Abstract
Abstract. For calculating the effects of hydrological measures on agricultural production in the Netherlands a new comprehensive and climate proof method is being developed: WaterVision Agriculture (in Dutch: Waterwijzer Landbouw). End users have asked for a method that considers current and future climate, that can quantify the differences between years and also the effects of extreme weather events. Furthermore they would like a method that considers current farm management and that can distinguish three different causes of crop yield reduction: drought, saline conditions or too wet conditions causing oxygen shortage in the root zone. WaterVision Agriculture is based on the hydrological simulation model SWAP and the crop growth model WOFOST. SWAP simulates water transport in the unsaturated zone using meteorological data, boundary conditions (like groundwater level or drainage) and soil parameters. WOFOST simulates crop growth as a function of meteorological conditions and crop parameters. Using the combination of these process-based models we have derived a meta-model, i.e. a set of easily applicable simplified relations for assessing crop growth as a function of soil type and groundwater level. These relations are based on multiple model runs for at least 72 soil units and the possible groundwater regimes in the Netherlands. So far, we parameterized the model for the crops silage maize and grassland. For the assessment, the soil characteristics (soil water retention and hydraulic conductivity) are very important input parameters for all soil layers of these 72 soil units. These 72 soil units cover all soils in the Netherlands. This paper describes (i) the setup and examples of application of the process-based model SWAP-WOFOST, (ii) the development of the simplified relations based on this model and (iii) how WaterVision Agriculture can be used by farmers, regional government, water boards and others to assess crop yield reduction as a function of groundwater characteristics or as a function of the salt concentration in the root zone for the various soil types.
Highlights
The United Nations formulated 17 sustainable development goals (SDG) for the period 2015–2030
In this paper we describe how this meta-model for WaterVision Agriculture was derived for grassland and silage maize and how it can be used
The linked SWAP-WOFOST model was evaluated using five experimental data sets with observations for grassland and silage maize (Table 1). These sets were selected because the experiments had a focus on stress due to drought or wet conditions; other stresses, like nutrient shortage or pests and diseases hardly occurred at these experiments which allowed an evaluation of the SWAP-WOFOST model for water stress situations
Summary
The United Nations formulated 17 sustainable development goals (SDG) for the period 2015–2030 (http:// sustainabledevelopment.un.org/focussdgs.html). Prominent goals are “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” (SDG 2) and “Ensure availability and sustainable management for water and sanitation for all” (SDG 6). A key factor to achieve these goals is efficient use of water in agriculture. We may release large amounts of water for extra food production or other pressing human or natural needs by increasing the water productivity in agriculture. This requires a profound knowledge of the effects of dry, wet, and saline conditions on growth and yield of agricultural crops
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
