Plant nutrition under drought conditions; with special reference to grain production in central South Africa
Mother Nature has yet again dealt South African agriculture a severe dry hand. In some central areas it is the fourth season of below average rainfall. This 2015-2016 season is now officially under the firm grip of the El Niño phenomenon with its harsh consequences. At the time of writing this article, it has been officially declared that this is the worst drought in the central areas since 1991, in KwaZulu-Natal since 1973 and in the Western Cape since 1941. Without re-stating the current grim press reports, it needs to be said that drought is nothing new to the South African farmer. Over the ages our farmers have shown a lot of innovation and tenacity to overcome the challenges of drought.
Technology and experience in the modern day, however, offers more alternatives to consider in terms of addressing the challenge of risk. The management of Omnia Nutriology® makes it their business to invest in research and technology, as well as to delve into the vast experience of their employees, to gain applied knowledge to assist their clients in mitigating risk. The company invests generously in such research, which includes agronomic as well as product and concept development.
Although plant nutrition explains less than 25% of the variability in eventual yield of grain crops (Bornman, 1992; Flock, 1992), it is still a major and important input on the grain farm. The protection of capital and reserves is also of the essence. In this article, related aspects and tools from within Omnia Nutriology® will be discussed briefly to give some pointers regarding plant nutrient management and related considerations under drought conditions.
Other articles in this newsletter will discuss most of the mentioned headings in much more detail. If more information is needed, your Omnia agronomist will gladly discuss it on the farm.
The classic principles and approach
In documents published all over the world regarding plant nutrition management within drought cycles two classic themes come to the fore.
The first is to accurately measure the quantity of nutrients which are inherently available for crop production. That is, of course, achieved by performing soil analyses and assessing all available analytical data. Delving into site specific soil analysis banks of the farm, whether from precision grid analysis, smart sampling or classic samples per field, is invaluable to make the right decisions. If there are any doubts, guided and specific samples need to be taken for analysis. Much emphasis (especially in the USA) is given to nitrogen assessment within the soil. In South Africa there is still some doubt regarding the use of soil nitrogen analyses for various reasons, the primary one being the dynamics of nitrogen in unbuffered soils from sampling to time of recommendation. Be that as it may, the humble soil analysis serves as a crucial source of information for decision-making regarding supplementory nutrient application or making use of reserves.
The second theme is drawing up balance sheets regarding available nutrients in the soil, from historic analysis values as well as recent crop additions and withdrawals. This is especially applicable after a drought season. For nitrogen management, as an example, a previous legume crop will add nitrogen to the net pool of nitrogen while cutting maize for forage will subtract potassium in the balancing equation.
From South African data, a third and most important classic theme emerges and that is ensuring that soil acidity is addressed. Soil acidity inhibits root growth and thus lessens the soil volume that can be explored for water and nutrients. Within the plant nutrition sphere and under acid soil conditions, liming is the input that gives the highest return on investment, even more than nitrogen fertilization. Sub-soil acidity is the most dangerous enemy. The utmost care should be taken to manage sub-soil acidity to ensure full profile utilisation.
New technology and tools from Omnia Nutriology®
Omnia Nutriology® offers precision farming with a difference. As previously mentioned, plant nutrition explains relatively little of the variance of eventual grain yield. Logically, soil physical parameters, drainage and available moisture explain more than 70% of the variance. Therefore OmniPrecise® emphasises the latter and then guides clients to manage nutrition accordingly. To identify zones that demarcate areas which need different nutrient management, long-term yield monitors and/or remote sensed vegetation index data are used from a comprehensive geographical information system with historical data. Zones with repeated below and above average yields are identified. This procedure is called zoning or OmniZone™. With the availability of these zones, OmniPrecise® then applies procedures (such as cumulative distribution curves) to define the long-term risk associated with each zone and then relating it to a probable yield. This service is called OmniRiskIQ™. Having access to OmniRiskIQ™ data is of huge value to manage various inputs, including nutrients, spatially and of course quantity.
During drought conditions, the use of OmniZone™ and OmniRiskIQ™ is of significant benefit to identify specific requirements and to allocate resources according to identified risk. It needs to be stated that the OmniRiskIQ™ offering is the only system currently known in South Africa that enables the quantification of the benefit of precision farming. OmniZone™ and OmniRiskIQ™ are discussed in more detail later in this newsletter, but Figures 1 and 2 provide a glimpse of the output.
Figure 1: Management zones of the OmniZone™ concept as compiled from four years of yield monitor data
Figure 2: An OmniRiskIQ™ output as compiled from OmniZone™ data
The use of response functions
A response function shows the relation between crop response (e.g. grain yield) and the level of an input such as, for instance, nitrogen. A response function is a mathematical equation and is site and condition specific. If long-term, site specific, functions are available, they can be used to calculate economic optimum levels of input under predicted conditions. Omnia Nutriology® has accumulated and, from its agronomic research, is still acquiring a magnitude of response functions per area per crop. Figure 3 illustrates the value of historic nitrogen response functions with a current price ratio to identify the point of application for minimum risk of margin loss. Figure 4 illustrates more specifically the scenario if only drought years are selected.
Figure 3: Margin loss curves due to nitrogen over and under fertilization of maize over the long term in the Hoopstad area (1986 to present). Current price ratios were used. Only relatively dry seasons of the early 80's and 90's are shown.
Figure 4: Margin loss curves due to nitrogen over or under fertilization of maize over the long term in the Hoopstad area (1986 to present). Current price ratios was used.
With response functions, the economic optimum level of input is usually calculated as the point showing the highest margin gained above the specific input cost. This point is not only dependent on the response function, but also the price ratio between the value of the output (grain value) and the cost of input (nutrient price) per same unit. There are, however, other calculations applicable, such as the rate of return on investment, taking total applicable fixed and variable costs into account. This calculation is most important under drought conditions, as a producer with a low fixed cost will be able to fertilize at lower levels and still generate a high level of return on investment. Ironically, producers with a high fixed cost structure will not be able to consider lower levels of fertilization than that of the point of highest margin above a particular input, such as nitrogen. Figure 5 illustrates the different economic optima mentioned. The low “other cost” scenario equivalent to 1 tonne of maize per ha is improbable, but it was nevertheless used to illustrate the concept. More detail was published in comprehensive papers by Prins, Terblanche and Bornman (1995) as well as Prins, Bornman & Meyer (1997).
Figure 5: Graphic illustration of different economic optima of nitrogen application using a representative response curve for the North West and Free State sandy soils (derived from ARC published data). The yield level is 7 tonne maize grain per ha, the current price ratio was used (October 2015) and the two fixed cost levels are equivalent to 1 and 3 tonnes of maize grain value per ha. The blue and red lines depict application for maximum return on total cost while the green depicts application for maximum margin over nitrogen cost only.
Fortunately, under drought conditions, price ratios are favourable, mostly because of higher crop prices. This aspect, together with historic response functions achieved under drought conditions, can paint realistic scenarios to consider when making decisions related to nutrient input quantity.
Another valuable source of information to guide investment in different nutrients is to calculate the relative return on investment for each nutrient using site specific response functions. The level of inherently available nutrients from the soil (such as phosphorus) obviously also plays a role. Using response functions generated by the Agricultural Research Council (Schmidt, Adriaanse, Smalberger and Du Preez, 2006) and Omnia Nutriology® for maize in the Western Free State and North West as well as current price ratios, it was recently calculated that the highest return on investment (liming excluded) is still from nitrogen (net return of more than 450%), followed by phosphorus (80%) and then potassium (45%), if inherent soil analyses levels are near optimum.
Although a rather popular practice for various reasons, pre-plant application of nitrogen, especially in a reduced form such as urea, is not good practice. Pre-applied nitrogen might not be completely taken up by growing plant roots, allowing it to be oxidised to nitrate or nitric acid. This will lead to serious sub-soil acidification. The extent and rate of this acidification will be discussed in another article (Fourie & Bornman, 2015). The best practice to follow under expected drought conditions remains banded nitrogen, mostly in nitrate form, at planting with topdressing according to seasonal rain.
The amount of nitrogen to band is even more important under drought conditions. High concentrations will lead to additional root stress due to osmotic potential (salt shock) under extended periods of dry soil. In the case of urea application, ammonia and ammonium toxicity is a real threat (Bornman, Moodley, Louw and Fouche, 2013).
An invaluable tool to manage nitrogen under drought conditions during the season is the chlorophyll (Minolta 502 SPAD) meter as it gives an accurate estimate of plant nitrogen content within seconds (Figure 6). The SPAD meter can also give an indication of sulphur availability by estimating tissue sulphur content (Figure 7). Omnia Nutriology® has accumulated extensive international and locally generated data for wheat and maize, especially to develop algorithms which are used primarily not only to guide nitrogen fertilization under site specific conditions, but also to identify probable sulphur deficiencies. Local trials have proven extensive savings in dry seasons. This service is available to Omnia Nutriology® agronomists via a short text message service from a central server. It will soon be available in the form of a smart phone application.
Figure 6: The predictability of tissue nitrogen content of maize plants with a Minolta SPAD 502 meter (Novoa and Villagran, 2002).
Figure 7: The predictability of tissue sulphur content of maize plants with a Minolta SPAD 502 meter (Pagani and Echeverria, 2012).
Increased water use efficiency (WUE) with specific nutrients
This is such an important topic that Omnia Nutriology® devoted an entire agronomy conference (2014) to the subject. Local and international academics specialising in this topic were invited to assist in the guidance of research and application. More detail is discussed in another article in this newsletter. However, it needs to be mentioned that the nitrogen form especially and sulphur are very important. Using the right form of nitrogen, which is nitrate, in the presence of adequate calcium and potassium can increase WUE significantly (Figure 8) (Bornman, 2014 and more specific articles in this newsletter). The presence of adequate sulphate sulphur can also lead to increased WUE, even to the extent of 50% as local trials have proven (Bornman, 2015). Other nutrients that need to be mentioned are of course potassium and also silicon.
Figure 8: The WUE of maize under simulated drought conditions in the greenhouse using different nitrogen source (Omnia Agronomy Research report, 2014). An Sol = ammonium nitrate solution, LAN = limestone ammonium nitrate and CN = calcium nitrate. the LSD (Least Significant Difference (p = 0.05) is indicated above every bar.
The use of OmniBio™ analyses
Protecting and maintaining a favourable environment for root growth in the sub-soil is most important, as previously mentioned under the heading of subsoil acidity. Other aspects, besides soil physical and chemical properties, also have an impact. One of these aspects is of course the microbiological activity in the soil. The presence of nematodes, for instance, can severely inhibit root growth into the sub-soil. Another aspect is understanding the activity of enzymes such as urease and phosphatase under drought conditions. An OmniBio™ analysis of top and sub-soils will shed much more light on the ability of a particular soil to act as a microbiological buffer against harsh dry conditions, ensuring root health, nutrient and especially water availability.
OmniSap® as an early warning system
It is now well-known that the plant sap analysis of Omnia is essential to give a “balance sheet” insight into the nutrients available to a plant at critical growth stages. This information is available within days to assist a producer to make nutrient management decisions. However, an OmniSap® analysis is much more than that. The uptake or lack of uptake of a combination of indicator nutrients can give a very early warning of plant stress, especially water stress. Principle component analysis of a huge database of analyses over 15 years makes this kind of interpretation possible.
The management of nutrients under irrigation where drought conditions prevail
Contrary to popular understanding, drought, besides emptying dams and rivers, also influences the irrigation farmer. There are many facets, but the most important one, nutrition-wise, is water quality. Water quality from sources deteriorates extensively under drought conditions. Certain salts become so concentrated that they inhibit root growth and essential nutrient uptake. There are several ways that Omnia Nutriology® approaches this threatening condition. The first is to continually monitor water quality from such sources by means of its sophisticated and accredited laboratory (Chemtech™). Not only are our own analyses used, but databases from various public institutions are also employed to monitor trends.
The other primary tool in the hands of the Omnia Nutriology® agronomist is the OFOS™ software. "OFOS™" stands for Omnia Fertigation Optimisation Software. This program carefully manages the concentration of each plant nutrient under site specific and crop specific conditions, even down to the specific growth stage. If certain ions or salinity in general become a threat, the software allows adjustments of nutrient concentrations in seconds to combat such a situation. For instance, the extensive value of calcium nitrate concentrations in the root zone, under saline conditions, is well documented (Bornman, 2014). Another example worth mentioning is the impact of the potassium source. Potassium chloride adds significantly to salinity, while potassium sulphate and nitrate much less so.
Omnia Nutriology® forms an integral part of agriculture and would like to stand at the right hand of the producer. Omnia is absolutely committed to contributing to risk mitigation on the farm, especially in the context of nutrient management. As mentioned before, Omnia contributes comprehensively to such research. Several products and concepts have been developed that are practically applicable on the farm. We at Omnia Nutriology® would like to assist our customers with all our available technology. We understand what matters to you and we are only a phone call away.
- Bornman, J. J., 1992. Risk analysis with regard to rainfed maize and wheat fertilization. Nampo Conference, Bothaville, June.
- Bornman, J.J., Moodley, V., van Zyl, K. Louw, T. & Fouché, D., 2013. Stomach ulcers, urease and urea. Omnia Nutriology® Newsletter, Summer 2013.
- Bornman, J.J., 2014. Plant nutrition to enhance water use efficiency. Presentation at the Agronomy Conference of Omnia Nutriology®. Brits. June.
- Bornman, J.J., 2015. The sulphur sting. Nutriology® News. Winter 2015. Also in a dedicated booklet by Omnia Fertilizer.
- Flock, M., 1992. Precision farming priority list. In a web based article accessed October, 2015; www.hrlimited.com.
- Fourie, M.C. & Bornman, J.J., 2015. Grondsuurheid in Vrystaat versleg – ook in VKB gebied. Free State Cooperative publication, December issue.
- Novoa, R.S.A & Villagran, N.A., 2002. Evaluation of a chlorophyll meter on the assessment of foliar nitrogen in corn. Agric. Tec. Chillian ene. V62.(1)
- Pagani, A. & Echeverria, H.E., 2012. Influence of sulfur deficiency on chlorophyll-meter readings of corn leaves. J. Plant Nutr. Soil Sci. p 604-613.
- Prins, A., Terblanche, E. & Bornman, J. J., 1995. Economic evaluation and practical use of long-term data from dryland maize nitrogen fertilization trials for recommendation purposes. Proceedings of the Joint Congress (Agriculture) Stellenbosch, January. 109.
- Prins, A., Bornman, J. J. & Meyer, J. H., 1997. Economic fertilizer recommendations for sugarcane in KwaZulu-Natal, incorporating risk quantification using a computer program. Proc. S Afr Sug Technol Ass 38-41.
- Schmidt, C.J.J., Smalberger S. A., Adriaanse F. G & Du Preez C.C., 2006. Relationship between inorganic soil nitrogen and maize grain yield with controlled traffic practices. South African Journal of Plant and Soil 23, 190-196.