Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Abstract

SummaryWeeds tend to aggregate in patches within fields, and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at various scales, the strength of the relations between soil properties and weed density would also be expected to be scale‐dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We developed a general method that uses novel within‐field nested sampling and residual maximum‐likelihood (reml) estimation to explore scale‐dependent relations between weeds and soil properties. We validated the method using a case study of Alopecurus myosuroides in winter wheat. Using reml, we partitioned the variance and covariance into scale‐specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales, we optimised the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Defining the habitat niche of Alopecurus myosuroides at the field scale

“…We did this using the residual maximum likelihood (REML) estimator as described by Metcalfe et al . (). Following partitioning of the components of variance at the different spatial scales, we estimated the correlations between A. myosuroides and the soil properties at each scale where the estimated components of variance were positive.…”

“…These can be created from manually sampled data on weed distributions. Some of these maps are of inadequate quality, often because the sampling on which they are based was too sparse (Metcalfe et al, 2016). The second online approach is through real-time detection of weeds with optical sensors, usually detecting mature weeds in the previous cropping season to guide spraying decisions in the following year.…”

The implications of spatially variable pre‐emergence herbicide efficacy for weed management

“…Alopecurus myosuroides exhibits patchy distributions within fields, yet its control is often through uniform application of herbicides. As with many species, it is thought that these patchy distributions in arable fields are strongly affected by their environment, in particular, the soil …”

“…As with many species, it is thought that these patchy distributions in arable fields are strongly affected by their environment, in particular, the soil. 3,4 Soil properties not only affect the life-cycle of the weeds directly 5 but they can also have an indirect effect by altering the efficacy of some herbicides. 6 Organic matter in the soil can lead to adsorption of herbicide.…”

Replicate and randomize, or lie

“…'We took replicate samples and pooled them before making the measurements'. (Do a pilot trial to find out-see, for example, the recent paper by Metcalfe et al, 2016). 'We sequenced many clones on our sample'.…”