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Description
Introduction
It is well known that the pH of soils will impact on the yield of the crop, and the optimum pH for grasslands is around 6.0 while for arable crops the optimum pH is 6.5. Soil pH is usually tested and modified periodically with lime application to raise the pH as crop growth and associated management practices generally tend to reduce the pH over time. To demonstrate the value of liming, a pH experiment was established at SRUC Craibstone, Aberdeen (02°15’W, 57°11’N) in 1961. The trial was established as a demonstration with a gradient from 4.5 to 7.5. The aim of this study was to assess the yield sensitivity of spring oats and winter wheat to meteorological variables.
Methodology
The rotation established in 1961 was an 8-course ley-arable system. The pH of the plots was tested annually and amended as appropriate in order to maintain the target pH. The rotation included three years of grass/white clover, winter wheat, potatoes, spring barley, swedes and undersown spring oats. Although every course of the rotation is present in every year, there are no within year replicates. The fertiliser applications have not changed since the establishment of the trial, and crop protections products were applied when required.
The yield data is reliable from 1969, and by that time the soils had settled at or close to the target pH. Currently, we have focused on exploring the effect of the pH on the yield of the oats and the wheat over the last 50 years. This has been carried out as a two-stage process. The first stage was to fit separately for each year a gaussian non-linear curve (Archontoulis & Miguez, 2015) to the yield data. Thus the equation fitted was:
yield = (w.max * exp(-0.5((pH-pHm)/b)*2)
w.max is the maximum yield, pHm represents the pH at this maximum yield, and b controls the width of the bell. The initial value for w.max was set at the average maximum yield over the course of the trial, and the pHm was set at the pH at which that yield occurred. The initial value of b was set at 1. The next stage was to explain the estimated w.max values by the weather parameters. The weather variables selected to include in the regression analysis were based on total precipitation and average daily temperature during both the previous winter, and the current growing season. In addition, variables based on rainfall and temperature were calculated to assess whether extreme weather conditions during the growing season were impacting on yield. These included the mean maximum temperature (TDD), the maximum number of dry days (rainfall < 0.2 mm (CDD), the maximum number of days where the rainfall was greater than 0.2 mm (CRD) and the maximum number of days where then rainfall was greater than 1mm (CHD). All the weather parameters were also squared to include any non-linear effects. A stepwise regression was performed to determine which of the climatic variables were impacting the w.max parameter.
Results
The peak maximum yield for the spring oats and winter wheat occurred at a pH5 and 5.5, respectively. The results indicate that for oats w.max was explained by average daily temperature (T), winter rainfall (WP), and the extreme conditions of the maximum average daily temperature (TDD), and the maximum number of dry days (CDD). A wider range of weather parameters has a significant impact on the w.max for winter wheat. In this case, average daily temperature (T, T2), and total rainfall during the winter and the growing season (P, P2) were significant. The extreme conditions of the maximum average daily temperature (TDD, TDD2), the maximum number of rain days (CHD, CRD).
Discussion
This long-term pH experiment has enabled us to explore the effect of pH on yield over an extended period. Temperature, rainfall and extremes of temperature and rainfall are important in determining the maximum yield of the spring oats and winter wheat. However, the maximum for winter wheat was affected by the number of rainy days whereas spring oats was affected by the number of dry days.
References
Archontoulis, S. V., & Miguez, F. E. (2015). Nonlinear Regression Models and Applications in Agricultural Research. Agronomy Journal, 107(2), 786–798. https://doi.org/10.2134/AGRONJ2012.0506
| Keywords | oats, wheat, pH, climate |
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