Water-limited yield potential is sensitive to soil hydraulic properties that govern plant-available water retention characteristics, and landscape and soil properties that influence infiltration rate and runoff. Likewise, presence of shallow water tables can also influence the crop water balance. Hence, it is necessary to determine the dominant soil units at the level of the RWS buffers and derive the parameters required by crop simulation models to simulate water-limited yield potential (Yw). Key issues discussed in this protocol are: soil data sources and the procedures to derive the dominant soil units and soil parameters.

1. Soil data sources

GYGA uses four complementary soil data sources:

1. High-quality national databases with ‘functional soil properties', that is, quantitative soil profile data from which required soil properties for crop simulation can be retrieved, for example, the  SSURGO database for USA, the European soil data base, and the soil database for Brazil compiled by Cooper et al. (2005).
2. Global soil databases (ISRIC-WISE soil database; Gijsman et al., 2007; Batjes, 2012) or regional soil databases. An example of a regional database is the AfSIS soil database that is used for Sub-Saharan Africa. The used product is the Root Zone Plant Available Water Holding Capacity of the Sub-Saharan Africa Soil. This product is the result of a collaboration between AfSIS and GYGA (see this link).
3. Expert opinion from country agronomists

 

2. Procedure to identify and estimate the area of the dominant soil units

The procedure to select the soil properties is as follows:

1. We select the one to three most dominant soil mapping units (SUID) per RWS buffer. The dominance of a SUID is dependent on its coverage of the crop-specific harvested area within the RWS buffer based on SPAM harvested crop area maps (IFPRI, 2019).
2. Each SUID consists of soil series. The approximate area fraction of each soil unit, within the SUID, is typically known. Each specific soil unit is defined by a particular combination of soil properties, such as soil texture, soil depth, drainage class, etc.
3. We select the most important soil series in each selected SUID until achieving ca. 50% area coverage within each selected SUID, after discarding those soils that are not suitable for long-term annual crop production based on the following rules:
   1. rootable depth < 30 cm.
   2. soil water holding capacity < 7 cm/m (averaged over the different layers).
   3. % sand > 75% (averaged over the different layers).
   4. if terrain slope data are available, from other sources or local experts, soils with slope > 10% will also be discarded.
4. Soil properties required for crop growth simulations are retrieved from the selected soil series descriptions. Local agronomists and experts might be consulted whenever there is uncertainty about the selected soil series and their properties and to verify if there is a special preference to grow a certain crop on specific soil units.
5. Water-limited yield potential is simulated for all selected soil series.
6. The mean water limited yield potential per buffer zone is calculated by weighing (based on the area fractions) the simulated water limited yields for each of the selected soil series within the selected SUIDs.
7. Additional soil parameters (slope and groundwater depths) are retrieved as needed from other databases and/or expert opinion and linked to the selected soil series.

3. Determination of soil properties

Soil input data used by different crop simulation models to simulate water-limited yield potentials (Yw) may differ to some extent. However, the basic required soil information by all models consists of (i) rootable depth, (ii) plant-available soil water (PASW, difference in water content between field capacity and permanent wilting point), either as direct input or estimated from soil texture using pedotransfer functions, and (iii) terrain slope and drainage class (for calculating runoff).

(i) Maximum soil rootable depth: Crop models simulate root depth dynamics during the growing season. However, soils may impose in some cases a maximum rootable soil depth due to physical or chemical constrains. If there are known soil constraints to rooting depth (e.g., caliche layer, hardpan, salinity, alkalinity, acidity) the maximum rooting depth for that site is reduced according to the information. Data about maximum rootable soil depth can be retrieved from soil unit descriptions and sometimes from national and global soil databases. However, in some cases the depth of the bottom soil layer in these databases is not necessarily a proxy for the maximum rootable soil depth. In such cases local experts are consulted to find the maximum rootable soil depth for a given soil series in a given RWS buffer.

(ii) Plant-available soil water: plant available soil water capacity (PASW) is determined by the upper and lower soil limits for water retention (i.e., field capacity and permanent wilting point, respectively). Some models (e.g., WOFOST) use the upper and lower soil limits reported in soil databases, e.g., national (SSURGO) and global soil databases (WISE), as input for the simulations and by simulating the rooting depth the PASW is calculated. Other models require users to set soil water limits for each soil layer (CERES-type models, CROPGRO, ORYZA, CropSyst, APSIM), while yet others estimate PASW internally based on soil texture specified by the users for each soil layer (Hybrid-Maize). Actual measurements of soil water characteristics are rarely available, so these are typically estimated using pedo-transfer functions (PTF) based on soil texture. Many PTFs are available to derive soil moisture limits (Gijsman et al., 2002). PTF values can vary depending on the climate of the region. Thus, calibrating them according to the climate of the region is essential. When locally-calibrated PTFs are not available, we recommend using the Saxton and Rawls (2006) PTF for temperate soils, which was satisfactorily calibrated and evaluated based on USDA soil profiles data (Hydrology and Remote Sensing Laboratory, USDA). In the case of tropical soils, we recommend using PTFs from Tomasella et al. (2000), Hodnett and Tomasella (2002), and Tomasella and Hodnett (2004).

(iii) Terrain slope and drainage class: these parameters are required to estimate water loss by surface runoff. Most models account internally for runoff. In turn, this requires information on slope, drainage class and soil cover. Information on slope and drainage class can be retrieved from the description of the ‘typical pedon', supplemented with local agronomist expert opinion and elevation maps. In all cases, it is important that the reported terrain slope and drainage class correspond to the selected soil series. Finally, because the goal is to simulate Yw following optimal crop and soil management, we assumed that soil is always covered with crop residue at sowing in the case of upland crops, which, in turn, helps reduce soil evaporation, runoff, and erosion.

(iv) Depth of shallow water tables. In many upland and lowland crops, a shallow water table can supply with water to crops, helping buffer against rain-free periods (Kroes et al., 2018). For GYGA, we considered soils with shallow water tables as separate soil series. Depending on the ability of specific models to reproduce the influence of shallow water tables on the crop-soil water balances, we follow two approaches: (i) specify the groundwater depth at sowing and let the model simulate the effect of it on the water balance (e.g., rice using ORYZA) or (ii) in the case of models that do not account for the effect of shallow water tables (basically most existing models for upland crops), we assume that crops grow without water limitation and, hence, we simulate Yw assuming that there is no water limitation. Such approach will tend to overestimate the positive impact of water tables on average yield and stability. On the other hand, it will underestimate the effect of excess water. In any case, assuming no water limitation in cases of presence of water table is more realistic that simulating Yw ignoring the effect of water table (Kroes et al., 2018).

(v) Initial soil water content: Crop growth can be affected by the soil water content at sowing or planting. This variable is highly context specific (time and space). This can be achieved following three approaches: (i) simulate the crop system over years to dynamically estimate the soil water content at the beginning of each crop season, (ii) run crop-specific simulations but initiate the simulations at least two months before planting date to allow the model to estimate soil water content at the sowing time, and (iii) use a fixed soil water content for cases in which this is well known by local experts and there is not too much variation in the initial soil content across years.

 

Batjes, N.H., 2012.  ISRIC WISE derived soil properties on a 5 by 5 arc-minutes global grid (ver. 1.2). Report 2012/01. ISRIC - World Soil Information, Wageningen, The Netherlands, 57 pp.

Cooper, M., Mendes, L.M.S., Silva, W.L.C., Sparovek, G., 2005. A national soil profile database for Brazil available to international scientists. Soil Sci. Soc. Am. J. 69, 649-652.

Gijsman, AJ, Jagtap, SS, Jones, JW, 2002. Wadding through a swamp of complete confusion: how to choose a method for estimating soil water retention parameters fro crop models. Europ. J. Agronomy 18, 77-106. 

Gijsman, AJ, Thornton, PK, Hoogenboom, G. 2007. Using the WISE database to parameterize soil inputs for crop simulation models. Computers and Electronics in Agriculture 56, 85-100.

Hodnett, M. G. & Tomasella, J., 2002. Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: A new water retention pedo-transfer functions developed for tropical soils. Geoderma 108, 155–180.

International Food Policy Research Institute. Global spatially-disaggregated crop production statistics data for 2010 version 2.0.https://doi.org/10.7910/DVN/PRFF8V (2019). SPAM are available at www.mapspam.info.

Kroes, J., Supit, I.,  Van Dam, J., Van Walsum, P., Mulder, M., 2018. Impact of capillary rise and recirculation on simulated crop yields. Hydrol. Earth Syst. Sci.,22,2937–2952.

Saxton, K.E., Rawls, R.J., 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70, 1569-1578. See also: (http://hydrolab.arsusda.gov/soilwater/Index.htm)

Soltani, A., Sinclair, TR. 2012. Modeling physiology of crop development, growth and yield. CAB, Wallingford, UK. 322 pp.

Tomasella, J, Hodnett, MG, Rossato, L, 2000. Pedotransfer functions for the estimation of soil water retention in Brazilian soils. Soil Sci Soc Am J 69, 649-652.

Tomasella, J, Hodnett, 2004. Pedotransfer functions for tropical soils. In: Developments in Soil Science, pp. 415-429.