How important is the description of soil unsaturated hydraulic conductivity values for simulating soil saturation level, drainage and pasture yield?

Accurate simulation of soil water dynamics is a key factor when using agricultural models for guiding management decisions. However, the determination of soil hydraulic properties, especially unsaturated hydraulic conductivity, is challenging and measured data are scarce. We investigated the use of APSIM (Agricultural Production Simulation Model) with SWIM3 as the water module, based on Richards equation and a bimodal pore system, to determine likely ranges of the hydraulic conductivity at field capacity (K-10; assumed at a matric potential of −10 kPa) for soils representing different drainage characteristics. Hydraulic conductivity measurements of soils with contrasting soil drainage characteristics and values for K-10 were extracted from New Zealand’s national soil database. The K-10 values were then varied in a sensitivity analysis from 0.02 to 5 mm d−1 for well-drained soils, from 0.02 to 1 mm d−1 for moderately well-drained soils, and from 0.008 to 0.25 mm d−1 for poorly drained soils. The value of K-10 had a large effect on the time it took for the soil to drain from saturation to field capacity. In contrast, the saturated hydraulic conductivity value had little effect. Simulations were then run over 20 years using two climatic conditions, either a general climate station for all seven different soils, or site-specific climate stations. Two values for K-10 were used, either the APSIM default value, or the soil-specific measured K-10. The monthly average soil saturation level simulated with the latter has a better correspondence with the morphology of the seven soils. Finally, the effect of K-10 on drainage and pasture yield was investigated. Total annual drainage was only slightly affected by the choice of K-10, but pasture yield varied substantially.Ministry of Business, Innovation and Employment’s Endeavour Fund, through the Manaaki Whenua-led ‘Next Generation S-map’ research programme, C09X161

Simulating water and nitrogen runoff with APSIM

To determine the impact of potential reductions of terrain-targeted nitrogen (N) fertilisation rates on N losses a simulation study was carried out using the Agricultural Production Systems Simulator (APSIM). To simulate N runoff a simple approach was used, in which runoff is based on the N concentration in the soil solution and an extraction coefficient. Firstly, APSIM parameters that have the largest effect on runoff of water and N were determined for terrains with different slopes for a poorly drained silt loam. A sensitivity analysis was then conducted to assess the effect of soil hydraulic properties and soil organic carbon content on runoff losses. Finally, APSIM was set up to simulate pasture production and water and N dynamics (including pasture N uptake, leaching and N runoff) for a farm on rolling hills in South Canterbury, New Zealand. Two different fertilisation approaches were used, either scheduled or based on the aboveground N concentration of the pasture. For the poorly drained silt loam, the rainfall intensity and the surface conductance had the highest effect on the amount of water lost by runoff. Soil hydraulic conductivity at saturation and field capacity, as well as plant available water content also controlled runoff of water and N, while the organic carbon content of the topsoil had less effect on N runoff. Both the extraction coefficient and the depth considered to exchange N with the runoff water affected the amount of N lost via runoff. Using the aboveground pasture N concentration prior to fertilisation had positive effects on pasture yield and reduced N runoff losses

Response of nitrate leaching to no-tillage is dependent on soil, climate, and management factors: A global meta-analysis

No tillage (NT) has been proposed as a practice to reduce the adverse effects of tillage on contaminant (e.g., sediment and nutrient) losses to waterways. Nonetheless, previous reports on impacts of NT on nitrate (NO¯₃) leaching are inconsistent. A global meta-analysis was conducted to test the hypothesis that the response of (NO¯₃) leaching under NT, relative to tillage, is associated with tillage type (inversion vs non-inversion tillage), soil properties (e.g., soil organic carbon [SOC]), climate factors (i.e., water input), and management practices (e.g., NT duration and nitrogen fertilizer inputs). Overall, compared with all forms of tillage combined, NT had 4% and 14% greater area-scaled and yield-scaled NO¯₃ leaching losses, respectively. The NO¯₃ leaching under NT tended to be 7% greater than that of inversion tillage but comparable to non-inversion tillage. Greater NO¯₃ leaching under NT, compared with inversion tillage, was most evident under short-duration NT (200 kg ha¯¹) and lower (0–100 kg ha¯¹) rates of nitrogen addition. Of these, SOC was the most important factor affecting the risk of NO₃‾ leaching under NT compared with inversion tillage. Globally, on average, the greater amount of NO₃‾ leached under NT, compared with inversion tillage, was mainly attributed to corresponding increases in drainage. The percentage of global cropping land with lower risk of NO₃‾ leaching under NT, relative to inversion tillage, increased with NT duration from 3 years (31%) to 15 years (54%). This study highlighted that the benefits of NT adoption for mitigating NO¯₃ leaching are most likely in long-term NT cropping systems on high-SOC soils

Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator

The inclusion of perennial groundcover (PGC) in maize production offers a tenable solution to natural resources-related concerns associated with conventional maize; however, insight into system management and key information gaps is needed to guide future research. We therefore extended the Agricultural Production Systems sIMulator (APSIM) to an annual and perennial intercrop by integrating annual and perennial APSIM modules. These were parameterized for Kentucky bluegrass (KB) (Poa pratensis L.) or creeping red fescue (CF) (Festuca rubra L.) as PGC using a three-year dataset. Our objectives for this intercropping modeling study were to: i) simultaneously model a PGC and annual cash crop using APSIM software; ii) utilize APSIM to understand interactive processes in the maize-PGC system; and iii) utilize the calibrated model to explore both production and environmental benefits via scenario modeling. For objective I, the integrated model successfully predicted maize total aboveground biomass (TAB) (relative root mean square error, RRMSE of 13- 27%) and PGC above- and belowground tissue N concentration (RRMSE of 11-18%). The calibrated model effectively captured observed trends in PGC biomass accumulation and soil nitrate (NO3). For objective II, model analysis showed that competition for light was the primary maize yield penalty factor from PGC, while water and N impacted maize yield later in the maize growing season. In objective III, we concluded that effective PGC suppression produces minimal maize yield loss and significant environmental benefits; conversely, poor groundcover suppression may produce unfavorable environmental consequences and decrease maize grain yield. Effective PGC suppression is key for long-term system success

Understanding water losses from irrigated pastures on loess-derived hillslopes

Irrigation is likely to increase water losses from hillslopes, particularly on loess-derived soils with impeded drainage. This is important as irrigation of these soils in New Zealand is increasing. A field site was established to monitor runoff from a pasture hillslope irrigated by a centre-pivot in South Canterbury. Between November and March, 161 and 199 mm of irrigation was applied, 23% more at the bottom of the slope. Runoff varied with position in the hillslope, 3.5 times greater on the bottom plot (52 mm) compared to the top. Over the length of the slope (40 m) this represents a potential loss of 9% of precipitation, or 21% of the irrigation. Evidence for both saturation excess and infiltration excess runoff was observed, with antecedent soil moisture conditions being a key factor. Pasture production and water use efficiency (WUE) also varied with slope, the least (4.6 t DM/ha or 12 kg DM/ha/mm) observed at the middle and most at the top of the slope (10.1 t DM/ha or 23 kg DM/ha/mm). This was likely due to a combination of differences in radiation and soil conditions. There was indication that pasture growth was limited by water availability at the top and potentially excess at the bottom of the slope. Our results indicate potential for improving irrigation practices

Development and analysis of the Soil Water Infiltration Global database.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type (~40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it