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Agronomic Spatial Variability and Resolution What is it? How do we describe it? What does it imply for precision management? Agronomic Variability • Fundament assumption of precision farming • Agronomic factors vary spatially within a field • If these factors can be measured then crop yield and/or net economic returns can be optimize Agronomic Variables • Soils – – – – Classification Texture Organic matter Water holding capacity • Topography – Slope – Aspect • Fertility – – – – – pH Nitrogen Phosphorus Potassium Other nutrients • Plant available water • Crop Cultivar Agronomic Variables • Temperature • Rainfall • Weeds – Species – Population • Insects – Species – Feeding patterns • Tillage Practices • Diseases – Macro and micro environment • Crop Stand • Method and Uniformity of Application – Fertilizers – Crop protectants What is variability • Variability - difference in the magnitude of measurements of variable – Values can change randomly because of error in the sensor – Values can change because of changes in the underlying factor • As time changes • As location changes Why statistically describe measurements • Raw data sets are too large to understand or interpret • Statistics provide a means of summarizing data and can be readily interpreted for making management decisions • Statistics can define relationships among variables Statistical Analyses Commonly Used In Precision Agriculture • Descriptive Statistics – Measures of Central Tendency • Mean • Median – Measures of Dispersion • Range • Standard Deviation • Coefficient of Variation • Regression • Geostatistics - Semivariance Analysis Measures of Central Tendency • When a factor, such as crop yield, is measured at different locations within a field, values vary greatly • This variation can appear to be random • The set of these measurements is a population • A value exists that is the central or usual value of the population Measures of Central Tendency • This is important because the current practices in precision farming treat the field or region in the field based on the average of the measurements Mean or Average Value • Most common measure of central tendency • Definition: For n measurements X1,X2,X3,…,Xn n X 1 + X 2 +...+ X n = X = n X i =1 n i Mean or Average • The mean value is useful if the measured value is normally distributed (Bell Curve) – Most biological processes are normally distributed – Spatially distributed measurements are often not normally distributed • To calculated the mean in Excel = Average (Col Row : Col Row) The Median Value • For skewed distributions, is the better predictor of the expected or central value • Calculated by ranking the values from high to low – For an odd number of measurements, the median is middle value – For an even number of measurements, the median is average of the two middle values The Median Value • In Excel, the median is calculated using the following formula: = Median (Col Row: Col Rows) Measures of Dispersion • Measures of dispersion describe the distribution of the set of measurements Range of the Sample Set • Difference between the maximum and minimum values of the measurement • Calculated in Excel by the following formula: = Max (Col Row : Col Row) - Min (Col Row : Col Row) Standard Deviation • The standard deviation of a normally distributed sample set is 1/2 of the “range” of 66 %values for the population n s= (X i =1 i -X ) n -1 2 Standard Deviation • For a normal distribution (Bell Curve) 95% of the samples from a population will lie in the interval X - 1.96s Z X + 1.96s • The standard deviation is calculated in Excel using the following formual: = Stdev (Col Row : Col Row) Coefficient of Variation • The magnitudes of the differences between large values and their means tend to be large. The differences between small values and there means tend to be small. • Consequently, a high yielding field is likely to have a higher standard deviation than a low yielding field, even if the variability is lower in the high yield field. Coefficient of Variation • Thus, variation about two means of different magnitudes cannot easily be compared. • Comparisons can be made by calculating the relative variation, or the normalized standard deviation. • This measurement is called the Coefficient of Variation. Coefficient of Variation • The Coefficient of Variation or C.V. is calculated by dividing the standard deviation of the data set by its mean. Often that value is multiplied by 100 and the C.V. is expressed as a percentage. • Experience with similar data set is required to determine if the C.V. is unusually large. Correlation • One objective of precision farming is to alter the level of one variable (e.g. soil nitrate) to change the response of another variable (e.g. grain yield). • There are other confounding factors affecting grain yield such as weed competition or pH, which cannot always be accounted for. Correlation • The practitioner still needs to determine the degree to which the two variables vary together. • The correlation coefficient indicates is that measure. • The correlation coefficient, r, lies between -1 and 1. Positive values indicate that X and Y tend to increase or decrease together. Correlation • Values of r near 0 indicate that there is little or no relationship between the two variables. • The coefficient of determination, r2, is important in precision farming because, when the samples are collected by location in the field, it indicates the percentage of the variability in the dependent variable explained by the independent variable. Correlation • For example, if the r2 of soil N and grain yield is 90% then 90% of the variability across the field can be explained by soil nitrate. Spatially varying the N fertilizer rate based on the nitrate level in the soil should have a large effect on grain yield. • In Excel, correlation is calculate by the following: = Correl (Col Row : Col Row, Col Row: Col Row) Small-scale spatial variability