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Table 10 Kharif cotton yield forecast in 2023 at the F2 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=–28.86 + 8.90*Time + 0.30*Z51 + 0.10*Z241

0.80

36.12

69.17

404

Belagavi

Y=–7.75 + 0.41*Z10 + 0.05*Z131

0.71

14.90

84.51

334

Chitradurga

Y=–1.10–0.02*Z150 + 0.06*Z451

0.80

24.48

58.69

208

Dharwad

Y = 31.94 + 3.36*Time + 0.04*Z131

0.89

57.08

41.91

267

Haveri

Y=–10.09 + 0.02*Z351 + 0.12*Z151

0.78

35.57

62.86

420

Kalaburagi

Y = 2.37 + 7.48*Time–0.82*Z40 + 0.91*Z121 + 0.05* Z131 + 0.04*Z451

0.84

25.58

95.26

897

Koppal

Y=–216.47 + 2.45*Z20 + 33.39*Z21 + 0.02*Z341 + 0.03*Z451

0.78

16.85

84.93

478

Mysuru

Y=–59.17 + 4.88*Z21 + 2.78*Z41–0.10*Z140 + 0.34* Z141 + 0.04*Z341–0.04*Z351

0.91

32.83

34.96

200

Raichur

Y=–21.01 + 10.42*Time–2.92*Z31 + 0.04*Z341

0.93

60.21

58.05

424

Vijayapura

Y = 2.52 + 10.65*Time–0.22*Z131 + 0.09*Z351

0.87

29.29

61.81

406

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