Take home messages
- Intensive monitoring of soils and crops over a rotation sequence has identified why crops
do not achieve their potential yield. - Reviewing paddock performance at the end of the season and using paddock records is essential for sustained improvement in agronomic performance.
- Insufficient nitrogen was the main cause for the yield gap in NPS monitored paddocks in the
southern Mallee. Frost and heat shock were also responsible for significant yield penalties.
Diseases, weeds and insects also contributed, but were less severe in impact.
Background
The yield gap is the term applied to the difference between achieved and potential yield, where potential yield is estimated from simulation models. On average, Australia’s wheat growers are currently achieving about half their water-limited potential yield (Hochman et al. 2016, Hochman and Horan, 2018). Previous research with individual growers in the Victorian Wimmera and Mallee determined that the long-term yield gap for those farmers was approximately 20% (van Rees et al. 2012). For a National overview of the yield gap see www.yieldgapaustralia.com.au.
National Paddock Survey is a four-year (2015 to 2018) GRDC-funded project designed to quantify the yield gap on 250 paddocks nationally, and to determine the causes for the yield gap. Further, its aim is to establish whether management practices can be developed to reduce the yield gap to benefit farm profitability. The project aims to provide growers and their advisors with information and the tools required to close the yield gap.
Aim
To determine the main causes for the yield gap nationally for crops grown over a four-year rotation. To identify reasons for the yield gap in local regions with specific advice on how to lower the yield gap.
Project details
250 paddocks nationally, 80 in each of WA and N NSW/Qld, and 90 in S NSW, Vic and SA, were monitored intensively over a four-year rotation (2015 to 2018). Consultants and farming systems groups undertook the monitoring. Two zones in each paddock were monitored at five geo-referenced monitoring points along a permanent 200 to 250m transect. Each monitoring point was visited four times per season (pre- and post-season soil sampling and in-crop at the equivalent crop growth stages of GS30 and 65). Yield map data was obtained for each paddock enabling the yield of each zone to be determined accurately. Table 1 lists the annual monitoring undertaken in each zone.
All paddocks were simulated with APSIM (Holzworth et al. 2014) and, during the season, Yield Prophet was available to all consultants and farmers.
The whole data set (four years x 500 paddock zones) is being analysed by Roger Lawes, CSIRO for factors primarily responsible for the yield gap in each of the three GRDC regions (Lawes et al. 2018).
This paper outlines the results of six paddocks in the southern Mallee monitored by Kelly Angel (BCG) and Craig Muir (AGRIvision). The results are discussed as a paddock specific yield gap analysis over four seasons focused on outcomes for the farmer and consultant.
Results are presented as the modelled APSIM simulations in which:
- Ya = Actual yield (as determined for each zone from yield map data)
- Ysim = Simulated yield (for the same conditions as those in which the crop was grown)
- Yw= Simulated water limited, nitrogen (N) unlimited yield (for the same conditions as those in which the crop was grown, but with N supply unlimited). Yw is considered the potential yield for the crop.
Note: APSIM currently accurately simulates wheat, barley and canola. Simulations have not been done on lentils, faba beans, chickpeas, vetch or field peas.
The yield gap is calculated as the percentage difference between Yw and Ya ((Yw-Ya)/Yw).
Table 1. Overview of monitoring and data collected per zone for each NPS paddock.
Results and interpretation
1. Example of annual individual paddock results
Data for three years on two paddocks in the Southern Mallee are presented as examples of outputs as informed by the paddock monitoring (Figure 1).
Example 1. 2015: Southern Mallee paddock wheat yield (Ya) was lower than the simulated yield (where Ysim = Yw). Zone A – sandy rise, Zone B – clay loam. In-crop rainfall – 111mm.
Example 1 interpretation
Crop 2014: Vetch brown manure
Wheat in 2015 had a simulated yield close to equal to the potential yield (Ysim = Yw), which is a strong indication that the crop was not N limited. This observation is supported through soil testing and the amount of N applied by the farmer; a total of 147 and 148kg N/ha was available in Zone A and B respectively. Zone A had a simulated yield 0.6t/ha higher than the actual yield (Ysim > Ya) which indicates there were some factors limiting production. The crop had moderate disease levels in the soil as determined by the Predicta B method, YLS was observed at flowering (upper leaves) and oat aphids were present at flowering in the upper canopy. Another factor contributing to the loss in yield appears to be frost damage caused by two nights of temperatures between 0 and -2°C during flowering and grain filling
(GS60 to 79). It was reported that the crop was in moisture stress early in October 2015 (grain filling).
Example 2. 2016: Southern Mallee paddock yield (Ya) was similar to simulated yield (Ysim), which was lower than the potential yield (Yw). Zone A – sandy clay loam, Zone B – clay loam. In-crop rainfall – 335mm.
Example 2 interpretation
Crop 2014: Wheat 0.7t/ha
Barley in 2016: Zone A Ya=Ysim<Yw and Zone B Ya<Ysim<Yw. Indicating N was limiting in both zones. Abiotic and biotic factors resulted in a yield penalty. Zone A had an additional 25kg of N top-dressed which resulted in a yield increase. Soil borne diseases were higher in Zone B than in Zone A. It is expected that if the season had a tight finish the level of root borne diseases could have had more significant impact on yield. Frost occurred on 1 day (0 to -2°C) during flowering and grain filling. The paddock was clean of weeds and there were no insects.
Example 3. 2017: Southern Mallee paddock yield (Ya) was lower than the simulated yield (Ysim) which was equal to the potential yield (Yw). Zone A – sandy rise, Zone B – clay loam. In crop rainfall – 178mm.
Example 3 interpretation
Crop 2016: Field peas 1.2t/ha
Barley in 2017: Actual yield was significantly lower than the simulated (Ysim) and potential yield (Yw) indicating that N was not limited, and the ~2.5t/ha yield loss resulted from biotic or abiotic stresses. The crop in 2017 was wheat on field peas, a break-crop. However, soil disease levels as assessed through the PredictaB soil test were still moderately high. There were no weeds at flowering and relatively low numbers of oat aphids. Frost (0 to -2°C) occurred on four nights during flowering and grain filling could have been responsible for the significant yield loss.
2. Assessing crop performance: Water Use Efficiency vs modelling
The first paper on Water Use Efficiency (WUE) was published by French and Schultz in 1984. It was a break through at the time, enabling farmers and agronomists to benchmark crop yield against a target and compare performance against other wheat crops. The French and Schultz WUE equation has since been updated by Sadras and Angus, 2006 and Hunt and Kirkegaard, 2012.
Hunt and Kirkegaard, 2012 calculate Crop Water Use as: soil water pre-sowing – soil water post-harvest + rainfall during the same period. WUE is then calculated as yield (kg/ha) / (Crop Water Use – 60). Potential yield is calculated as 22 x (Crop Water Use – 60).
The 2015 to 2017 Southern Mallee NPS wheat and barley yields are plotted against Crop Water Use in Figure 1. The graph reveals a strong tendency for Ya to increase with Crop Water Use with an upper boundary of yield. The upper boundary is reasonably interpreted as Yw for well-managed crops as Crop Water Use increases. The two lines included on the diagram are the potential yield lines proposed by French & Schultz, 1984 and Hunt & Kirkegaard, 2012 the latter calculated as potential yield = 22 x (Crop Water Use – 60). This establishes a common maximum WUE of 22kg/mm/ha.
Figure 1. NPS – Southern Mallee wheat yields (Ya) plotted against Water Use (2015 to 2018). F & S refers to French and Schultz (1984); and H & K refers to Hunt and Kirkegaard (2012).
The reason for improvement in potential WUE, from 20 to 22kg/mm/ha since French and Schulz (1984) have been due to improved cultivars (semi-dwarf wheats) and higher atmospheric CO2 levels.
How useful is WUE compared with computer modelled assessments of potential yield,
and what will the future hold?
Figure 1 demonstrates a considerable variation in paddock yield relative to the potential, i.e. a considerable yield gap in many crops. Key questions for farmers and agronomists are what is the cause of the yield gap in each individual case and how can it be alleviated?
There are many possible causes that cannot be identified without careful paddock monitoring
of abiotic and biotic factors, as attempted in the present project.
We must remember that WUE to assess yield potential is a bucket approach to a complex problem in a system with many interactions. WUE will not explain the causes of a yield gap, nor can it inform on reasons for favourable outcomes. It may identify the presence of a yield gap but not their cause.
Causes of yield gaps
Abiotic factors
Variability is a feature of farming in Australia and there are several reasons why crop roots cannot access soil water such as soil type (texture) and physical and chemical limitations. Chemical and physical constraints to root development can have a large impact on potential yield.
Interactions between soil type, available soil water and the amount of water extracted by the growing crop are influenced by crop growth and the distribution and amount of rainfall. If these factors are ignored there is limited predictive capability of yield.
High and low temperatures at critical times of crop development also can cause devastating yield loss.
Biotic factors
Crop nutrition appropriate to achieving potential yield (Yw) is relatively well understood and in the case of N, with many examples of successful tactical responses to fertilisation. But this is not matched for other nutrients such as phosphorus and potassium, and micronutrients such as zinc.
Major infestations of weeds, pests and diseases can cause dramatic yield loss and less serious infestations may cause greater losses than is commonly appreciated and remain unknown without careful paddock monitoring.
The nature of these biotic causes of yield loss vary greatly from site to site, paddock to paddock and within paddock also.
Going forward with crop simulation models
Crop models, such as APSIM used in this study, are focussed on abiotic factors, but include biotic factors such as N nutrition. Their objective is to simulate yield (Ysim) in the absence of biotic factors such as weeds, diseases and pests and to estimate Yw by removing the effect of N shortage. For this, APSIM grows the crop on a daily time step and takes into account daily solar radiation, rainfall and availability of N. It uses soil-specific information for Crop Lower Limit (CLL) (wilting point) of the soil, defined as the soil water content below which water is not accessible to the crop. CLL is influenced by soil texture (sand, silt, clay
content) and subsoil limitations (such as high chloride levels). APSIM also explains the importance of rainfall distribution in terms of growth reductions due to transient water stress. Extreme events of temperature (hot and cold), which may be important at less-than daily time scales need to be further addressed.
Over the last decade our industry has made huge advances in engineering, with precision agriculture enabling mapping soil types across paddocks, understanding what affects the crops’ ability to extract water and most importantly empowering farmers to adopt precision seeding and to apply nutrients as required.
To fully utilise the power of crop models, we need to incorporate on-the-go modelled outputs to field operations such as seeding and nutrient applications. This could well be the next frontier in crop management. Biotic stresses such as weeds, diseases and pests can be included if the appropriate in‑field observations are made.
The NPS project has demonstrated that, as crop management becomes more sophisticated, it is essential to understand the reasons why crops fail to perform at their potential. When we understand the reasons why crops do not reach their potential we can better advise the growers we are working with.
References
Hochman, Z. & Horan, H. (2018). Causes of wheat yield gaps and opportunities to advance
the water‑limited yield frontier in Australia. Field Crops Research 228, 20-30.
Acknowledgements
The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC, the author would like to thank them for their continued support.
