Precision yield measurement for more economic management, especially in dry years
Precision farming is currently regarded as the most promising agronomic approach to improve sustainable crop production. The approach focuses on making the right choices to optimise yield and quality, while reducing the impact on the environment and also to help the producer in identifying and quantifying risk.
There are, of course, several aspects to precision farming such as soil classification, grid-based soil sampling, variable rate fertilisation, variable rate seeding, yield monitor data, remote sensing, etc. These different aspects all represent ways of measuring soil, crop and management aspects in order to continuously improve economic sustainability, as well as lowering the impact on the environment.
This article covers the economic aspects of the management zone and risk model OmniZone™ and OmniRiskIQ™ respectively, which were introduced in a previous edition of the Nutriology® Newsletter. The information should be read in the context of the prevailing drought, where risk quantification and management are of critical importance for survival. In order to refresh your memory, we will briefly explain the principle of OmniZone™ and OmniRiskIQ™, followed by examples of how these models can be used to specify profitability and also to identify and manage economic risks.
OmniZone™ and OmniRiskIQ™
Yield monitors measure several variables and link them to their spatial position in a given field by means of GPS technology. The most important parameter is, of course, the amount of grain harvested as yield is the integration of all the facets that had an impact on a specific season. Most producers only look at the yield monitor as the harvester moves across the field, just to be able to remember the maximum yield harvested on the day. There is, however, so much more that a producer can get out of this information that will simplify his future decision-making and increase his productivity and efficiency as a result.
An employee of a well-known American company once said: “The information that this equipment gathers these days, is worth much more than the equipment itself”. Therefore, the information collected by the yield monitors should be processed properly so that key parameters can be calculated to support management decisions. One of these parameters or derivatives is the management zones obtained by comparing the relative yields over a number of seasons. Even though this information is historical in nature, it can serve as a foundation on which future decisions can be based.
The OmniZone™ map (Figure 1) can be used to make effective fertiliser recommendations. But now the question is: What should my planned yield per zone be for the coming season? This is where decisions regarding risk have to be made and where the OmniRiskIQ™ model can greatly assist the producer. OmniRiskIQ™ (Figure 2) is a model unique to Omnia Nutriology® that plots the cumulative probability/ certainty of a specific yield per management zone on a graph by looking at what happened during the previous seasons.
If the producer decides to aim for a yield that, according to historical data, has a probability of 50% of being realised, then the yield target for the “Far below average” yield zone (red) will be 3.24 t/ha, 5.38 t/ha for the “Below average” zone (orange), 6.85 t/ha for the “Average” zone (yellow), 7.62 t/ha for the “Above average” zone (light green) and 8.34 t/ha for the “Far above average” zone (dark green). If the producer wishes to follow a more conservative approach because of insufficient soil moisture and would rather fertilise according to a yield that offers a certainty of 70%, the yield target would move to 2.09 t/ha, 3.75 t/ha, 5.57 t/ha, 6.85 t/ha and 7.65 t/ha for the respective zones. The more information (seasons) used in this analysis, the more seasonal variation is taken into consideration to better quantify the risk. This information will assist farmers in preventing under-fertilising on high-potential areas and over fertilising on low-potential areas – in other words, the right product, at the right time, in the right place, at the right rate.
In this example, we could see how the OmniZone™ concept assisted the farmer by spacially identifying how and where yield risk is distributed over a certain farm. The OmniRiskIQ™ model subsequently illustrated the various yield risks on the basis of yield and the prospect/probability of realising it in a specific management zone. Everything that happened up to now in the planning phase is well and good, but it still doesn’t take the prevailing economic aspects into consideration. This is surely one of the most determining factors in crop production. When the economic aspects are taken into consideration, the producer can calculate profitability per management zone and make final adjustments to the planning. Two pratices will be compared: variable fertilisation (according to management zones) and conventional fertilisation (based on average yield per farm). In the example used in this article, the average yield for maize over the last five years points to 6.85 t/ha. This will be the target yield for the conventional fertilisation, whereas the target yield for the variable fertilisation will be calculated by using OmniZone™ and OmniRiskIQ™. These two management strategies for maize will be compared by using two grain prices and different input costs (Grain SA, 2015), where applicable.
Figure 3 shows the margins for every management zone if all the management zones are fertilised using the same rate (conventional). The fertiliser rate was based on a yield of 6.85 t/ha across the entire farm. The total costs associated with this yield amounted to R14 245/ha or R2 080/tonne (Grain SA, 2015). The grain price, which was used to calculate income, was R2 400/tonne in the farmer’s pocket, in other words a Safex price of ± R2 650/tonne, delivered in July. The black line on the graph represents the breakeven point. The coloured lines represent the actual yields converted to income per hectare. Any part of a coloured line that lies to the left of the breakeven point, means that losses will be incurred at those yields at the given input cost and grain price. Therefore, where the breakeven line and the specific management zone cross, will be the minimum income that should be earned in that particular management zone to start making a profit. Where these two lines cross, a straight line can be drawn to the Y-axis in order to obtain a percentage. This percentage is an indication of the probability/prospect of making a profit in a specific zone, given the fixed input cost of R14 245/ha and a R2 400/tonne maize price (farmer’s price).
Figure 3 therefore shows that the “Far below average” zone (red) has a 15% probability, the “Below average” zone (orange) has a 42% probability, the “Average” zone (yellow) has a 66% probability and the “Above and Far above’’ zones (light and dark green) have an 87% and 100% probability respectively to generate a profit at the specified input cost and grain price. The OmniRiskIQ™ model identifies the level of risk of each management zone. This can assist the farmer to possibly choose a more conservative target yield, which will be more profitable at the current input cost or at a lower grain price.
Figure 4 indicates the margins where the input costs were kept at the same level for the 6.85 t/ha fertilisation, but where the grain price was calculated at R2 700 in the farmer’s pocket. The higher grain price increased the probability of making a profit in the “Far below average zone (red) from 15% to 21%, the “Below average” zone (orange) from 42% to 52% and the probability of the “Average” zone (yellow) to show a profit moved up from 66% to 74%. The “Above average” and “Far above average” zones both showed a probability of 100% to generate a profit. The OmniRiskIQ™ model also indicates that a R300/tonne increase in the grain price increased the profitability of the farm by between 6 and 10%. In this way, the target yield of 6.85 t/ha can also be adjusted in the OmniRiskIQ™ model to suit the farmer’s risk appetite.
In the case of variable fertilisation, the input cost (Grain SA, 2015) is calculated per management zone. These management zones will also be tested against grain prices of R2 400/tonne and R2 700/tonne. Figure 5 again shows the margins that can be reached given specific input costs and a grain price of R2 400/tonne in the farmer’s pocket. In this case, the probability of the lower yield zones to generate a profit, is considerably higher than in the case of the conventional fertilisation approach. The reverse also applies, as in the case of the higher yield zones where the conventional approach stood a better chance to generate a profit, because the input costs were calculated according to the conventional approach at a much lower yield target of 6.85 t/ha, while the input costs for the variable fertilisation was calculated at yield targets of 7.62 t/ha and 8.34 t/ha (see Table 1).
However, it is the lower yield zones that create a better opportunity for risk management, as these are the areas where greater financial losses are suffered. The higher yield zones are more of an opportunity for the producer than a risk.
Figure 6 shows the margins where the input costs for the different management zones were kept the same, but the grain price increased to R2 700 in the farmer’s pocket. The change in grain price increased the probability of the “Far below average” zone (red) to make profit from 38% to 44%, the “Below average” zone (orange) from 52% to 60%, the “Average” zone (yellow) from 65% to 74% and the “Above average” zone (light green) from 80% to 90%. The “Far above average” zone (dark green) show a 100% probability of generating a profit, whilst before the grain price adjustment, it was 90%.
By using different scenarios in the OmniRiskIQ™ model, planning and risk management can be carried out. Every season should be carefully considered, especially under the prevailing harsh drought conditions (November 2015). The risks and opportunities that the particular season might offer, should be accurately identified and managed to the benefit of the producer, according to his specific circumstances and risk appetite. Speak to your Omnia agronomist. Maybe your historic yield data can add real value to your business by making use of the OmniRiskIQ™ model.
By Tiaan Terblanche