Agrohydrological analysis of groundwater recharge and land use changes in the Pampas of Argentina
Kroes, Joop ; Dam, Jos van; Supit, Iwan ; Abelleyra, Diego de; Verón, Santiago ; Wit, Allard de; Boogaard, Hendrik ; Angelini, Marcos ; Damiano, Francisco ; Groenendijk, Piet ; Wesseling, Jan ; Veldhuizen, Ab - \ 2019
Agricultural Water Management 213 (2019). - ISSN 0378-3774 - p. 843 - 857.
Argentina - Capillary rise - Groundwater recharge - Land use - Pampas - Soybean - SWAP - WOFOST
This paper studies the changes of groundwater, climate and land use in the Pampas of Argentina. These changes offer opportunities and threats. Lowering groundwater without irrigation causes drought and successive crop and yield damage. Rising groundwater may alleviate drought as capillary rise supports root water uptake and crop growth, thus narrowing the difference between potential and actual yields. However, rising groundwater may also limit soil water storage, cause flooding in metropolitan areas and have a negative impact on crop yields. Changing land use from continuous soy bean into crop rotations or natural vegetation may decrease groundwater recharge and thus decrease groundwater levels. In case of crop rotation however, leaching of nutrients like nitrate may increase. We quantified these impacts using integrated dynamic crop growth and soil hydrology modelling. The models were tested at field scale using a local dataset from Argentina. We applied distributed modelling at regional scale to evaluate the impacts on groundwater recharge and crop yields using long term weather data. The experiments showed that threats arise from continuous monotone land use. Opportunities are created when a proper balance is found between supply and demand of soil water using a larger differentiation of land use. Increasing the areas of land use types with higher evapotranspiration, like permanent grassland and trees, will contribute to a more stable hydrologic system with more water storage capacities in the soil system and lower groundwater levels. Modelling tools clearly support the evaluation of the impact of land use and climate change on groundwater levels and crop yields.
Mapping the soils of an Argentine Pampas region using structural equation modelling
Angelini, Marcos E. ; Heuvelink, Gerard B.M. ; Kempen, Bas ; Morrás, Héctor J.M. - \ 2016
Geoderma 281 (2016). - ISSN 0016-7061 - p. 102 - 118.
Cause–effect models - Interrelationships soil properties - Mechanistic models - Pampas - Soil forming processes - Soil genesis
Current digital soil mapping (DSM) methods have limitations. For instance, it is difficult to predict a large number of soil properties simultaneously, while preserving the relationships between them. Another problem is that prevalent prediction models use pedological knowledge in a very crude way only. To tackle these problems, we investigated the use of structural equation modelling (SEM). SEM has its roots in the social sciences and is recently also being used in other scientific disciplines, such as ecology. SEM integrates empirical information with mechanistic knowledge by deriving the model equations from known causal relationships, while estimating the model parameters using the available data. It distinguishes between endogenous and exogenous variables, where, in our application, the first are soil properties and the latter are external soil forming factors (i.e. climate, relief, organisms). We introduce SEM theory and present a case study in which we applied SEM to a 22,900 km2 region in the Argentinian Pampas to map seven key soil properties. In this case study, we started with identifying the main soil forming processes in the study area and assigned for each process the main soil properties affected. Based on this analysis we defined a conceptual soil-landscape model, which was subsequently converted to a SEM graphical model. Finally, we derived the SEM equations and implemented these in the statistical software R using the latent variable analysis (lavaan) package. The model was calibrated using a soil dataset of 320 soil profile data and 12 environmental covariate layers. The outcomes of the model were maps of seven soil properties and a SEM graph that shows the strength of the relationships. Although the accuracy of the maps, based on cross-validation and independent validation, was poor, this paper demonstrates that SEM can be used to explicitly include pedological knowledge in prediction of soil properties and modelling of their interrelationships. It bridges the gap between empirical and mechanistic methods for soil-landscape modelling, and is a tool that can help produce pedologically sound soil maps