Fowkes, Neville

McDonald, Barry

English

Mathematics in Industry Information Service Eprints Archive

 Page 10 of 25 
PREDICTING THE EFFECTS OF AGRICULTURAL LAND MANAGEMENT 
CHANGE ON SOIL QUALITY AND PRODUCTIVITY 
 
Soil Sustainability Group, Crop & Food Research Ltd, Christchurch 
 
Industry Representatives 
Dr Mike Beare, Crop and Food Research, Christchurch, NZ 
Ms Erin Lawrence, Crop and Food Research, Christchurch, NZ 
Mr Jeromy Cuff, Environment Canterbury, Timaru, NZ 
Ms Esther Meenken, Crop and Food Research, Christchurch, NZ 
 
Project Moderators 
Dr Barry McDonald, Massey University, Albany, NZ 
Dr Neville Fowkes, University of Western Australia, Australia 
 
Student Moderator 
Mrs Penny Bilton, Massey University, Albany, NZ 
 
Much of New Zealand’s income derives directly from agriculture, and for 
decades individual New Zealanders, the New Zealand public, and 
agricultural industries have invested heavily in innovative farming 
practices to increase efficiency and to produce high quality products, and 
in scientific research to enhance the productivity and product quality of 
agricultural production systems.  One aspect of this is ongoing scientific 
research into the characteristics, health or quality of the soil itself and its 
importance to sustaining high levels of agricultural production.   
 
Soil quality can be defined as the fitness of soil for a specific land use. The 
farming management practices employed under a given land use – 
including the type of farm, the crops grown, and the tillage methods 
employed, the fertiliser and irrigation applied, and the grazing practices 
used  – can affect the quality of the soil and the resulting economic 
outputs and environmental impacts.  Farming practices can also damage 
the soil stability, putting it at risk of erosion.  The focus of the MISG 
problem was to examine factors that affect seven important measures of 
soil quality – measures relating to both short-term productivity and long-
term sustainability of the soil.  The MISG study formed one chapter in an 
ongoing study programme being undertaken by researchers from Crop & 
Food Research and the Sustainable Soil Management Promotion Group.  
The eventual aim of the study programme is to produce a land 
management index that can be used by farmers to assess the likely effect 
of their farming practices on the soil and future productivity. An example 
would be the possible benefits or otherwise of replacing conventional 
ploughing with minimum-tillage techniques. 
 
The seven soil measures under study reflected:  soil compaction (BD15, 
the bulk density of the top 15 cm of soil);  organic matter (C%15, the 
carbon percentage in the top 15 cm; and HWC, the biologically available 
carbon); the amount of phosphorous available in the soil (measured by 
Olsen’s P); and the size and stability of soil aggregates (measured in 
terms of mean weight AgStabMWD  and the percentage of very small or 
very large aggregates, ASD<0.85 and ASD>9.5 - both of which are 
related to erosion). Predictors of the soil measures included the soil order 
 Page 11 of 25 
(i.e. soil classification), soil texture (silt, sand or clay), the geographic 
region (Auckland/Waikato, Hawke’s Bay, Canterbury, or Southland) and 
the land use or type of farming practiced (intensive cropping, mixed 
cropping, vegetable production, dairy farming, conventional sheep/beef 
farming, or high-tech intensive beef production).   The effect of land 
management practices was summarised by a tillage index calculated from 
10 years of management data (tillage index high for many years of 
intensive cultivation, zero for undisturbed grass) and a crop index 
reflecting the   crop types grown.  
 
The MISG contributed to the analysis of the soil data in several ways. 
 
Exploratory data analysis was used to investigate the inter-relationships 
between the soil measures and to conclude some variables should be 
analysed on a logarithmic scale. 
 
Preliminary regression models were fitted to each soil measure in terms of 
the categorical variables land use (farm type), soil order, soil texture and 
geographic region. These indicated between 31% and 78% of the 
variation in soil measures was “explained” by the categorical variables, 
with BD15 the best explained and ASD>9.5 the worst explained.  The 
amount explained was somewhat higher when data from the cropping land 
uses alone was considered.  Adding in the crop index and tillage index 
scores gave a significant improvement in the model for AgStabMWD, 
logHWC, logC%15, logOlsenP and ASD<0.85.   The crop and tillage index 
did not add significantly to the model in the case of ASD>9.5 or BD15. 
Although these numbers would change with more development of the 
model, they broadly indicate which soil variables are the most/least 
responsive to the crop and tillage indices.  In particular, bulk density and 
high compaction (ASD>9.5) do not appear to be affected much by the 
activities measured by the crop and tillage indices.  
 
The crop and tillage indices, however, could be tweaked. They are based 
on empirical weightings of both the type of activity and how long ago the 
activity occurred.  The weights were based on earlier experiments coupled 
with informed guesswork. An alternative approach was suggested at 
MISG, using Structural Equation Modelling to determine the weights. This 
approach was tried, using AMOS, and looks promising.  
 
Significant regional differences were found in the soil measures even after 
adjusting for land use, soil order and soil texture.  The strength of 
regional differences after these adjustments was surprising.  The LENZ 
database (Land Environment of New Zealand) was interrogated to identify 
possible explanations for the regional differences.  LENZ categorises sites 
according to a variety of measures, and data can be found to describe 
most agricultural locations.  Climatic variations (annual rainfall, mean 
temperature and solar radiation) offered a partial explanation, significantly 
related to carbon mass, but not sufficient to explain all regional 
differences.  Other components of LENZ may help explain soil order 
differences.  LENZ data could well provide a useful component of a Land 
Management Index. 
 
 Page 12 of 25 
There appear to be interactions between the effects of region, land use, 
soil order and soil texture on the soil measures, for example the effect of 
land use on the soil measures may not be the same for all soil textures. 
Unfortunately the categorical data was unbalanced: each categorical 
variable had at least one category that was not present at all levels of 
some other categorical variable. For example each region had at least one 
missing soil order, land use or texture.  This made the exploration of 
interactions difficult. A graphical method was proposed and used to 
identify those interactions that seemed to be the most important. These 
were tested in a regression model, but the procedure needs to be 
repeated with more developed models including more variables e.g. LENZ 
data. The intention would be to only include interactions when absolutely 
necessary, as for prediction purposes one needs to avoid over-fitting to 
the sample data.   Also some alleged interactions may just be the result of 
data errors, and the technique aided in identifying these unusual cases.  
 
An additional question concerned penetration resistance, a measure of soil 
compaction and therefore the difficulty roots have in entering the soil. 
Penetration resistance is an important soil measure in its own right, and 
the data are currently collected, but the results are greatly affected by the 
moisture content of the soil. The MISG group were asked to consider 
methods of correcting the penetration resistance data for differences in 
soil moisture content.   A simple model was proposed and calculations 
suggest the model is plausible.  
 
Finally suggestions were given as to how one could better express the 
data in a regression model for computation in Excel with new farmers’ 
data, and how to present the results in a way easy to communicate to 
farmers. 
 
   
******************** 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


Predicting the Effects of Agricultural Land Management Change on Soil Quality and Productivity

Abstract

Similar works

Full text

Available Versions

Mathematics in Industry Information Service Eprints Archive