In farmer fields under sub-optimal conditions, spatial variability is typically large. One of the factors is plant failure, creating gaps in the canopy that may or may not be filled in by neighbouring plants. The occurrence of these canopy gaps is directly linked to weed presence, but also to a sub-optimal light interception by the crop. Spatial variability may be further increased by nutrient or water deficiency, which reduces plant growth but may also decrease plant survival rates.
Our current crop growth models are developed to represent experimental conditions with typically very homogeneous plots. As such, do not take spatial variability into account and are therefore not suitable to analyse the influence of nutrient- and water stress on actual yields measured in real on-farm fields. The effect of plant density, compression or distance between rows (Gou et al. 2017) and clumping of leaves (Goudriaan and Van Laar, 1994) are well understood for crops in rows with regular in-row plant distances, but not for more heterogeneous situations. Measuring spatial variability strongly depends on scale, where the variation decreases with increasing scale according to Taylor’s power law (Göring et al., 2015). The residual, i.e. the difference between the observed and predicted variation at a particular scale, is expected to depend on canopy gaps with more variation than expected when larger gaps occur. A better understanding of the influence of canopy gaps on these residuals is important, as it may provide better means to link ground cover estimates from drones or from satellite to light interception and leaf area index. It would be improve so-called inverse modelling techniques to estimate LAI from measured light reflection.
You will use a functional-structural plant model (Vos et al, 2010) to explore the influence of plant death on LAI, light interception, ground cover and spatial variability of ground cover at various scales. We hypothesise that the within-field spatial variability as function of average ground cover can be used to differentiate homogeneous from heterogeneous canopies (Schut and Ketelaars, 2003) and assess the gap fraction. Further, we hypothesise that these residuals from Taylor’s power law are related to clumping and compression factors. Hence, relationships between ground cover, LAI, spatial variability and light interception are better understood. You will compare simulated to measured plant densities and ground cover values in the field.
Students with an interest in plant production systems and crop growth modelling.
Location and Period
Wageningen, any time
Döring, T.F., Knapp, S., and Cohen, J.E. (2015). Taylor's power law and the stability of crop yields. Field Crops Research183,294-302.
Goudriaan, J., and Van Laar, H.H. (1994). Modelling potential crop growth processes : textbook with exercises. Dordrecht, the Netherlands: Kluwer Academic Publishers.
Gou, F., Van Ittersum, M.K., and Van Der Werf, W. (2017). Simulating potential growth in a relay-strip intercropping system: Model description, calibration and testing. Field Crops Research200,122-142.
Taylor, L.R. (1961). Aggregation, variance and the mean. Nature189,732-735.
Schut, A.G.T., and Ketelaars, J.J.M.H. (2003). Assessment of seasonal dry-matter yield and quality of grass swards with imaging spectroscopy. Grass and Forage Science58,385-396.
Vos, J., Evers, J.B., Buck-Sorlin, G.H., Andrieu, B., Chelle, M., and Visser, P.H.B.D. (2010). Functional-structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany61,2101-2115.
Tom Schut 0317-482454 email@example.com
Jochem Evers 0317-487219 firstname.lastname@example.org