Soybean plastochron
The plastochron of soybean represents the time interval between leaf initiation. Understanding its influence on growth is key to optimizing crop management, improving yield prediction, and adapting practices to environmental conditions for better productivity.
The plast() function estimates the air temperature
required for leaf expansion and node emission in soybean crops, as
described by Porta et al (2024).
#Predict ∆T to determine the ideal times to apply agricultural pesticides.
Delta T, the difference between air temperature and dew point, is
crucial for agrochemical application. The tdelta() function
performs forecasting or retrospective analysis of climate data to
understand the best time for application.
library(EstimateBreed)
# Forecasting application conditions
forecast <- tdelta(-53.6969,-28.0638,type=1,days=10)
forecast
#> Day Hour Temp RH WindS Prec DELTAT
#> 1 2025-03-19 21:00 19.2 64 8.9 0.0 6.9800393
#> 2 2025-03-19 22:00 18.5 64 8.5 0.0 6.9425667
#> 3 2025-03-19 23:00 17.9 65 7.3 0.0 6.6767223
#> 4 2025-03-20 00:00 17.3 68 6.9 0.0 5.9660194
#> 5 2025-03-20 01:00 16.2 73 5.8 0.0 4.8477829
#> 6 2025-03-20 02:00 15.5 77 5.8 0.0 4.0170528
#> 7 2025-03-20 03:00 15.0 79 5.4 0.0 3.6143743
#> 8 2025-03-20 04:00 14.7 81 5.8 0.0 3.2283040
#> 9 2025-03-20 05:00 17.9 71 6.5 0.0 5.3368443
#> 10 2025-03-20 06:00 21.3 58 12.2 0.0 8.6056142
#> 11 2025-03-20 07:00 24.6 50 11.9 0.0 11.1244757
#> 12 2025-03-20 08:00 27.3 41 9.7 0.0 14.4223437
#> 13 2025-03-20 09:00 29.2 34 5.2 0.0 17.4936107
#> 14 2025-03-20 10:00 30.3 30 1.5 0.0 19.5300696
#> 15 2025-03-20 11:00 30.6 28 0.8 0.0 20.6076487
#> 16 2025-03-20 12:00 31.1 28 3.2 0.0 20.6815682
#> 17 2025-03-20 13:00 31.0 28 6.6 0.0 20.6667745
#> 18 2025-03-20 14:00 30.4 28 9.0 0.0 20.5781151
#> 19 2025-03-20 15:00 28.9 30 10.0 0.0 19.3339171
#> 20 2025-03-20 16:00 26.2 37 9.2 0.0 15.8542689
#> 21 2025-03-20 17:00 24.3 40 8.2 0.0 14.4754271
#> 22 2025-03-20 18:00 22.9 41 7.3 0.0 13.9600039
#> 23 2025-03-20 19:00 21.5 47 7.9 0.0 11.7945839
#> 24 2025-03-20 20:00 20.8 53 8.0 0.0 9.9372351
#> 25 2025-03-20 21:00 20.5 56 8.7 0.0 9.0850349
#> 26 2025-03-20 22:00 20.0 58 8.3 0.0 8.5208637
#> 27 2025-03-20 23:00 19.5 60 8.4 0.0 7.9764776
#> 28 2025-03-21 00:00 18.8 63 7.8 0.0 7.1972764
#> 29 2025-03-21 01:00 18.1 66 7.1 0.0 6.4560231
#> 30 2025-03-21 02:00 17.4 69 6.3 0.0 5.7497272
#> 31 2025-03-21 03:00 16.7 72 6.0 0.0 5.0757573
#> 32 2025-03-21 04:00 16.4 73 5.8 0.0 4.8553502
#> 33 2025-03-21 05:00 19.4 66 5.7 0.0 6.5209736
#> 34 2025-03-21 06:00 24.1 47 8.9 0.0 12.0269008
#> 35 2025-03-21 07:00 27.4 39 12.1 0.0 15.1960831
#> 36 2025-03-21 08:00 29.5 34 11.7 0.0 17.5316674
#> 37 2025-03-21 09:00 31.0 29 10.8 0.0 20.1397635
#> 38 2025-03-21 10:00 31.4 28 11.7 0.0 20.7259784
#> 39 2025-03-21 11:00 31.5 28 11.0 0.0 20.7407916
#> 40 2025-03-21 12:00 31.7 27 9.0 0.0 21.3168740
#> 41 2025-03-21 13:00 31.5 28 7.2 0.0 20.7407916
#> 42 2025-03-21 14:00 31.0 29 5.9 0.0 20.1397635
#> 43 2025-03-21 15:00 29.7 32 5.4 0.0 18.4755241
#> 44 2025-03-21 16:00 26.5 43 3.5 0.0 13.6113064
#> 45 2025-03-21 17:00 25.7 49 1.8 0.0 11.5285780
#> 46 2025-03-21 18:00 23.4 52 4.5 0.0 10.4261346
#> 47 2025-03-21 19:00 21.7 59 4.6 0.0 8.3696337
#> 48 2025-03-21 20:00 20.8 60 4.6 0.0 8.0560517
#> 49 2025-03-21 21:00 20.2 61 4.6 0.0 7.7676052
#> 50 2025-03-21 22:00 19.3 62 5.0 0.0 7.4678992
#> 51 2025-03-21 23:00 18.5 62 5.6 0.0 7.4221502
#> 52 2025-03-22 00:00 19.0 59 6.2 0.0 8.1990611
#> 53 2025-03-22 01:00 19.6 56 5.8 0.0 9.0229171
#> 54 2025-03-22 02:00 20.0 56 6.6 0.0 9.0504996
#> 55 2025-03-22 03:00 20.6 53 8.2 0.0 9.9221594
#> 56 2025-03-22 04:00 21.6 50 8.9 0.0 10.8767392
#> 57 2025-03-22 05:00 23.0 48 12.9 0.0 11.6105468
#> 58 2025-03-22 06:00 25.1 44 17.1 0.0 13.1224867
#> 59 2025-03-22 07:00 27.0 39 18.3 0.0 15.1515705
#> 60 2025-03-22 08:00 28.5 35 17.3 0.0 16.9671002
#> 61 2025-03-22 09:00 29.9 32 15.9 0.0 18.5022156
#> 62 2025-03-22 10:00 30.5 29 13.9 0.0 20.0676795
#> 63 2025-03-22 11:00 30.3 29 13.3 0.0 20.0388794
#> 64 2025-03-22 12:00 30.8 28 12.1 0.0 20.6372019
#> 65 2025-03-22 13:00 31.1 27 8.6 0.0 21.2257677
#> 66 2025-03-22 14:00 30.8 27 5.7 0.0 21.1802818
#> 67 2025-03-22 15:00 29.4 30 5.0 0.0 19.4038663
#> 68 2025-03-22 16:00 26.5 40 3.3 0.0 14.7127208
#> 69 2025-03-22 17:00 24.9 48 2.3 0.0 11.7765780
#> 70 2025-03-22 18:00 24.1 48 3.6 0.0 11.7065329
#> 71 2025-03-22 19:00 22.0 53 4.3 0.0 10.0279203
#> 72 2025-03-22 20:00 20.5 59 4.5 0.0 8.2936138
#> 73 2025-03-22 21:00 20.0 61 3.8 0.0 7.7557435
#> 74 2025-03-22 22:00 20.7 58 3.0 0.0 8.5664482
#> 75 2025-03-22 23:00 20.8 57 3.1 0.0 8.8373254
#> 76 2025-03-23 00:00 20.3 59 4.3 0.0 8.2809765
#> 77 2025-03-23 01:00 19.6 62 4.6 0.0 7.4850903
#> 78 2025-03-23 02:00 19.1 63 4.9 0.0 7.2138852
#> 79 2025-03-23 03:00 18.9 63 5.3 0.0 7.2028106
#> 80 2025-03-23 04:00 19.5 57 6.3 0.0 8.7501673
#> 81 2025-03-23 05:00 22.0 53 6.3 0.0 10.0279203
#> 82 2025-03-23 06:00 25.4 47 11.4 0.0 12.1438692
#> 83 2025-03-23 07:00 27.5 42 13.0 0.0 14.0738742
#> 84 2025-03-23 08:00 29.5 37 14.9 0.0 16.2410539
#> 85 2025-03-23 09:00 30.9 34 16.2 0.0 17.7097755
#> 86 2025-03-23 10:00 31.2 33 16.9 0.0 18.2059254
#> 87 2025-03-23 11:00 30.8 35 17.3 0.0 17.2520615
#> 88 2025-03-23 12:00 30.3 37 16.7 0.0 16.3354786
#> 89 2025-03-23 13:00 29.7 41 14.3 0.0 14.6775271
#> 90 2025-03-23 14:00 28.8 45 10.7 0.0 13.1341761
#> 91 2025-03-23 15:00 27.9 50 8.2 0.0 11.4000680
#> 92 2025-03-23 16:00 26.7 55 7.7 0.0 9.8033695
#> 93 2025-03-23 17:00 25.3 59 8.1 0.0 8.5997055
#> 94 2025-03-23 18:00 24.0 65 8.4 0.0 6.9948499
#> 95 2025-03-23 19:00 23.0 72 7.6 0.6 5.3276314
#> 96 2025-03-23 20:00 22.1 81 5.8 0.6 3.4190951
#> 97 2025-03-23 21:00 21.4 87 4.5 0.6 2.2575920
#> 98 2025-03-23 22:00 20.9 90 3.3 1.5 1.7050630
#> 99 2025-03-23 23:00 20.5 90 3.4 1.5 1.6998097
#> 100 2025-03-24 00:00 20.2 91 4.4 1.5 1.5190652
#> 101 2025-03-24 01:00 19.8 93 4.7 0.0 1.1668680
#> 102 2025-03-24 02:00 19.4 95 4.8 0.0 0.8232852
#> 103 2025-03-24 03:00 19.4 95 4.8 0.0 0.8232852
#> 104 2025-03-24 04:00 19.9 93 5.2 0.0 1.1677724
#> 105 2025-03-24 05:00 20.9 89 6.4 0.0 1.8845643
#> 106 2025-03-24 06:00 22.0 84 7.2 0.0 2.8332746
#> 107 2025-03-24 07:00 23.5 77 9.4 0.0 4.2727633
#> 108 2025-03-24 08:00 25.0 68 13.0 0.0 6.3272900
#> 109 2025-03-24 09:00 26.2 62 15.5 0.0 7.8681440
#> 110 2025-03-24 10:00 26.7 60 15.3 0.0 8.4220002
#> 111 2025-03-24 11:00 26.8 60 14.4 0.0 8.4282705
#> 112 2025-03-24 12:00 26.6 62 13.7 0.0 7.8916573
#> 113 2025-03-24 13:00 26.0 64 13.2 0.1 7.3491598
#> 114 2025-03-24 14:00 25.1 68 12.0 0.1 6.3320500
#> 115 2025-03-24 15:00 24.2 72 10.1 0.1 5.3762846
#> 116 2025-03-24 16:00 23.3 77 7.7 0.0 4.2662760
#> 117 2025-03-24 17:00 22.4 82 5.1 0.0 3.2298741
#> 118 2025-03-24 18:00 21.7 86 4.6 0.0 2.4488729
#> 119 2025-03-24 19:00 21.3 88 4.7 0.3 2.0722091
#> 120 2025-03-24 20:00 21.0 88 4.4 0.3 2.0674305
#> 121 2025-03-24 21:00 20.8 89 4.1 0.3 1.8831123
#> 122 2025-03-24 22:00 20.6 90 3.5 0.0 1.7011223
#> 123 2025-03-24 23:00 20.4 91 2.8 0.0 1.5214152
#> 124 2025-03-25 00:00 20.3 91 2.3 0.0 1.5202400
#> 125 2025-03-25 01:00 19.1 96 2.9 0.0 0.6541165
#> 126 2025-03-25 02:00 18.5 98 3.1 0.0 0.3226258
#> 127 2025-03-25 03:00 18.7 97 3.1 0.0 0.4868640
#> 128 2025-03-25 04:00 20.3 90 3.3 0.0 1.6971860
#> 129 2025-03-25 05:00 22.8 79 3.5 0.0 3.8392501
#> 130 2025-03-25 06:00 24.8 70 3.6 0.0 5.8535015
#> 131 2025-03-25 07:00 26.1 65 5.4 0.2 7.1060360
#> 132 2025-03-25 08:00 26.9 61 8.9 0.2 8.1700451
#> 133 2025-03-25 09:00 27.5 59 11.6 0.2 8.7417895
#> 134 2025-03-25 10:00 28.0 57 12.3 0.2 9.3275915
#> 135 2025-03-25 11:00 28.3 55 12.3 0.2 9.9201517
#> 136 2025-03-25 12:00 28.2 56 11.7 0.2 9.6248801
#> 137 2025-03-25 13:00 27.6 60 9.8 0.3 8.4785143
#> 138 2025-03-25 14:00 26.6 65 7.6 0.3 7.1326345
#> 139 2025-03-25 15:00 25.6 71 5.8 0.3 5.6596746
#> 140 2025-03-25 16:00 24.4 76 5.1 0.2 4.5134752
#> 141 2025-03-25 17:00 23.2 82 5.1 0.2 3.2496490
#> 142 2025-03-25 18:00 22.1 86 5.8 0.2 2.4563953
#> 143 2025-03-25 19:00 21.3 89 5.2 0.0 1.8903775
#> 144 2025-03-25 20:00 20.7 90 4.0 0.0 1.7024353
#> 145 2025-03-25 21:00 20.1 92 3.1 0.0 1.3429084
#> 146 2025-03-25 22:00 19.6 94 2.7 0.0 0.9940198
#> 147 2025-03-25 23:00 19.1 96 2.5 0.0 0.6541165
#> 148 2025-03-26 00:00 18.7 98 2.5 0.0 0.3231294
#> 149 2025-03-26 01:00 18.3 99 2.6 0.0 0.1603492
#> 150 2025-03-26 02:00 17.9 100 2.4 0.0 0.0000000
#> 151 2025-03-26 03:00 18.2 98 3.2 0.0 0.3218711
#> 152 2025-03-26 04:00 19.3 93 4.9 0.0 1.1623511
#> 153 2025-03-26 05:00 21.0 87 7.7 0.0 2.2506571
#> 154 2025-03-26 06:00 22.9 79 8.8 0.0 3.8421765
#> 155 2025-03-26 07:00 24.9 70 7.5 0.0 5.8579124
#> 156 2025-03-26 08:00 27.0 60 5.6 0.0 8.4408179
#> 157 2025-03-26 09:00 28.5 53 4.8 0.0 10.5260046
#> 158 2025-03-26 10:00 29.0 51 4.5 0.1 11.1780750
#> 159 2025-03-26 11:00 29.0 51 4.4 0.1 11.1780750
#> 160 2025-03-26 12:00 28.7 52 4.0 0.1 10.8449148
#> 161 2025-03-26 13:00 28.5 52 4.7 0.1 10.8289682
#> 162 2025-03-26 14:00 28.2 54 6.1 0.1 10.2054061
#> 163 2025-03-26 15:00 27.6 56 6.6 0.1 9.5822749
#> 164 2025-03-26 16:00 26.6 61 5.5 0.0 8.1518148
#> 165 2025-03-26 17:00 25.3 68 3.8 0.0 6.3415752
#> 166 2025-03-26 18:00 24.1 73 2.9 0.0 5.1510898
#> 167 2025-03-26 19:00 23.3 76 2.9 0.0 4.4759932
#> 168 2025-03-26 20:00 22.7 79 3.1 0.0 3.8363247
#> 169 2025-03-26 21:00 22.1 81 3.3 0.0 3.4190951
#> 170 2025-03-26 22:00 21.6 84 3.1 0.0 2.8246009
#> 171 2025-03-26 23:00 21.0 87 3.0 0.0 2.2506571
#> 172 2025-03-27 00:00 20.6 89 2.9 0.0 1.8802102
#> 173 2025-03-27 01:00 20.0 91 2.6 0.0 1.5167170
#> 174 2025-03-27 02:00 19.6 93 2.5 0.0 1.1650602
#> 175 2025-03-27 03:00 19.5 93 3.3 0.0 1.1641568
#> 176 2025-03-27 04:00 20.3 89 5.2 0.8 1.8758611
#> 177 2025-03-27 05:00 21.4 84 7.9 0.8 2.8202691
#> 178 2025-03-27 06:00 22.3 80 10.0 0.8 3.6233858
#> 179 2025-03-27 07:00 28.2 50 22.5 0.0 11.4252818
#> 180 2025-03-27 08:00 29.4 48 21.9 0.0 12.1742949
#> 181 2025-03-27 09:00 30.1 47 23.0 0.0 12.5712535
#> 182 2025-03-27 10:00 30.0 48 23.5 0.0 12.2277998
#> 183 2025-03-27 11:00 29.3 50 23.1 0.0 11.5179603
#> 184 2025-03-27 12:00 28.7 51 22.0 0.0 11.1534674
#> 185 2025-03-27 13:00 28.3 50 19.1 0.0 11.4336923
#> 186 2025-03-27 14:00 28.0 48 14.7 0.0 12.0498855
#> 187 2025-03-27 15:00 27.2 49 11.2 0.0 11.6573513
#> 188 2025-03-27 16:00 25.6 55 9.2 0.0 9.7234680
#> 189 2025-03-27 17:00 23.7 64 7.7 0.0 7.2232755
#> 190 2025-03-27 18:00 22.0 71 4.8 0.0 5.5075862
#> 191 2025-03-27 19:00 20.8 75 1.6 0.0 4.5995495
#> 192 2025-03-27 20:00 19.9 78 9.2 0.0 3.9546684
#> 193 2025-03-27 21:00 19.4 80 14.3 0.0 3.5435580
#> 194 2025-03-27 22:00 19.5 81 14.3 0.0 3.3514444
#> 195 2025-03-27 23:00 19.9 82 11.6 0.0 3.1684615
#> 196 2025-03-28 00:00 20.1 83 9.7 0.0 2.9817669
#> 197 2025-03-28 01:00 19.5 87 9.5 0.1 2.2247454
#> 198 2025-03-28 02:00 18.6 92 9.9 0.1 1.3273667
#> 199 2025-03-28 03:00 18.4 94 10.8 0.1 0.9847874
#> 200 2025-03-28 04:00 19.3 91 12.1 0.1 1.5085125
#> 201 2025-03-28 05:00 21.0 85 13.3 0.1 2.6227079
#> 202 2025-03-28 06:00 22.9 77 13.5 0.1 4.2533158
#> 203 2025-03-28 07:00 25.3 66 12.8 0.0 6.8197284
#> 204 2025-03-28 08:00 27.9 53 13.8 0.0 10.4795420
#> 205 2025-03-28 09:00 29.8 44 16.0 0.0 13.5839267
#> 206 2025-03-28 10:00 30.4 41 16.8 0.0 14.7523536
#> 207 2025-03-28 11:00 30.2 42 16.3 0.0 14.3541496
#> 208 2025-03-28 12:00 29.5 45 15.9 0.0 13.2015553
#> 209 2025-03-28 13:00 28.0 52 15.7 0.1 10.7891510
#> 210 2025-03-28 14:00 25.9 61 15.5 0.1 8.1093542
#> 211 2025-03-28 15:00 24.0 71 13.3 0.1 5.5918295
#> 212 2025-03-28 16:00 22.4 81 12.6 0.3 3.4269439
#> 213 2025-03-28 17:00 21.0 91 23.7 0.3 1.5284761
#> 214 2025-03-28 18:00 20.0 98 30.9 0.3 0.3264122
#> 215 2025-03-28 19:00 19.4 100 24.2 1.2 0.0000000
#> 216 2025-03-28 20:00 19.2 99 12.7 1.2 0.1614783
#> 217 2025-03-28 21:00 19.1 98 10.7 1.2 0.3241377
#> 218 2025-03-28 22:00 18.8 98 12.4 0.0 0.3233813
#> 219 2025-03-28 23:00 18.6 97 12.6 0.0 0.4864847
#> 220 2025-03-29 00:00 18.4 97 12.4 0.0 0.4857265
#> 221 2025-03-29 01:00 18.1 97 11.8 0.0 0.4845902
#> 222 2025-03-29 02:00 17.9 98 11.0 0.0 0.3211174
#> 223 2025-03-29 03:00 17.9 98 10.1 0.0 0.3211174
#> 224 2025-03-29 04:00 18.3 97 11.0 0.0 0.4853476
#> 225 2025-03-29 05:00 18.9 95 12.5 0.0 0.8200912
#> 226 2025-03-29 06:00 19.4 94 13.9 0.0 0.9924781
#> 227 2025-03-29 07:00 19.4 96 13.7 0.1 0.6556437
#> 228 2025-03-29 08:00 19.2 99 13.1 0.1 0.1614783
#> 229 2025-03-29 09:00 19.7 97 13.0 0.1 0.4906655
#> 230 2025-03-29 10:00 21.5 84 14.0 0.0 2.8224346
#> 231 2025-03-29 11:00 24.0 66 15.2 0.0 6.7533413
#> 232 2025-03-29 12:00 25.6 53 15.8 0.0 10.3023498
#> 233 2025-03-29 13:00 25.5 50 15.4 0.0 11.1993172
#> 234 2025-03-29 14:00 24.6 51 14.5 0.0 10.8197686
#> 235 2025-03-29 15:00 23.3 56 12.9 0.0 9.2796043
#> 236 2025-03-29 16:00 21.4 64 10.7 0.0 7.0984486
#> 237 2025-03-29 17:00 19.2 75 8.8 0.0 4.5432878
#> 238 2025-03-29 18:00 17.6 83 7.9 0.0 2.9245444
#> 239 2025-03-29 19:00 17.0 85 6.0 0.0 2.5426293
#> 240 2025-03-29 20:00 16.9 84 3.6 0.0 2.7236709
# Retrospective analysis of application conditions
retrosp <- tdelta(-53.6969,-28.0638,type=2,days=10,
dates=c("2023-01-01","2023-05-01"),
details=TRUE)
retrosp
#> ────────────────────────────────────────────────────────────────────────────────
#>
#> ── NASA/POWER Source Native Resolution Hourly Data ────────────────────────────
#> Dates (month/day/year): 01/01/2023 through 05/01/2023 in LST
#> Location: Latitude -28.0638 Longitude -53.6969
#> Elevation from MERRA-2: Average for 0.5 x 0.625 degree lat/lon region = 429.95
#> meters
#> The value for missing source data that cannot be computed or is outside of the
#> sources availability range: NA
#> Parameter(s):
#> ────────────────────────────────────────────────────────────────────────────────
#> Parameters:
#> T2M MERRA-2 Temperature at 2 Meters (C) ; RH2M MERRA-2 Relative Humidity at 2
#> Meters (%) ; PRECTOTCORR MERRA-2 Precipitation Corrected (mm/hour)
#> ────────────────────────────────────────────────────────────────────────────────
#> # A tibble: 2,904 × 8
#> YEAR MO DY HR T2M RH2M PRECTOTCORR DELTAT
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2023 1 1 0 21.3 78.0 0.02 4.01
#> 2 2023 1 1 1 21.1 79.9 0.97 3.60
#> 3 2023 1 1 2 21.0 81.6 2.92 3.27
#> 4 2023 1 1 3 21.0 82.4 2.14 3.12
#> 5 2023 1 1 4 21.0 83.6 1.32 2.88
#> 6 2023 1 1 5 22.0 80.1 0.4 3.60
#> 7 2023 1 1 6 23.4 75.7 0.14 4.53
#> 8 2023 1 1 7 25.6 67.0 1.26 6.60
#> 9 2023 1 1 8 28.4 56.1 3.07 9.62
#> 10 2023 1 1 9 31.0 48.5 2.92 12.2
#> # ℹ 2,894 more rows##Stress indicators from agronomic traits
The stind() function estimates several stress indicators
based on the productivity of a given crop subjected or not to stressful
conditions, as described by Ghazvini et al(2024).
library(EstimateBreed)
data("aveia")
#General
with(aveia,stind(GEN,MC,MG,index = "ALL",bygen=TRUE))
#> GEN STI YI GMP MP MH SSI YSI
#> 1 G1 0.6843575 0.6843575 1.233995 0.7613715 0.3747927 1.1866617 0.6843575
#> 2 G10 0.7628362 0.7628362 1.488428 1.1077083 0.2690707 0.7998926 0.7628362
#> 3 G11 0.7172237 0.7172237 1.372668 0.9421094 0.3293413 1.0143855 0.7172237
#> 4 G12 0.7589286 0.7589286 1.626175 1.3222222 0.2741117 0.8172584 0.7589286
#> 5 G13 0.6997519 0.6997519 1.404643 0.9865104 0.3532847 1.1039536 0.6997519
#> 6 G14 0.6722222 0.6722222 1.229837 0.7562500 0.3920266 1.2545304 0.6722222
#> 7 G2 0.7753623 0.7753623 1.518943 1.1535938 0.2530612 0.7454053 0.7753623
#> 8 G3 0.7288136 0.7288136 1.259216 0.7928125 0.3137255 0.9573397 0.7288136
#> 9 G4 0.7648579 0.7648579 1.410230 0.9943750 0.2664714 0.7909777 0.7648579
#> 10 G5 0.7218543 0.7218543 1.425474 1.0159877 0.3230769 0.9913736 0.7218543
#> 11 G6 0.6934673 0.6934673 1.380972 0.9535417 0.3620178 1.1372745 0.6934673
#> 12 G7 0.7043478 0.7043478 1.608571 1.2937500 0.3469388 1.0799619 0.7043478
#> 13 G8 0.6726058 0.6726058 1.534318 1.1770660 0.3914780 1.2523478 0.6726058
#> 14 G9 0.7193878 0.7193878 1.385340 0.9595833 0.3264095 1.0035942 0.7193878
#> RSI
#> 1 1.0522445
#> 2 0.9439922
#> 3 1.0040263
#> 4 0.9488527
#> 5 1.0290954
#> 6 1.0712401
#> 7 0.9287418
#> 8 0.9880599
#> 9 0.9414970
#> 10 0.9975856
#> 11 1.0384216
#> 12 1.0223804
#> 13 1.0706293
#> 14 1.0010060
#Only the desired index
with(aveia,stind(GEN,MC,MG,index = "STI",bygen=TRUE))
#> GEN STI
#> 1 G1 0.6843575
#> 2 G10 0.7628362
#> 3 G11 0.7172237
#> 4 G12 0.7589286
#> 5 G13 0.6997519
#> 6 G14 0.6722222
#> 7 G2 0.7753623
#> 8 G3 0.7288136
#> 9 G4 0.7648579
#> 10 G5 0.7218543
#> 11 G6 0.6934673
#> 12 G7 0.7043478
#> 13 G8 0.6726058
#> 14 G9 0.7193878##Risk of Disease Occurrence in Soybeans
Predicting the occurrence of Asian soybean rust is critical for timely disease management. Early detection allows for targeted interventions, such as fungicide application, minimizing crop losses and reducing the spread of the disease.
This can be estimated with the risk() function, based on
the methodology proposed by Engers et al. (2024), which uses temperature
and relative humidity to define the potential risk of the disease
occurring.
library(EstimateBreed)
# Rust Risk Prediction
data("clima")
with(clima, risk(DY, MO, TMED, RH, disease = "rust"))
#> Month RHrisk TEMPrisk TOTALrisk RELrisk
#> 1 1 12.706058 26.83114 340.91801 77.28258522
#> 2 2 13.377456 26.68142 356.92951 81.03100483
#> 3 3 16.307827 26.85558 437.95622 100.00000000
#> 4 4 4.674734 21.95465 102.63217 21.49798046
#> 5 5 1.003280 11.08089 11.11724 0.07361011
#> 6 6 6.195717 15.41482 95.50588 19.82966088
#> 7 7 1.000115 13.76856 13.77015 0.69467659
#> 8 8 1.000790 10.79428 10.80281 0.00000000
#> 9 9 1.138186 23.72200 27.00004 3.79189947
#> 10 10 2.132149 24.50724 52.25308 9.70383628
#> 11 11 1.010342 22.68888 22.92353 2.83755630
#> 12 12 5.045327 27.28285 137.65089 29.69614071