How far might medium-term weather forecasts improve nitrogen fertiliser use and benefit arable farming in the England and Wales?

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1 Agriculture, Ecosystems and Environment 117 (2006) How far might medium-term weather forecasts improve nitrogen fertiliser use and benefit arable farming in the England and Wales? A.G. Dailey a, J.U. Smith b, A.P. Whitmore a, * a Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK b Department of Plant and Soil Science, University of Aberdeen, UK Received 17 August 2005; received in revised form 12 February 2006; accepted 2 March 2006 Available online 18 April 2006 Abstract Because of the interest in the value of medium-term weather forecasts to UK agriculture, we attempt to quantify this value for N fertiliser use with arable crops. Model systems such as SUNDIAL provide arable N fertiliser advice by modelling the N supply from soil, but poor knowledge of future weather reduces accuracy. Aweather generator was used to produce sets of simulated weather of a range of accuracies and durations, for 10 regions in England and Wales. In a series of computer simulations, we tested the effect of prior knowledge of weather following the date of N fertiliser application on the efficiency of use of applications using SUNDIAL. The changes in N leaching, denitrification and crop N uptake due to the forecast quality were calculated. Yield and gross profit changes were estimated from N uptake, for the arable industry in England and Wales. Changes in losses were small. With a perfect forecast, there was a small decrease in leaching (approximately 1 kg N ha 1 ), and still less change in denitrification. The increase in crop uptake due to a perfect 27-week weather forecast was 6 kg N ha 1, and the increase in farm profit in England and Wales amounted to 68M per annum. With more accurate forecasts, the system can reduce the risk of under-application of N. A perfect 3-week forecast would increase uptake by an average of 2 kg N ha 1, and increase profit nationally by 23M per annum but have negligible impact on losses. These improvements appear to be systematic and could be expected to be achieved with any recommendation system that makes explicit use of post-application weather. # 2006 Elsevier B.V. All rights reserved. Keywords: Nitrogen management; Nitrogen losses; Economic value; Arable farming; Computer model; Optimised fertiliser advice 1. Introduction Farming is under diverse pressures: on the one hand there are environmental norms to be met (Pretty et al., 2001), on the other hand profitability has declined dramatically (Defra, 2003). Nitrogen fertiliser applications are an extremely difficult issue that farmers must balance between these two pressures. Nitrate leaching into ground and surface water must be kept below tight limits in order to comply with EU water quality legislation (Anon, 2000). Loss of nitrous oxide (N 2 O) through denitrification has serious consequences for global warming (IPCC, 2001). However, most crops respond * Corresponding author. Tel.: ; fax: address: andy.whitmore@bbsrc.ac.uk (A.P. Whitmore). readily to nitrogen and it is a relatively cheap input. Even though losses of N (leaching or denitrification) increase rapidly with applications of N beyond the physiological optimum, farmers have tended to over apply N (e.g. Sylvester-Bradley et al., 1987; Whitmore and Van Noordwijk, 1995) because the consequence of applying too little was a large loss of farm yield (and income) whereas a major consequence of applying too much is off-farm diffuse pollution that has little if any cost to the farmer. Systems such as SUNDIAL (Bradbury et al., 1993; Smith et al., 1996) are intended to optimise N fertiliser advice for farmers by taking full account of the N that can be supplied from soil, including mineralisation that occurs postapplication. In this way, yields can be maximised and nitrogen pollution minimised. In general, SUNDIAL has /$ see front matter # 2006 Elsevier B.V. All rights reserved. doi: /j.agee

2 A.G. Dailey et al. / Agriculture, Ecosystems and Environment 117 (2006) been shown to agree well with RB209 (MAFF, 2000), the current Defra-sponsored recommendation system for N (ADAS, 2005). Predictions from SUNDIAL, however, are dependent on the estimates of the weather post-application and in general the estimated and actual weather will differ. Thus the fertiliser advice may turn out to be less than optimal. If too much N is applied, losses to the environment may increase. Under application may result in a shortfall in yield and profit. There is potential for medium-term weather forecasts to be made available to the farming community and it seems likely that such information might improve the accuracy with which farmers can obtain site and season-specific fertiliser advice using fertiliser recommendation systems such as SUNDIAL. In the UK, an exercise known as Foresight (Anon, 2001) has tried to suggest the value of research into new areas that could benefit the economy. One aspect of this was whether or not it could be worthwhile developing a capability for medium-term weather forecasts of two or three months (Anon, 2001). The objective of this article is to quantify the potential of medium-term forecasts to improve the accuracy and benefit of nitrogen fertiliser recommendations using SUNDIAL as an example. The analysis carried out in this project reflects the avoided loss of yield or reduction in loss of N (denitrification or leaching) that can come about with increasing prior knowledge of weather if SUNDIAL is used to provide fertiliser advice. It is with this standpoint in mind that we examined the potential of medium-term weather forecasts to reduce nitrogen pollution and to contribute to the farming economy in England and Wales. 2. Materials and methods SUNDIAL, in common with other N recommendation systems, suggests a level of N application on the basis of assumptions about the weather between application and harvest. Unlike most N recommendation systems, however, SUNDIAL does this explicitly with a sequence of expected weather. This expected weather is nonetheless an assumption, so by repeating the simulations to harvest with sequences of weather that differed from the expected weather in a predetermined manner (see below), we could make the expected weather represent a range of forecasts of different accuracies and durations The weather generator A weather generator (Dailey et al., 2005) was used to produce series of weekly values for rain, evapotranspiration (ET) and the weekly mean of daily mean temperature (T) required by SUNDIAL. The generator is controlled by characteristic parameters that describe the weather for a particular location, such that the data generated is statistically similar to observed weather at that location. The parameters describing rainfall were derived from 95 weather stations in England and Wales, each having at least 20 years of data. The parameters describing ET and T were derived from a subset of 34 sites reporting ET and T for 20 or more years (Dailey et al., 2005). Two sets of weather data were generated. The first series represents expected weather, which is the weather used to optimise SUNDIAL at the time a decision on fertiliser application needs to be made; the second series, which deviates from the first, represents realised weather. These terms, expected and realised weather will be used throughout. The extent of the deviation of the realised series from the expected is specified by a parameter, w, which has a value between 0 and 1. A value of 1 signifies no deviation of the realised weather from the expected; in other words the expected weather is a perfect forecast. A lower value of w signifies that the expected weather, used in SUNDIAL, is a forecast of correspondingly lower accuracy. By increasing w over the range of 0 1, prior knowledge of the weather is increased from zero to complete knowledge. Reliability of prior knowledge is one issue, however. The other is the duration of the reliability of that prior knowledge. We examined the effect of having prior knowledge of a given accuracy (w = 0 1) for durations of 1, 3, 5, 7, 9 and 27 weeks. In order to assess the benefits of prior knowledge of weather, we generated 80 different sequences of weather corresponding to 10 different weather stations in England and Wales Generation of rainfall The weather generator for a particular location is based on a cumulative density function (CDF) of weekly amounts of rain for each of the 13 4-week periods of the year. This generator (Dailey et al., 2005) is of a kind sometimes known as a Richardson type (Richardson, 1981). A rain amount is generated by first producing a random number (a uniform deviate in the range 0 1), and transforming to rainfall amount using the CDF. Without further modification the transformed series would be expected rain. The random series, R r, used for realised rain is given by R r ¼ R e þ R i ð1 Þ (1) where R e is the random series used for expected rain and R i is a different, independent random series. R r is constrained to the range 0 1. Persistence is implemented as a first order Markov chain (Gabriel and Neumann, 1962) based on the observed probability of 0.57 across the UK that two consecutive weeks are both dry or both wet (Dailey et al., 2005). The resultant random numbers are transformed to rain amounts using the CDF of rainfall at the weather station of interest Generation of ET and T The long-term weekly amounts and standard deviations of evapotranspiration (ET) and weekly means and standard

3 24 A.G. Dailey et al. / Agriculture, Ecosystems and Environment 117 (2006) deviations of temperature (T) at a particular location are each parameterised as a single-term Fourier series. For temperature this is T t ¼ a sinðbtþ (2) where T t is the seasonal mean temperature at time t, and a and b are constants. A process that generates a series of residuals for ETand T was suggested by Matalas (1967), and was modified in our weekly weather generator (Dailey et al., 2005) to provide a description of the correlation between, and the persistence of, temperature and evaporation. There is, for instance, an increased probability that a warmer than average week will coincide with above average evapotranspiration, and be followed by another warm week. The expected weather is first generated by adding these residuals to the Fourier series (2) as described by Richardson (1981). To generate the realised weather, a new series of residuals is generated that tracks the original series, but deviates from it by a random amount with an average magnitude dependent on w. The realised ET and T are calculated by adding the new series of residuals to (2). Full details and testing of the weather generator and of its evaluation can be found in Dailey et al. (2005) SUNDIAL The SUNDIAL Fertiliser Recommendation System (SUNDIAL-FRS, Smith et al., 1996) uses a computer simulation model (Bradbury et al., 1993) of N turnover in soil to estimate the supply and loss of N to a number of arable crops. For this work, SUNDIAL used potential yield and weather data for the location to simulate up to the date at which fertiliser is to be applied. Then using the expected weather for the period up to harvest, the system optimised the amount of fertiliser that should be applied for the expected conditions. A second series of simulations was then performed using this same fertiliser recommendation. This time, however, realised weather for the period following fertiliser application was used with different degrees of prior knowledge (achieved by setting w to 0.1, 0.2, 0.3, 0.5, 0.7) and different durations of that prior knowledge of 0, 1, 3, 7, 9 or 27 weeks. For the remainder of the simulation until harvest, realised weather data that represents no prior knowledge was used. For each simulation, results for leaching, denitrification and crop uptake of nitrogen were obtained. Three soil types, that are the default soils in the SUNDIAL model, were used in this project: sand, loam and clay containing 10, 23 and 40% clay, respectively. Ten weather stations having at least 20 years of records were chosen for the simulations to represent wet, moderate rainfall and dry regions and warm, moderate or cool conditions (Table 1). These ten stations represent the range of conditions found in England and Wales and are geographically spread throughout the countries. We took the nine Government Office Regions of England (e.g. Defra, 2002), and separate data for Wales. However, we combined the London and the South East regions, giving nine in all. One, or in the case of the South West two, appropriate weather stations were allocated to represent each region. The regional results were then combined according to climate into five general categories (Table 1). Results were weighted by the extent of cropping in each of the regions. Four crops were investigated to represent major classes of arable crops grown in England and Wales: (i) a winter cereal, winter wheat; (ii) a spring sown cereal, spring barley; (iii) a high nitrogen residue crop winter oil seed rape (osr); (iv) a shallow-rooting crop, potatoes. The potential yields and dates of establishment and harvest of the crops used in the simulations are shown in Table 2. Winter oilseed rape had a 30 kg dressing of fertiliser nitrogen at drilling. For all simulations, the crop preceding the crop of interest was winter wheat. The total application of fertiliser nitrogen to the preceding crop was 160 kg for sand, 220 kg for loam and for clay in wet or moderate rainfall locations, and 180 kg for clay soils in dry locations. These recommendations were taken from current fertiliser advice, RB209 (MAFF, 2000), and thus represent the actions likely to be taken by local farmers. SUNDIAL does not predict yield, but if the potential uptake is reduced for any reason, yield is reduced. This loss of yield and losses to the environment by leaching and denitrification are the yardsticks by which we assess the value of access to medium-term weather forecasts. As a context in which to interpret these losses, Table 3 gives the results from the baseline simulations using the expected weather Estimation of economic benefits The benefits to the farmer in terms of avoided loss of yield were calculated using the following sources of Table 1 Climate regions used in the simulations Climate Temperature (mean annual 8C) Rainfall (annual mm) Corresponding Defra regions Cool wet Wales and North West Cool dry North East, Yorkshire and Humberside Moderate East and West Midlands Warm wet South West Warm dry East and South East and London Data from two weather stations representative of each region was used. Area cropped to the crops used in this study (ha)

4 A.G. Dailey et al. / Agriculture, Ecosystems and Environment 117 (2006) Table 2 Potential yield (tonnes ha 1 ) and dates of establishment and harvest of the crops used in the simulations Crop Potential yield Dates Area of crop in England Sand Loam or clay Establish Harvest and Wales (ha) Winter wheat October 12 August Spring barley February 12 August Winter oilseed rape September 5 August Main potatoes April 30 September Table 3 Mean simulated amounts of nitrogen (kg N ha 1 ) by soil and region applied as fertiliser, lost, or taken up by the crop Crop information. Product prices for April 2002 were obtained from Farmers Weekly (5 April 2002); nitrogen and dry matter content for oilseed rape from A.J. Macdonald (RRes, personal communication); crude protein and dry matter contents for other crops from Agro Business Consultants (1998); crop areas for England from Defra (2002); crop areas for Wales, year 2001 from John Bleasdale, Agricultural Statistics, Welsh Assembly Government (personal communication). Results are expressed, in general, as the benefit from having prior knowledge of a given accuracy of the weather forecast. The benefit has been quantified as avoided loss of crop offtake, avoided leaching or denitrification (kg N ha 1 ), or as the avoided loss of gross profit to the arable industry in England and Wales (pounds sterling for the total cropped area). 3. Results and discussion 3.1. Losses of N Fertiliser applied Denitrification Leaching Crop uptake Winter wheat Spring barley Winter oilseed rape Potatoes Increased prior knowledge had a small direct environmental benefit by decreasing the amount of N leached and of that denitrified before harvest. We expected these changes to be small as only a small proportion of these losses occurs ordinarily during the summer. The reduction in losses by denitrification was not statistically significant at any location, the biggest reduction being 0.2 kg (data not shown). With perfect knowledge of the weather for three or more weeks into the future, denitrification was reduced by an average of 0.1 kg N ha 1 only. The mean decrease in leaching of nitrogen due to the maximum level of prior knowledge was 1.2 kg N ha 1 calculated over all soils, sites and crops. The wetter areas benefit most, having the largest decreases in leaching with increased prior knowledge (Table 4). The winter crops experience the largest, and spring crops the smallest, reduction in leaching (Table 5). There is much variation between years but even in the most extreme circumstances the maximum level of prior knowledge did not decrease leaching by more than 5.4 kg N ha 1. These decreases in leaching are unlikely to be detectable by measurement against background losses Crop N offtake Crop N offtakes increased with forecast quality relative to offtakes obtained with no prior knowledge (Fig. 1). With knowledge of the weather up to 3 weeks in advance that is 60% accurate, an increase of 1 kg N ha 1 on average can be expected in crop uptake in England and Wales. A forecast that is 100% accurate 3 weeks into the future could push that gain up to 2 kg N ha 1. A 7 weeks forecast that is 70 or 80% reliable could increase N offtake (or rather, avoided shortfall in N offtake) by up to 2 kg N ha 1. These amounts are not large but are averaged, systematic effects over the 80 Table 4 Mean decrease in leaching due to perfect knowledge, compared with no knowledge of future weather between fertiliser application and harvest Climate region Mean decrease in leaching (kg N ha 1 ) Cool wet 1.5 * Cool dry 1.2 * Moderate 0.7 Warm wet 3.1 * Warm dry 0.8 * Weighted mean 1.1 * Significant at the 95% confidence level. Table 5 Mean decrease (kg N ha 1 ) in leaching due to perfect knowledge, compared with no knowledge of future weather between fertiliser application and harvest Crop Soil type Sand Loam Clay Winter wheat * 1.9 * Spring barley Winter oilseed rape * Potatoes * Significant at the 95% confidence level.

5 26 A.G. Dailey et al. / Agriculture, Ecosystems and Environment 117 (2006) Fig. 1. Change in crop N (kg N ha 1 ) offtake due to increase in prior knowledge of weather relative to no forecast. Durations refer to length of forecast, accuracy (w) is the reliability of that forecast. simulation years and represent a consistent benefit. There is variability between the simulation years so that in some years the benefit was more (see next section). With a virtually perfect forecast, we found little difference in benefit between soil types (range kg ha 1 ) but more between crops (spring barley 4.7, winter wheat 7.2 kg N ha 1, data not shown). Perhaps surprisingly there was little difference ascribable to the region and its weather (Table 6). This may be because our analysis compares differences in prior knowledge within regions of the same weather. Fig. 2. Shortfall or gain in N uptake by a crop of winter wheat in 80 separate realisations of weather, forecast with the specified accuracy and duration; (&) w = 1, duration 1 week; (*) w = 0.9, duration 27 weeks; (*) w = 0.5, duration 5 weeks; (&) zero accuracy. and 25 kg N ha 1 are roughly equivalent to a loss of half or one tonne ha 1 of cereal yield respectively. For consistency these same amounts of N were used as cut-off values in looking at oil seed rape and potatoes even though such amounts of N correspond to different amounts of yield in these crops. The larger number and magnitude of shortfalls in N offtake rather than gains in the results (Fig. 2) arises from the fact that advice from SUNDIAL is geared towards supplying fertiliser N below an upper bound (related to the expected yield) in order to minimise the losses to the 3.3. Variability between years Variability ensures that in some years the losses were greater or less. However, the effect of knowledge of the weather on the distribution of crop N offtake over the 80 years studied was somewhat skewed. Where there was no prior knowledge of the weather, shortfalls in N offtake relative to the SUNDIAL norm were frequent and sometimes large, whereas gains were rare and small (Fig. 2). Moreover, shortfalls were more frequent than gains. To make the consequences of this clear we have plotted the frequency with which losses of crop N offtake exceeded 12.5 or 25 kg N ha 1 (Figs. 3 and 4). The cut-off values of 12.5 Table 6 Mean benefit in crop offtake using a 27 week forecast at 90% accuracy Climate region Mean benefit (kg N ha 1 ) Cool Wet 6.6 * Cool Dry 5.7 * Moderate 5.9 * Warm Wet 7.0 * Warm Dry 5.9 * * Significant at the 95% confidence level. Fig. 3. Frequency with which weather forecasts for the specified duration and accuracy (w) would increase crop N offtake by 12.5 kg N ha 1 (0.5 tonnes cereal yield) relative to SUNDIAL s advice and with no prior knowledge of the weather (w = 0). Averaged over all crops and weather stations.

6 A.G. Dailey et al. / Agriculture, Ecosystems and Environment 117 (2006) Fig. 4. Frequency with which forecasts of weather for the specified duration and accuracy (w) would increase crop N offtake by 25 kg N ha 1 (1 tonne cereal yield) relative to SUNDIAL s advice and with no prior knowledge of the weather (w = 0). Averaged over all crops and weather stations. Table 7 Gross value ( M, through avoided loss of yield) of prior knowledge of weather for the specified duration and accuracy Weeks of Forecast accuracy (w) prior knowledge We employed 84% of arable land in England and Wales in our calculations to reach these values. The calculations take no account of the change in cost of chemicals (fertilisers and sprays) that might be needed to avoid this loss of yield. environment that come about with over-fertilisation. The main benefit of prior knowledge of weather is to reduce the number of occasions when lack of applied N depresses yields even though the risk of leaching or denitrification turns out to be small. Although the extra amounts of N acquired by a crop are relatively small in comparison to the amount of fertiliser applied (Figs. 3 and 4), one tonne per hectare is a significant contribution to farm yield and would probably be very profitable at this level. Taken over the whole of England and Wales, these benefits could be considerable. A forecast of 27 weeks (i.e. for the whole of the growing season following application) of 90% accuracy would be worth more than 54M (on an annual basis) to farmers on average (Table 7). In one of the extreme years referred to in Figs. 3 and 4 the amount of money could be even more. Even modest and more realistic levels of prior knowledge (say 3 or 5 weeks forecast of 50% reliability) could be worth 9 11M (Table 7). An increase in reliability to 70% might be worth 15 18M to the industry on average each year. Foresight (Anon, 2001) has suggested that forecasts of 2 3 months duration might be useful in many contexts. To arable crop farmers in England and Wales such forecasts, at an accuracy of 70%, could be worth over 23M each year on average in avoided loss of yield. This article deals with the potential benefits of mediumterm weather forecasts in terms of nitrogen and its effects only. There are other potential benefits. Forecasting of disease, pest and weed infestation might be improved and irrigation might be used more effectively and efficiently. It ought also become possible to schedule farm operations more efficiently with a potential saving in labour. These aspects are beyond the scope of the present article, however. These analyses are expressed in terms of the financial benefit to be gained from medium-term weather forecasts where farmers follow the advice given by SUNDIAL. As a corollary of this, our results suggest that there is currently a financial benefit from not under-applying N. Thus farmers are probably managing risk rationally when they consistently over-fertilise crops, e.g. Whitmore and Van Noordwijk (1995). Fig. 2 shows that there is little risk of financial loss from over-applying N but a risk of considerable loss of yield in some years from under-applying. There are benefits to be gained in some years by applying more nitrogen fertiliser than appears necessary based on expectation derived from long-term records. Occasionally these benefits can be quite large. The financial gains referred to in this report should be seen as potential benefits with respect to the use of nitrogen fertiliser within a framework of strict adherence to environmental constraints. In England, such constraints are now imposed on approximately 60% of the land area through the designation of nitrate vulnerable zones. In these areas it seems reasonable to expect mediumterm weather forecasts to have benefits of at least the magnitude suggested in this article. 4. Conclusions The benefit of reliable medium term weather forecasts following fertiliser application to water quality (nitrate leaching) or to greenhouse gas emissions of nitrous oxide from denitrification is unlikely tobegreat.nor,atfirstsight, do the benefits of medium-term weather forecasts to crop N offtake appear to be large. However, the distribution of crop responses found here is heavily skewed towards the risk of yield loss through failure to obtain sufficient N. In an industry that adheres strictly to a nitrogen prediction system to minimise losses of N, the benefits of even a limited weather forecast (three-week forecast that is 50% accurate) could be worth 9M per year (arable farming in England and Wales). A rather better forecast (27 weeks, 90% reliability)

7 28 A.G. Dailey et al. / Agriculture, Ecosystems and Environment 117 (2006) might be worth more than 54M per annum. The benefits presented in this article represent the extra value of yield in relation to nitrogen fertiliser only and make no attempt to assess the value of better pest and disease predictions or of the timing and scheduling of operations. Acknowledgements Rothamsted Research is grant aided by the Biotechnology and Biological Sciences Research Council. We gratefully acknowledge support from the UK Government Department of the Environment, Food and Rural Affairs (Defra) and useful discussions with Margaret Glendining. References Agro Business Consultants, Agricultural Budgeting and Costing Book No. 47. Agro Business Consultants Ltd., Leicestershire, UK. ADAS, Anon, Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for community action in the field of water policy. Off. J. L 327, Anon, Seasonal weather forecasting for the food chain. Final report. Department of Trade and Industry, London, UK. Available online at Bradbury, N.J., Whitmore, A.P., Hart, P.B.S., Jenkinson, D.S., Modelling the fate of nitrogen in crop and soil in the years following application of 15N-labelled fertilizer to winter wheat. J. Agric. Sci. 121, Dailey, G.J., Smith, J.U., Whitmore, A.P., Weekly weather generation for a nitrogen turnover model: getting representative results from fewer simulations. Nutr. Cycl. Agroecosyst. 73, Defra, Agricultural and Horticultural Census: 5 June 2002 England. Defra, UK. Defra, Agriculture in the United Kingdom. The Stationery Office, London. Farmers Weekly, 5 April 2002, Reed Business Information, Sutton, Surrey. Gabriel, K.R., Neumann, J., A Markov chain model for daily rainfall occurrence at Tel Aviv. Quart. J. R. Met. Soc. 88, IPCC, Climate Change: The Scientific Basis. Cambridge University Press, Cambridge, UK. MAFF, Fertiliser Recommendations for Agricultural and Horticultural Crops. RB209, 7th ed. The Stationery Office, London. Matalas, N.C., Mathematical assessment of synthetic hydrology. Water Resour. Res. 4, Pretty, J., Brett, C., Gee, D., Hine, R.E., Mason, C.F., Morison, J.I.L., Raven, H., Rayment, M., van der Bijl, G., Dobbs, T., Policy challenges and priorities for internalising the externalities of agriculture. J. Environ. Plan. Manage. 44, Richardson, C.W., Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17, Smith, J.U., Bradbury, N.J., Addiscott, T.M., SUNDIAL: a PC-based system for simulating nitrogen dynamics in arable land. Agron. J. 88, Sylvester-Bradley, R., Addiscott, T.M., Vaidyanathan, L.V., Murray, A.W.A., Whitmore, A.P., Nitrogen advice for cereals: present realities and future possibilities. In: Proceedings of the Fertilizer Society, vol. 263, London, pp Whitmore, A.P., Van Noordwijk, M., Bridging the gap between environmentally acceptable and agronomically desirable nutrient supply. In: Glen, D.M., Greaves, M.P., Anderson, H.M. (Eds.), Ecology and Integrated Farming Systems. John Wiley and Sons, Chichester, UK, pp