451

0.77

15.14

88.56

682

Mysuru

Y = 16.23–2.69*Time + 0.02*Z151–0.01*Z230 + 0.02*Z341

0.84

27.98

44.51

166

Raichur

Y=–15.44 + 10.31*Time–0.07*Z131 + 0.04*Z341

0.87

59.88

58.18

423

Vijayapura

Y=–9.26 + 11.02*Time–0.24*Z131 + 0.09*Z351

0.76

28.90

62.14

404

  1. The weather parameters in the formula are shown in Table 2
  2. SE standard error