MILK DEVELOPMENT COUNCIL DEVELOPMENT OF A SYSTEM FOR MONITORING AND FORECASTING HERBAGE GROWTH

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1 MILK DEVELOPMENT COUNCIL DEVELOPMENT OF A SYSTEM FOR MONITORING AND FORECASTING HERBAGE GROWTH Project No. 97/R1/14

2 Milk Development Council project 97/R1/14 Development of a system for monitoring and forecasting herbage growth A.J. Rook and R. O. Clements Institute of Grassland and Environmental Research, North Wyke, Okehampton, Devon EX20 2SB

3 Farmer Recommendations Herbage growth forecasts should be used on a regular basis to plan grassland utilization by forward budgeting of available grass to match animal requirements. Wherever possible these forecasts should be based on local information as climatic and soil conditions vary widely. Monitoring of current yields is crucial to good forecasts and is of great value even when forecasting models perform poorly. A network of properly managed and monitored sites is crucial to achieving this aim, as it ensures accurate measurement, which may not always be possible under time pressured circumstances on farms. Published current yields and forecasts are of great value in building farmer confidence in their own on-farm observations.

4 Executive Summary Objective To develop a system of weekly forecasts of herbage growth to be published as a grassland management tool for farmers Background Grazed herbage is an important food source for dairy cows and can supply nutrients at a lower cost than alternative foods. For efficient and sustainable grazing systems it is necessary to plan the utilization of herbage so as to match the pattern of herbage production. Reliable forecasts of herbage growth are an important prerequisite for such planning. Large data sets on weekly herbage growth over many years from IGER North Wyke and other sites provide a resource for the construction of herbage growth forecasting models. Methods Regression models were constructed using data collected between 1967 and 1985 at Hurley and between 1983 and 1997 at North Wyke. At each site in each year, 2 replicate series of 4 plots of perennial ryegrass, were cut at 4 weekly intervals throughout the growing season. Cutting dates were staggered such that one plot was cut in each week. Growth rates were calculated using the method of Corrall and Fenlon, ( J. Ag. Sci. Camb. 91, 67-67). Concurrent local meteorological data were also available. Models were constructed by regression analysis. All models were subject to outlier and collinearity analyses and were checked for correlated errors. In 1998 similar series of plots were measured on 4 sites, North Wyke, Trawscoed, Bridgets and Headley Hall. These data was used to test the performance of the models. Results Separate models were fitted for weeks and of the year (i.e. before and after peak growth rate). R 2 for models of growth rate using week of the year alone were 75% and 66% ( in early and late season respectively). Inclusion of current yield increased R 2 to 84.4% and 69.8%. The best model for early season also included air temperature and rainfall (R 2 86%) while for late season, air temperature was the only significant additional meteorological variable (R 2 70%). The performance of all models with 1998 data was disappointing. It is clear that more site specific information is needed. and that the inclusion of current yield data led to substantial improvement in prediction in most cases. Taken together these conclusions point to the need to establish a network of monitored sites which can be used to further refine the models and improve prediction. Conclusions There is a need for more site specific information, for example, soil moisture deficit and soil temperature. The inclusion of current yield data led to substantial improvement in prediction in most cases. There is a need to establish a network of 5-7 monitored sites which can be used to further refine both the input data and in the longer term the form of the models.

5 Report Objective To develop a system of weekly forecasts of herbage growth to be published as a grassland management tool for farmers Background The need in the industry. Grazed herbage is an important food source for dairy cows and can supply nutrients at a lower cost than alternative foods. For efficient and sustainable grazing systems it is necessary to plan the utilization of herbage so as to match the current and future patterns of herbage production and animal intake. To do this it is necessary for farmers to be able to forecast future herbage growth patterns. A possible solution One method of making this information available to farmers is by the publication of reliable forecasts of herbage growth for use in the planning of grazing strategies. Such published forecasts have proved popular in Ireland and the usefulness of this type of information is also demonstrated by the success of the tsum day degree forecasts in the UK The scientific resource Much progress has been made at IGER and elsewhere in understanding the basic principles underlying the growth of pastures. However, there is a need to transfer the knowledge gained in these studies to the farm. Large data sets on weekly herbage growth over many years from IGER North Wyke and the former GRI site at Hurley provide a resource for the construction of herbage growth forecasting models which has previously not been utilised. Methods Data for building the models (estimation data) Weekly herbage dry matter yield and herbage growth rate data collected between 1967 and 1985 at Hurley, Berkshire and between 1983 and 1997 at North Wyke, Devon were used to construct models. At each site in each year, 2 replicate series of 4 plots of perennial ryegrass, were cut to between 40 and 50 mm sward surface height at 4 weekly intervals from the first week in March until growth fell below 300 kg/ha in autumn. Cutting dates were staggered such that one plot in each series was cut in each week. Growth rates were calculated from this yield data using the method of Corrall and Fenlon, ( J. Ag. Sci. Camb. 91, 67-67) This method allows the mean growth of plots at different stages of regrowth in any one week to be calculated. Each plot received 600 kg N and 230 kg K per ha in equal weekly doses with 30 kg P/ha at the start of the season. These high fertiliser inputs were used in order to avoid nutrient stress and allow the effects of weather conditions to be fully manifested.. Concurrent local meteorological data (mean, minimum and maximum air temperatures, rainfall and sunshine hours) were also used. Other more detailed meteorological data were not used as they are unlikely to be locally available at all sites that might in future be used to supply data for forecasts.

6 Building the models Regression models were used to obtain forecasts of the growth rate in the forthcoming week from variables measured in the current week. All models analysed for outliers (i.e unusual data points that would lead to misleading results and collinearity (i.e. the existence of close relationships between two variables in the model, which effectively means that only one variable is really needed). Models were also checked for correlated errors, to ensure that errors observed in one week did not have an effect in the next week and thus invalidate the models. None of these issues proved to be of major importance, thus ordinary least squares regression methods were retained throughout. New data to test the models In order to test the performance of models in independent data, i.e. data not used in constructing the models, 4 replicate series of plots, managed in the same way as those used for the estimation data, were measured in 1998 on 4 sites, North Wyke, Trawsgoed (Dyfed), Bridgets (Hampshire) and Headley Hall (Yorkshire). Testing the models The performance of the models was tested using the MSPE method. Briefly this method partitions the prediction error into three components due to bias - the difference between the mean actual and predicted values. This indicates consistent over or under estimation. It often arises from differences between the estimation data and the test data which has not been included in the model, for example in this case, soil type. It can often be dealt with by simple correction factors line bias - the departure of the slope of the regression of actual on predicted values from unity. This indicates over prediction at low values and under prediction at high values or vice versa. It is generally due to inadequacies in the form of the model. random error. This has no systematic cause. The overall error in the forecast is expressed as the mean prediction error (MPE). This is the average difference between the actual and predicted values. Ideally a forecast will have a low overall error with most of this due to random error. Results- development of models Characteristics of estimation data The mean seasonal patterns of grass growth in the long term data from Hurley and North Wyke are shown in Figure 1. The patterns are partly attributable to seasonal changes in the inherent physiology of the grass plant, with increased growth in early season associated with the development of flowering stems, but differences in temperature, solar radiation and rainfall will also influence growth rates. The higher late season growth rates at North Wyke are probably related to the higher rainfall and better water retention by the impermeable soil at this site. A simple model of growth rate The simplest approach to predict grass growth rate is to relate it to week of the year. Because of the complex shape of the growth curve, the approach taken here has been to fit separate equations for the periods before and after peak growth rate. Despite the apparently nonlinearity of the response it was found that there was no significant improvement in fit to be gained from using non-linear models. therefore only the simple linear models are reported. These simple models accounted for 75% of the variation (termed R 2 in Table 1) in weekly growth rate in these data in early season (model 1) and 66% in late season (model 4).

7 Figure 1: Mean growth rates Hurley and North Wyke Growth rate kg/ha/day Hurley N. Wyke Week of year Including current yield data The simple models discussed above would not be expected to account for variation between years and between sites in different regions. The next step therefore was to include actual data on harvested yields collected from the plots at each site. The yield in the current week was used to predict the growth rate in the following week. Inclusion of current yield in the models, together with week of year, increased the percentage variation accounted for to 84% and 70% for early and late season respectively (models 2 and 5). Including meteorological data Once week of year and current yield are accounted for, the next possibility for improving prediction was to incorporate information from readily available meteorological data. Combinations of air temperature, sunshine hours and rainfall for the current week were tried in the models. In general their inclusion did not markedly improve the percentage variation accounted for. Only the best models are therefore reported here in order to avoid confusion. The best model for early season (model 3) included air temperature and rainfall along with week of year and current yield and accounted for 86% of the variation. For late season (model 6), air temperature was the only significant meteorological variable, the model accounting for 70% of the variation). Table 1 Model parameters and fit in estimation data model next week s growth rate = R 2 Early season *week of year *week of year *current yield *week of year *current yield * air temperature in current week *rainfall in current week Late season *week of year *week of year *current yield *week of year *current yield * air temperature in current week

8 Testing of models Characteristics of test data Growth rates, temperature and rainfall over the season for each of the 4 test sites are shown in figures 2a, 2b and 2c respectively. Actual data are given in the Appendix. It can be seen that growth rates at Trawsgoed and Bridgets peak at higher values than at the other two sites. However, the two sites in the west of the UK (North Wyke and Trawsgoed) sustained higher growth rates later in the season. It can be seen from the rainfall data that this was probably due to the generally higher rainfall on these sites. The low rainfall at Bridgets is particularly evident, as are the generally higher temperatures recorded at this site. Performance of models The performance of all models when tested with independent data obtained in 1998 was somewhat disappointing (Table 2). Table 2. Model performance with independent data Model Mean prediction error kg/ha/day percentage contribution to MSPE bias line bias random NW B HH T NW B HH T NW B HH T NW B HH T Early season Late season NW North Wyke; B Bridgets; HH Headley Hall; T Trawsgoed The mean prediction error in early season varied from 19.1 to 32.7 kg/ha/day for the simplest model based only on season of the year (Model 1), but was reduced to 12.1 to 21.8 kg/ha/day when current yield and significant weather variables were included (model 3). This level of precision is not sufficient to allow reliable use in practice where errors closer to 5 kg/ha/day would be needed. Most early season models showed high levels of bias. This suggests that there were systematic differences (most probably due to site differences such as soil type) between the test data and the estimation data. These differences can potentially be corrected for, given the right measurements. For example, it may be possible simply to produce site specific correction factors. More generally, however, variables which more closely reflect the effects of weather on the plant may be needed, for example soil moisture deficit rather than rainfall, soil temperature rather than air temperature. These measures are not available at all sites but would be relatively easy and cheap to measure at a dedicated network of forecasting sites. By using such measurements, rather than site specific corrections, it should be possible to generalise the models to any farm, not just those that are very similar to the test sites

9 Figure 2a Growth rates in Growth rate kg/ha/day /02 17/03 14/04 12/05 09/06 07/07 04/08 01/09 29/09 27/10 Date North Wyke Trawsgoed Bridgets Headley Hall

10 Figure 2b. Mean weekly temperature Temperature (degrees C) /02 17/03 14/04 12/05 09/06 07/07 04/08 01/09 29/09 27/10 Date North Wyke Trawsgoed Bridgets Headley Hall

11 Figure 2c. Weekly rainfall rainfall (mm) /02 17/03 14/04 12/05 09/06 07/07 04/08 01/09 29/09 27/10 Date North Wyke Trawsgoed Bridgets Headley Hall

12 In late season the simplest model gave mean prediction errors of 13.1 to 33.0 kg/ha/day and these were little improved by adding current yield or weather variables to the model. It is likely, that soil type is particularly important later in the season, when moisture stress is one of the main factors limiting growth. For both periods of the year the discrepancies were greatest for Bridgets. This site is subject to greater moisture stress than the others and this may explain why the model perform less well. Technology transfer Discussions were held with Farmers Weekly with a view to publishing the results of the test sites and the forecast values on a weekly basis. However, the magazine were reluctant to take this on without an assurance that it would be available in the long term. Consideration was also given to publishing this data on the IGER web site but it was felt that it might be more appropriate for MDC themselves to take on the technology transfer role as part of the wider programme being run by the council. Future research requirements The results suggest that there is a need to establish a network of 5-7 carefully chosen monitored sites, to give reasonable coverage of the country, which could be used to further refine both the input data and in the longer term the form of the models. Such sites would cost around 3000 /year each to run. The potential benefits from such information are large, allowing much better use of grazed grass and planning of conservation cuts, thus reducing feed costs for dairy farmers. Conclusions The inclusion of current yield data led to substantial improvement in prediction There is substantial between site variation that cannot be explained by the factors included in this study, for example differences in soil type. There is a need for more site specific information as indicated by the differences in the growth curves for the four sites (Figure 3). There is a need to establish a network of 5-7 carefully chosen monitored sites, which could be used to further refine both the input data and in the longer term the form of the models.

13 Appendix: Weekly meteorological and growth data from 4 sites, March-October 1998 Week beginning Mean air temperature (ºC) Total rainfall (mm) Growth rate (kg/ha) North Trawscoed Headley Bridgets North Trawscoed Headley Bridgets North Trawscoed Headley Bridgets Wyke Hall Wyke Hall Wyke Hall 3/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /