Forages for Reduced Nitrate Leaching, Eric and Maxine Watson (Canterbury)
10 min read
10 min read
Eric and Maxine, arable farmers from Wakanui, contributed to the FRNL research in Canterbury. Their 481 ha farm has specific soil types, an average rainfall of 600 mm, and they mainly grow vegetable and seed crops. Joining the FRNL programme made them re-evaluate their practices, especially around minimising potential nitrogen (N) leaching using cover crops. They've become more conscious of soil mineral nitrogen and the benefits of not leaving ground fallow. Joining the programme was about studying best practices. It's crucial to understand N usage for the environment and maintain control over application rates. Proper scientific data can guide future regulations and practices.
Area:
481 ha
Dominant soils:
Wakanui silt loam, Wakanui clay loam, and Templeton silt loam.
Average rainfall:
approx. 600 mm
Main crops:
vegetable seed crops, ryegrass and plantain seed crops, wheat
Trading as:
Rangitata Holdings Limited
“It seemed like a good idea to make a controlled study of what good operators do.”
"It’s very important environmentally to know what’s happening N-wise. If we can’t quantify N usage in plant growth without compromising yield, we risk losing control of application rates."
“If the programme provides good scientific data about how and when plants use N in its various forms, voluntary adoption of the resulting recommendations by the wider farming community is more likely, thus making any future regulation match good practice.”
Annual summary
The Simple Crop Resource Uptake Model operating within the Agricultural Production Systems sIMulator (SCRUM-APSIM) was used to simulate N (N) balances at Eric and Maxine’s farm. The APSIM predicted data indicated that paddocks with high N leaching (≥30 kg N/ha) had greater residual soil N after the summer harvest.
Modelling outcomes of two seasons on the farm identified the following factors that increased the amount of leachable soil N:
Leaching was greater when at least two of these factors were in play. For example, excessive N application to spring-sown crops resulted in high residual soil N at risk of leaching if no crops were sown after the summer harvest to take up the N. Similarly, mineralised N from residues retained in paddocks was available for leaching if there was no vegetation in autumn/winter to take up water and N to reduce drainage and N leaching.
N leaching mitigation options that reduced N leaching and increased gross margins included:
Heavy rainfall during the autumn period contributed to a high number of fallow paddocks in the 2014-15 season. The absence of vegetation to take up soil N, combined with the high rainfall resulted in high leaching losses. Reductions in N leaching on this farm were achieved by reducing the area of the farm in fallow for the subsequent seasons. It is important to note that the use of catch crops to mop up excessive soil N may not always be guaranteed because it is subject to weather conditions. Therefore, using fertiliser recommendation systems which calculate crop fertiliser N requirements that account for soil mineralisation could be a more reliable approach to reducing soil N at risk of leaching during autumn and winter.
Overall, results have shown that leaching is mainly influenced by rainfall through its impact on drainage, but farm management practices determine the amount of soil N at risk of leaching. Strategies that can reduce the amount of soil N available for leaching include: sowing catch crops immediately after the summer harvest to mop up residual soil N or N mineralised from soil organic matter and crop residues, and reduced N fertiliser use by calculating requirements with a recommendation system that accounts for soil mineralisation.
The below table shows N leaching and drainage over five seasons (season = 01 Apr – 31 Mar) as estimated by SCRUM-APSIM at a depth of 150cm.
Paddock | Leaching (kg N/ha) | Drainage (mm) | ||||||||
2014-15 | 2015-16 | 2016-17 | 2017-18 | 2018-19 | 2014-15 | 2015-16 | 2016-17 | 2017-18 | 2018-19 | |
RH1 | 39.6 | 23.0 | 5.8 | 18.0 | 5.2 | 207 | 213 | 62 | 248 | 171 |
RH2 | 18.2 | 4.3 | 0.2 | 9.2 | 37.4 | 237 | 338 | 10 | 337 | 279 |
RH2A | 10.7 | 23.0 | 0.1 | 31.1 | 3.0 | 386 | 212 | 1 | 513 | 112 |
RH3 | 7.6 | 20.7 | 26.2 | 30.8 | 7.2 | 208 | 135 | 73 | 485 | 239 |
RH4 | 74.2 | 48.9 | 0.3 | 27.7 | 4.0 | 255 | 223 | 2 | 460 | 205 |
RH5 | 7.2 | 15.2 | 1.6 | 18.2 | 21.5 | 400 | 209 | 24 | 218 | 196 |
RH6 | 51.6 | 5.4 | 4.7 | 21.8 | 5.0 | 333 | 60 | 39 | 349 | 61 |
RH7 | 18.6 | 7.9 | 4.4 | 20.0 | 6.9 | 178 | 75 | 33 | 263 | 141 |
RH8_12 | 79.2 | 9.8 | 9.2 | 41.5 | 51.0 | 402 | 186 | 167 | 429 | 187 |
RH9A | 13.7 | 4.9 | 2.5 | 31.5 | 15.9 | 256 | 86 | 70 | 333 | 191 |
RH9B | 14.3 | 8.4 | 7.7 | 24.8 | 13.5 | 293 | 111 | 21 | 491 | 183 |
RH10 | 53.3 | 10.2 | 3.6 | 7.9 | 20.5 | 416 | 262 | 47 | 312 | 289 |
RH11A | 8.9 | 7.8 | 45.9 | 31.1 | 14.8 | 160 | 137 | 132 | 398 | 151 |
RH11B | 8.9 | 21.8 | 54.5 | 43.6 | 21.2 | 160 | 149 | 133 | 323 | 146 |
RH13 | 34.9 | 19.1 | 71.3 | 39.6 | 7.2 | 263 | 141 | 151 | 316 | 139 |
RH14 | 7.7 | 30.3 | 4.3 | 12.4 | 6.8 | 172 | 300 | 202 | 374 | 128 |
RH15 | 60.5 | 26.8 | 10.3 | 15.6 | 26.8 | 288 | 283 | 45 | 337 | 201 |
RH16 | 9.0 | 0.8 | 0.0 | 8.1 | 22.9 | 146 | 11 | 0 | 310 | 126 |
RH17 | 24.7 | 51.5 | 1.6 | 4.0 | 14.8 | 227 | 303 | 14 | 381 | 121 |
RH18 | 6.3 | 2.7 | 0.6 | 13.4 | 14.7 | 189 | 59 | 11 | 431 | 124 |
RH19 | 24.6 | 1.1 | 4.0 | 8.5 | 14.4 | 149 | 85 | 69 | 423 | 238 |
RH20 | 56.0 | 7.4 | 0.5 | 7.6 | 7.1 | 528 | 137 | 50 | 474 | 134 |
RH21 | 28.1 | 52.6 | 5.9 | 11.5 | 4.8 | 129 | 251 | 125 | 236 | 109 |
Whole-farm average | 33.7 | 16.2 | 9.5 | 19.5 | 16.9 | 284 | 178 | 71 | 384 | 173 |
The below table shows fertiliser application and N uptake (as estimated by SCRUM-APSIM) over five seasons (season = 01 Apr – 31 Mar).
Paddock | Applied fertiliser (kg N/ha) | N Uptake (kg N/ha) | ||||||||
2014-15 | 2015-16 | 2016-17 | 2017-18 | 2018-19 | 2014-15 | 2015-16 | 2016-17 | 2017-18 | 2018-19 | |
RH1 | 287 | 95 | 0 | ̶ | 0 | 342 | 101 | 125 | ̶ | 80 |
RH2 | 268 | 189 | 253 | 209 | 57 | 358 | 117 | 357 | 180 | 176 |
RH2A | 340 | 182 | 112 | 94 | 203 | 258 | 254 | 199 | 220 | 251 |
RH3 | 266 | 373 | 111 | 179 | 141 | 229 | 357 | 149 | 275 | 193 |
RH4 | 19 | 94 | 65 | 114 | 90 | 73 | 215 | 157 | 108 | 148 |
RH5 | 321 | 79 | 304 | 151 | 96 | 234 | 80 | 343 | 230 | 171 |
RH6 | 290 | 15 | 304 | 108 | 191 | 156 | 222 | 403 | 230 | 234 |
RH7 | 377 | 57 | 130 | 172 | 57 | 345 | 265 | 173 | 203 | 140 |
RH8_12 | 216 | 150 | 146 | 283 | 268 | 151 | 150 | 130 | 347 | 341 |
RH9A | 63 | 341 | 124 | 280 | 81 | 73 | 352 | 210 | 333 | 134 |
RH9B | 134 | 341 | 0 | 280 | 117 | 132 | 353 | 134 | 317 | 125 |
RH10 | 182 | 169 | 396 | ̶ | 96 | 141 | 109 | 225 | ̶ | 122 |
RH11A | 165 | 341 | 111 | 15 | 224 | 223 | 349 | 191 | 44 | 317 |
RH11B | 186 | 341 | 111 | 122 | 57 | 165 | 349 | 181 | 120 | 265 |
RH13 | 219 | 341 | 111 | 72 | 257 | 264 | 358 | 236 | 161 | 328 |
RH14 | 272 | 205 | 131 | 128 | 192 | 331 | 165 | 140 | 267 | 315 |
RH15 | 157 | 150 | 258 | 98 | 100 | 98 | 116 | 404 | 138 | 142 |
RH16 | 327 | 249 | 184 | 118 | 212 | 335 | 326 | 258 | 125 | 314 |
RH17 | 230 | 182 | 253 | 158 | 110 | 355 | 80 | 348 | 102 | 310 |
RH18 | 331 | 58 | 181 | 118 | 212 | 343 | 218 | 219 | 100 | 323 |
RH19 | 202 | 92 | 230 | 200 | 40 | 234 | 64 | 238 | 212 | 139 |
RH20 | 368 | 239 | 0 | 168 | 23 | 265 | 257 | 60 | 233 | 68 |
RH21 | 203 | 0 | 230 | 188 | 185 | 266 | 200 | 273 | 254 | 267 |
Whole-farm average | 248 | 183 | 167 | 161 | 129 | 238 | 205 | 217 | 198 | 210 |
Estimating N fertiliser rates
The industry-agreed good management practice for nutrient management is to “Manage the amount and timing of fertiliser inputs, taking account of all sources of nutrients, to match plant requirements and minimise risk of losses”. To do this with confidence, farmers require reliable information and methods for working out how much fertiliser to apply to their crops.
Comparisons were carried out in crops sown in spring 2017 comparing farmers’ current N application rates with APSIM forecasts based on deep soil mineral N sampling. Models like APSIM use a mass balance approach to determine how much fertiliser should be applied to the crop to achieve its potential yield. Paddocks were selected and divided into two sections to demonstrate crop performance using farmer- and model-estimated fertiliser N application rates.
Soil mineral N tests give a measure of N available for plant uptake and are used to improve fertiliser N predictions. The anaerobically mineralizable N (AMN) test gives a measure of the N that will become available over the growing season. These measurements can be costly and time consuming, so for soil mineral N, one method which may overcome some of this is the ‘quick test’. This is an in-field approach which utilises a test strip and a simple colorimetric scale which can be used to quantify soil solution nitrate-N concentrations. These are currently being validated as part of another project and were used to help forecast N fertiliser required on this farm in addition to the APSIM estimates.
APSIM predicted, quick test predicted, and actual farmer practice fertiliser N rates for the 2017−18 growing season, for the paddocks used in the fertiliser N rates comparison.
Paddock | Crop | Soil sampling date | Pre-sowing soil mineral N (kg/ha) | APSIM estimated fertiliser N (kg/ha) | Quick test estimated fertiliser N (kg/ha) | Farmer fertiliser N (kg/ha) |
RH3 | Barley | 23 Aug 17 | 82.5 | 140 | 160 | 205 |
RH4 | Barley | 23 Aug 17 | 47.3 | 170 | 170 | 197 |
Paddock sections that received model-estimated N rates yielded greater dry matter (DM), had greater N use efficiency, lower soil residual N at harvest, and lower N leaching compared with the paddock sections which used farmer-estimated N rates.
Applied N fertiliser, crop yield, N use efficiency and model-predicted N leaching and residual soil N at harvest for demonstration paddocks in 2017-18 season.
Paddock | Crop | N rate estimated by | Applied N (kg N/ha) | Yield (t DM/ha) | N use efficiency* | Leaching (kg N/ha) | Residual N (kg N/ha) |
RH3 | Barley | Farmer Model | 205 140 | 8.99 10.39 | 0.88 1.34 | 26 17.2 | 74 39 |
RH4 | Barley | Farmer Model | 197 170 | 9.97 13.51 | 0.96 1.67 | 33 29.3 | 29 19 |
*NUE = grain DM produced per kg of N applied.
An article relating to the SCRUM-APSIM modelling work was published in Agronomy NZ.
A full report on the arable monitor farms, including the detailed results of the catch crop demonstrations on this farm and at Chertsey, and an N fertiliser rate demonstration on another arable monitor farm.