Using crop simulation models to develop treatment

Aspects of Applied Biology 50, 1997 Optimising cereal inputs: Its scientific basis

Using crop simulation models to develop treatment maps in precision agriculture By R B MATTHEWS and ND COSSER School of Agriculture, Food and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK

Summary Preliminary results from experimental work indicate that rooting depth is an important factor causing spatial variation in crop yields. To investigate how a knowledge of spatial variation in soil depth could be used to optimise the use of resources in a precision farming context, the CERES-Wheat simulation model was used to derive response curves of grain yield to nitrogen applied for a range of soil depths for a number of years. Distribution of applied nitrogen across a field was then optimised by reiteratively maximising the marginal responses to incremental units of fertiliser, with the goal of minimising the amount of fertiliser required to obtain the same yield that would be obtained from a uniform application. There was a wide variation in the predicted curves describing the yield response to applied nitrogen in different seasons, reflecting the influence of environmental variables, particularly rainfall. Optimisation analysis, in which overall yields were maintained at the level attained with a uniform application, suggested that little fertiliser (-2%) could be saved by variable rates of application in a relatively wet year, but that larger savings (-25%) were possible in a dry year. The optimal distribution was when more fertiliser was applied to the parts of the field with deeper soils, where the potential yield was greater, and less to the shallower parts of the field. The approach demonstrates the possibility of using crop simulation models to predict yield response curves for different regions of a variable field to aid in the development of treatment maps for variable rate applications of specific inputs. Keywords: Yield, soil depth, fertiliser, simulation models, rooting depth

Introduction The concept of precision farming is based on the use of spatial information at a sub -field level to help farmers make better informed management decisions to achieve their goals. Significant technical advances have been made in measuring and displaying variation in crop yields across a field, but it is not always clear how to determine the best management practice for each part of a field in order to achieve a particular goal. For example, does a fanner apply more fertiliser to the lower yielding areas to try and raise yields to the average, or are these low -yielding areas at their potential yield already, and would he therefore be advised to apply more to the high yielding areas on the basis that they are able to make better use of it? Similarly, are the optimal fertiliser application rates the same for all parts of the field year after year, or do they vary in response the changing environmental conditions between years?

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Answers to these questions are not clear-cut, but can be found using optimisation techniques based on a knowledge of the marginal yield response to a unit of the particular input for all the different regions of a field. The marginal yield response can be obtained from the curve describing yield response to the level of a particular input for each homogeneous region of the field. However, in most cases the response curves of each of these regions are unlikely to be known. While mini -experiments on each homogeneous region with a range of application levels of the input will provide this information, this approach is time-consuming, labour-intensive, and results are likely to be specific to that field only. Moreover, if responses change from year to year, then these experiments would have to be repeated every season. An alternative, and perhaps complementary, approach is to use crop simulation models to predict the likely yield response to different levels of a particular input. Such models offer a cost-effective way in which agronomic knowledge accumulated from numerous previous experiments, usually with treatments of uniformly applied inputs on small and relatively homogeneous plots, can be extended to larger spatially -variable fields. Matthews & Blackmore (1997) have suggested a theoretical framework (Table 1) to analyse spatial variability in crop yields based on the hierarchical 'crop production levels' identified by Penning de Vries, Jansen, ten Berge & Bakema (1989). At the first level, the potential production of a crop is assumed to be limited only by solar radiation, temperature, daylength, and varietal characteristics. Factors such as the topography of a field will influence the degree of spatial variability in the first two of these. The second production level is when water limits production; factors that will influence spatial variability of this level are soil water release characteristics, soil and rooting depth, and possibly variations in evaporative demand due to differences in temperature across a field. The third production level is where nutrients (particularly nitrogen) are additionally limiting; factors influencing this level are those causing variation in soil organic nitrogen content, rates of N transformations in the soil, and crop N uptake. Nitrogen may not often limit yields since it is applied by the farmer, but responses to N will depend on the spatial variation in these factors. The fourth production level is where pests, weeds and diseases are taken into account; there may be considerable spatial variation in these, although the causes of this variability are not well understood. A major project in the UK funded by the Home Grown Cereals Authority (HGCA), Hydro (Agri) and Massey Ferguson was initiated in 1996 at Cranfield University, Silsoe, in conjunction

Table 1. A suggested framework for analysing the factors causing spatial variation in crop yields following a hierarchical system of production levels. Starting at production level I, the factors controlling spatial variation are analysed in turn until a satisfactory proportion of the total variation can be explained Production level

Controlling environmental variables

Factors causing spatial variation

I. Potential production

temperature, solar radiation, CO2

slope, aspect

2. W ater limited

soil water characteristics, solar radiation, temperature, rainfall, humidity, evaporation, wind

slope, aspect, soil type & depth, runoff, shelter, compaction, irrigation

soil characteristics: temperature, nutrient levels, water content, 02, pH

slope, aspect, soil type, crop residues, fertiliser

temperature, wind, humidity, soil water, soil nutrients, chemical environment

slope, aspect, position in field, shelter, crop residues, spraying

production

3. Nutrient limited production

4. Production limited by weeds, pests & diseases

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with Arable Research Centres (ARC) and Shuttleworth Farms, to develop methodologies to use spatially variable field information to help farmers make better informed decisions. Field exper i ment s ha ve been est a bl i shed a t Shu t t l ewor t h a nd H ou ght on C onqu est , bot h i n Bedfordshire, and Andover and Cirencester. Initial resu lts from these experiments have indicated that rooting depth, as influenced by soil depth, is an important factor causing spatial variation in crop yields. In this study, therefore, we consider only the effects of variation in soil depth on crop yields, and how this variation can be managed to improve the efficiency of resource use. We recognise that many other interacting factors besides soil depth are also likely to be influential, but it was decided to focus on a single factor initially in order to develop a methodology, and to consider additional complexity in future studies. We have also focused only on the responses of yield to applied nitrogen, as N is a major input that the farmer has control over, and technology exists to apply N at spatially variable rates. It is therefore of interest to the farmer to know how its application to a field may be optimised. Our approach is to use a wheat simulation model to predict the yield -fertiliser response curves for a range of soil depths in different years. We then use this information together with optimisation techniques to produce recommended fertiliser application rates for each area of the field, with the goal of reducing the amount of fertiliser used while maintaining the overall yield achieved with a uniform application.

Methods Experimental measurements Results from only two of the experimental sites are presented; the first on a Cotswold brash soil near Cirencester in Gloucestershire, and the second on a clay soil near Houghton Conquest in Bedfordshire. Both sites had been in continuous winter wheat for several years before the start of the experiment. In the 1995/96 season, winter wheat was again sown at both sites; the first on October 9th, 1995 with the cv Brigadier, and the second on October 14th, 1995 with the cv Riband. The crops were harvested in mid -August the following year. Nitrogen was applied in April at 200 and 160 kg N respectively. After harvesting, between five and nine profile pits were dug to a depth of 1.5 metres in each

field. Their positions were based on the results of an initial auger survey, aerial digital photography images, and yield maps from 1995 and 1996, particularly with respect to low- and high -yielding areas. The pit faces were carefully examined to determine mean terminal rooting depth measured from the soil surface. The model

The CERES-Wheat model (Godwin, Ritchie, Singh & Hunt, 1989) was used to develop the yield response curves. CERES-Wheat is part of the DSSAT (Decision Support System for Agrotechnology Transfer) software package produced by the International Benchmark Sites Network for Agrotechnology Transfer (D3SNAT) Project (Harrison, Thornton & Dent, 1990), and has been widely tested in many parts of the world, providing some degree of confidence in its predictions. The model operates on a daily time step and simulates the main processes of crop growth and development, including the timing of phenological events, the development of the canopy to intercept light, and its use to fix carbon which is converted into dry matter. Potential growth rates may be modified by shortages of water or nitrogen, but the effects of other nutrients, pests or diseases are not currently considered. The model is capable of simulating the growth of both

winter wheat (requiring vernalisation) and spring wheat, and can also take into account

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photoperiodic sensitivity. Seven variety -specific parameters are used; three of these control rates of phenological development (PIV, P1D and P5), the fourth (G1) determines the number of grains set per plant, the fifth (G2) the maximum rate of grain -filling, the sixth (G3) the number of ears per plant, and the seventh (PHINT) describes the rate of leaf appearance. Daily solar radiation receipts, minimum and maximum temperatures, rainfall, humidity and wind speed are required as inputs; information must also be provided on soil water release characteristics, soil depth and bulk density. Crop management details, such as time of planting, population density, and timing and amount of fertiliser applications (if any), are also necessary. The date of harvest is determined automatically by the model. Developing yield response curves The model was used to simulate the yield response of cv Avalon winter wheat to applied nitrogen. Some of the genotype parameter values supplied with the model for this cultivar have been found to require some modification (Porter, Jamieson & Wilson, 1993); we have used these modified values, and have additionally changed the G1 parameter from 3.0 to 4.0 to match observed grain numbers, so that the values used were as follows: P1V=6.0, P1D=3.6, P5=7.0, G1=4.0, G2=2.7, G3=1.7, and PHINT=95. Weather data collected at the Silsoe College weather station (latitude 52°N, longitude 0°E, altitude 60 m) for the period 1989 to 1996 were used. A summary of the weather data over the whole growing season and grain -filling periods for each yea r is given in T a ble 2 . T he soil pa ra meters su pplied with the model for Rotha msted Experimental Station were used; available water in this soil was 153 mm nfl in the top 155 cm of the profile, comparable to many soils on which wheat is grown in the UK. In the simulations, the crop was sown on October 10 of each year, and harvested when the model had determined that physiological maturity had been reached, which ranged from July 21 to August 5. Eight levels of nitrogen fertiliser as ammonium nitrate were applied in a single application on April 17 each year at rates of 0, 30, 60, 90, 120, 150, 180, and 210 kg N had. It was assumed that rooting depth was limited by the soil depth; this was adjusted by removing successive soil layers from the existing soil parameter data in the SOIL.SOL file, giving six soil depths of 25, 45, 65, 95, 125 and 155 cm. Optimising fertiliser application

A hypothetical 60 ha field was used with its total area being classified into six homogeneous regions representing the six simulated soil depths. For the purposes of this analysis, the areas of

Table 2. Summary of weather data during the growing season and grain -filling phase for each year used in the simulations

Year

1989/90 1990/91 1991/92 1992/93 1993/94 1994/95 1995/96

Rainfall (m m ) 426 453 478 501 467 450 339

W hole growing season Mean temp. Solar radiation (°C) NJ m-2) 9.3 8.3 8.4 8.3 8.3 9.2 7.9

2950 2790 2596 2754 2866 2884 3040

Rainfall (mm) 29 90 110 63 23 24 37

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Grain -filling phase Mean temp. Solar radiation (°C) (mr 111-2) 16.1 16.9 15.9 14.6 17.6 17.7 17.0

736 672 654 801 717 695 679

each region were assumed to be equal (i.e. 10 ha), but it is recognised that the distribution will vary widely from field to field. The optimising program described by Matthews & Blackmore (1997) was used to determine the optimal distribution of fertiliser over the field to minimise the total amount applied without affecting the overall yield. Briefly, optimisation was achieved by taking a unit of fertiliser from the total amount available and applying it to the part of the field where the marginal response was the greatest. Thus, fertiliser would be applied first to the region which gave the greatest initial yield response, but as more and more fertiliser was applied to the region, the marginal response would decline until such time as it was lower than the initial marginal response for another region, to which further units would be applied. This was done reiteratively until the specified total production from the field was reached. The size of the unit of fertiliser used could be varied, but 1/1000th of the total amount to be applied to the field was found to work well in most cases. The baseline for comparison was the yield obtained by applying fertiliser at the rate of 100 kg N ha-1 at a uniform rate across the whole field.

Results Effect of soil and rooting depths on crop yields The observed relationship between crop yield and rooting depth for the two experimental sites is shown in Fig. 1. There is an increase in yields from 5500 kg ha-1 as rooting depth increases until 100 cm, beyond which there is no further response past 8500 kg ha-i. There was a good

10000 8000 'co

-c 6000 -o 4000 ▪ Andover

• Houghton Conquest — Simulated

2000 0

1

0

50 I

100 I

150

200 1

,________________________

rooting depth (cm) Fig. 1. Relationship between rooting depth and wheat yields (kg hi') for each of

the two experimental sites in the 1995/96 season. The line represents the relationship between yield and soil depth.simulated by CERES-Wheat using 1992 weather data from Silsoe College, genotype parameters for cv Avalon, and with N applied at a rate of 120 kg N

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agreement between this response and that simulated for yield and soil depth by the CERESWheat model using 1992 weather data for Silsoe College, genotype parameters for the wheat cultivar Avalon, with 120 kg N ha-' being applied. Weather data from Silsoe was used as it was not available for the experimental sites, but as the average rainfall at Andover is about the same at that in a wet year at Silsoe, it was hoped that data for 1992, the wettest year between 1989 and 1996, was a reasonable substitute. Yield response curves There was a considerable variation in yield response curves between years, even for the same soil depth, shown in Fig. 2 for the 95 cm depth. In the wet years (e.g. 1991/92 and 1992/93), there was a response to applied N up to a rate of about 100 kg N ha -I, beyond which there was a levelling off. In some wet years (e.g. 1993/94), there was even a decline at higher levels of N due to a shift in the partitioning of thy matter away from the roots to the shoots, resulting in increased drought stress if rainfall was low towards the end of the season. In the dry years, the response to N was much less, and appeared to plateau at a higher rate. There was also an interaction between soil depth and year, with proportionately less variation between wet and thy years at the greater soil depths due to their ability to store more water. In general, there was a good relationship between the plateau yield and the total rainfall over the growing period (r = 0.8; N = 7; P