Explicit wheat production model adjusted for semi-arid environments
Introduction
Crop models are increasingly being used in agriculture to support decision-making and planning at a large spatial scale, particularly in relation to risks associated to climate change issues such as decreases in yield and/or soil fertility (Rosenzweig et al., 2014; Schauberger et al., 2017; Wu et al., 2016). Among these models, process-based crop growth simulation models are the most useful in predicting crop responses to climate because they attempt to explain the mechanism of the processes rather than simply provide parameter predictions such as statistical models and models based on empirical relationships do (Challinor et al., 2009; Chenu et al., 2017; Holzkämper, 2017). Crop growth models have been originally developed at the plot scale and more recently used and evaluated at the regional and global scale but with a rather coarse spatial resolution. These models have been used to study the impacts of projected climate change over large agricultural areas (Challinor et al., 2016; Deryng et al., 2014; Zhao et al., 2017) but need to be adjusted for smallholding farm applications.
Process-based crop models are used to simulate crop growth dynamics and yield predictions under diverse management practices, climate and environmental conditions (Chenu et al., 2017). These models are either driven by plot-based meteorological information acquired from weather stations providing estimates at the plot scale (e.g. Brisson et al., 2003; Stöckle et al., 2003), or by grid-based modelled climate data, providing estimates at a regional to global scale but at a very coarse spatial resolution (at least a few to hundreds of kilometers, e.g. Stöckle et al., 2014). Due to a lack of spatial continuity in input weather data and/or the too coarse spatial resolution of climate data, these models suffer from a poor representation of water dynamics at the root zone essential to feedback process-based mechanistic crop models. The lack of a proper surface physics simulation, such as SWC dynamics, may result in model uncertainties, particularly in crops growing under water-limited conditions (Helman et al., 2019; Rajala et al., 2009).
To provide a better application to local conditions model predictions are often downscaled using either statistical or dynamic downscaling approaches (Fowler et al., 2007). However, such downscaling efforts might introduce biases in terms of unaccounted specific temperature extremes or rainfall patterns that could bias the simulated growth dynamics and yield (Cammarano et al., 2013; Cammarano and Tian, 2018; Hansen and Jones, 2000). Moreover, downscaled results may involve a high degree of uncertainty related to the original model parameters and model structure (Palosuo et al., 2011).
To overcome these drawbacks, the use of a one-dimensional mechanistic crop model is suggested for local applications (Boogaard et al., 1998; Brisson et al., 2009). The one-dimensional mechanistic model uses meteorological information acquired from a near weather station as an input instead of using a grid-based modelled climate data, providing estimates at the field/farm level. These kinds of models have been successfully applied in the estimation of wheat, maize and rice yields providing a potential decision supporting tool for local farming (Attia et al., 2016; Corbeels et al., 2016; Lopez et al., 2017).
Though promising, it requires the use of many input variables, which are often unavailable or difficult to acquire. It also lacks the spatially continuous representation needed to cope with issues of spatial variability in local soils and other environmental characteristics, which have been shown to affect crop yield production (Jégo et al., 2015). A combination of approaches is therefore suggested for merging process-based models using GIS tools to simulate crop growth processes over large areas with a relatively high spatial resolution (Huffman et al., 2015; Liu, 2009; Thorp et al., 2008).
A Digital Elevation Model (DEM) layer, for example, may provide high-resolution grid-based information relevant for computing topographic models (Tarboton, 1997) and potential incident radiation load (Fu and Rich, 2002), necessary for a more accurate simulation of plant growth in the one-dimensional mechanistic crop model (Amir and Sinclair, 1991a, 1991b). Meteorological radars may provide a more accurate grid-based rainfall data than statistical interpolation between weather stations, particularly in areas where rainfall is highly variable in time and space (Marra and Morin, 2015; Morin and Gabella, 2007).
Integrating the grid-based information with a simple mechanistic plant growth model in a geographic information system (GIS) environment may provide a spatially continuous estimation of crop production and yield at a very high spatial resolution (of a few tens of meters). Moreover, the high-resolution spatial hydrologic information may be used as an input into a one-dimensional hydrological model to improve SWC dynamics simulations providing a more accurate information on the available SWC at the root zone, which makes it an important factor in crop development and productivity (Lawes et al., 2009), particularly in water-limited environments (Acevedo et al., 1999; Amir and Sinclair, 1991b; Helman et al., 2018).
Here, we combine a one-dimensional Richard’s equation-based hydrological model with a two-dimensional runoff model and a mechanistic wheat growth model (Amir and Sinclair, 1991b, 1991a) on a grid-based GIS platform to provide high resolution aboveground wheat biomass and grain yield estimates. The integrated model, linking different processes on a grid-based platform, may be important for estimating AGB and GY variations in our region that present variable topography and significant slopes. To assess the importance of our integrated approach we first tested the spatial and temporal accuracy of the integrated model in 22 commercial wheat fields in Israel, validating the model with in-situ SWC, AGB and GY measurements (Section 3.1). Then, we examined the spatial sensitivity of the models through fixing each input parameter per model run (Section 3.2). Finally, we used the model to simulate field-level AGB and GY variations expected from projected rainfall and temperature changes in our region (Section 3.3).
Section snippets
Study area
The model was used in the study area located at Ramot Yssakhar (32°35′38.76″N, 35°27′48.96″E; Fig. 1a), which is a relatively hilly region in Northern Israel. At Ramot Yssakhar, there are numerous crop fields with a total agricultural area of c. 30 km2. Elevation in Ramot Yssakhar ranges between -150 and c. 270 m above sea level (m.a.s.l.) with a more narrow range of 30 and 260 m.a.s.l. at the area of the 22 fields used in this study (Fig. 1c). Typical slopes are 1° to 30°, with an average
Hydrus -1D
Fig. 3a,b show the seasonal evolution of observed (sensors) and modelled (HYDRUS-1D) volumetric SWC in the top-soil layers at location A (see Fig. 1c). HYDRUS-1D showed high sensitivity to rainfall, which was in accordance with observed SWC at the shallower soil depth (5–10 cm, Fig. 3a), but less at the deeper 30-cm soil layer (Fig. 3b). The lower sensitivity to rain events of observed SWC at the deeper layer was likely due to low percolation rate, which was less successfully simulated by HYDRUS
Discussion
The integrated model presented in this study links between water infiltration into the soil, water runoff flow and crop growth modeling at a high spatial resolution (50 m). The linkage between surface water dynamics and crop growth is of a great importance, particularly at the start of the season prior to the increase in surface roughness following vegetation growth. The use of three well-established sub-model components allowed to better quantifying water redistribution, which plays an
Acknowledgments
David Helman is a Fulbright Fellow 2018/2019. This research was supported by a grant from the Chief Scientist of the Israeli Ministry of Agriculture and Rural Development (IMARD; Grant #857061910). The authors thank Dr Enli Wang and two anonymous reviewers for thorough review and helpful comments, Shacham Mekorot for providing the radar data, the Israel Meteorological Service for the rainfall data and IMARD for sharing the meteorological data. Special thanks to farmers Tal Ofek, Eitan Avivi and
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Currently at the Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA.