Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling

AbstractThe ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO3− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3− and NH4+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent

What does it take to detect a change in soil carbon stock? A regional comparison of minimum detectable difference and experiment duration in the north central United States

Variability in soil organic carbon (SOC) results from natural and human processes interacting across time and space, and leads to large variation in the minimum difference in SOC that can be detected with a particular experimental design. Here we report a unique comparison of minimum detectable differences (MDDs) in SOC, and the estimated times required to observe those MDDs across the north central United States, calculated for the two most common SOC experiments: (1) a comparison between two treatments, e.g., moldboard plow (MP) and no-tillage (NT), using a randomized complete block design experiment; and (2) a comparison of changes in SOC over time for a particular treatment, e.g., NT, using a randomized complete block design experiment with time as an additional factor. We estimated the duration of the two experiment types required to achieve MDD through simulation of SOC dynamics. Data for the study came from 13 experimental sites located in Iowa, Illinois, Ohio, Michigan, Wisconsin, Missouri, and Minnesota. Soil organic carbon, bulk density, and texture were measured at four soil depths. Minimum detectable differences were calculated with probability of Type I error of 0.05 and probability of Type II error of 0.15. The MDDs in SOC were highly variable across the region and increased with soil depth. At 0 to 10 cm (0 to 3.9 in) soil depth, MDDs with five replications ranged from 1.04 g C kg−1 (0.017 oz C lb−1; 6%) to 7.15 g C kg−1 (0.114 oz C lb−1; 31%) for comparison of two treatments; and from 0.46 g C kg−1 (0.007 oz C lb−1; 3%) to 3.12 g C kg−1 (0.050 oz C lb−1; 13%) for SOC change over time. Large differences were also predicted in the experiment duration required to detect a difference in SOC between MP and NT (from 8 to \u3e100 years with five replications), or a change in SOC over time under NT management (from 11 to 71 years with five replications). At most locations, the time required to detect a change in SOC under NT was shorter than the time required to detect a difference between MP and NT. Minimum detectable difference and experiment duration decreased with the number of replications and were correlated with SOC variability and soil texture of the experimental sites, i.e., they tended to be lower in fine textured soils. Experiment duration was also reduced by increased crop productivity and the amount of residue left on the soil. The relationships and methods described here enable the design of experiments with high power of detecting differences and changes in SOC and enhance our understanding of how management practices influence SOC storage

Soil water improvements with the long-term use of a winter rye cover crop

AbstractThe Midwestern United States, a region that produces one-third of maize and one-quarter of soybean grain globally, is projected to experience increasing rainfall variability. One approach to mitigate climate impacts is to utilize crop and soil management practices that enhance soil water storage and reduce the risks of flooding as well as drought-induced crop water stress. While some research indicates that a winter cover crop in maize-soybean rotations increases soil water availability, producers continue to be concerned that water use by cover crops will reduce water for a following cash crop. We analyzed continuous in-field soil water measurements from 2008 to 2014 at a Central Iowa research site that has included a winter rye cover crop in a maize-soybean rotation for thirteen years. This period of study included years in the top third of the wettest on record (2008, 2010, 2014) as well as drier years in the bottom third (2012, 2013). We found the cover crop treatment to have significantly higher soil water storage at the 0–30cm depth from 2012 to 2014 when compared to the no cover crop treatment and in most years greater soil water content on individual days analyzed during the cash crop growing season. We further found that the cover crop significantly increased the field capacity water content by 10–11% and plant available water by 21–22%. Finally, in 2013 and 2014, we measured maize and soybean biomass every 2–3 weeks and did not see treatment differences in crop growth, leaf area or nitrogen uptake. Final crop yields were not statistically different between the cover and no cover crop treatment in any of the seven years of this analysis. This research indicates that the long-term use of a winter rye cover crop can improve soil water dynamics without sacrificing cash crop growth in maize-soybean crop rotations in the Midwestern United States

Investigations into aspects of nitrogen and carbon dynamics in grassland used for dairy production on a clay loam soil

Increasing concentrations of nitrogen (N) in the waterbodies along with increasing concentrations of nitrous oxide (N2O) and carbon dioxide in the atmosphere have become an international environmental concern. Permanent grasslands are important sources of feed for intensively managed dairy and beef farming systems in North West Europe and represent around 90% of agricultural land in Ireland. However, there is a potential for substantial N losses following grazing during the winter when a significant effective rainfall occurs. In addition, ploughing and reseeding of grassland in order to increase its productivity may lead to a substantial decrease in soil organic matter (SOM) followed by an increase in soil inorganic N. This two year study investigated (i) the environmental impact of a dairy production system involving grazing over the winter through calculations of soil surface N balances and its effect on soil N dynamics and N loses to groundwater; (ii) the impact of permanent grassland renovation on soil N, N2O emissions, N leaching and soil organic carbon (SOC) in a poorly drained clay-loam soil at Solohead Research Farm. The correlations between grazing management and soil N dynamics in the soil profile or N concentrations in the groundwater were difficult due to high natural buffering capacity of the soils associated with heavy texture, high SOC, high soil pH, anaerobic conditions and presence of shallow groundwater. For this reason, grazing over the winter period had no effect on soil N dynamics and groundwater quality on this site. In contrast, grassland renovation decreased topsoil SOC and total N, increased oxidized N leaching to groundwater and N2O emissions from the soil surface as a result of soil disturbance, enhanced decomposition of SOM followed by nitrification and denitrification. Although, the overall losses due to net SOM mineralisation were high (3 t N and 32 t C ha-1), the proportion lost via N leaching and direct N2O emissions was low (27 kg N ha-1 y-1) suggesting that nitrate were instantaneously reduced by dissimilatory nitrate reduction to ammonium or complete denitrification

Direct and Indirect Economic Incentives to Mitigate Nitrogen Surpluses: A Sensitivity Analysis

The reduction of nitrogen (N) surplus is an ongoing topic in the agri-environmental policies of many countries in the developed world. The introduction of N balance estimation in agricultural sector models is therefore pertinent and requires an interdisciplinary approach. We extended the agent based agricultural sector model SWISSland with an N farm gate balance estimation to pre-evaluate the introduction of a levy on N inputs, particularly a levy on fertilizer and imported concentrates, on N surplus reduction in the Swiss agriculture. The model was based on the Swiss farm accountancy data network (FADN) for 3,000 farms. The model’s ability to represent the N balance was assessed by conducting a structured full factorial sensitivity analysis. The sensitivity analysis revealed the possibility to switch to organic farming and the hectare based payments for ensuring food security as key parameters with the largest influence on the modelled N surplus. The evaluation of N input levy scenarios suggested that an introduction of a tax of 800% of N price will reduce the N surplus by 10% indicating a price elasticity of -0.03. The sensitivity analysis and the results from the levy scenarios suggest that indirect instruments, such as optimizing the direct payments scheme, should be considered rather than direct instruments for an effective N surpluses mitigation in Swiss agriculture.ISSN:1460-742

Developing recommendations for increased productivity in cassava-maize intercropping systems in Southern Nigeria

Cassava-maize intercropping is a common practice among smallholder farmers in Southern Nigeria. It provides food security and early access to income from the maize component. However, yields of both crops are commonly low in farmers’ fields. Multi-locational trials were conducted in Southern Nigeria in 2016 and 2017 to investigate options to increase productivity and profitability through increased cassava and maize plant densities and fertilizer application. Trials with 4 and 6 treatments in 2016 and 2017, respectively were established on 126 farmers’ fields over two seasons with a set of different designs, including combinations of two levels of crop density and three levels of fertilizer rates. The maize crop was tested at low density (LM) with 20,000 plants ha−1 versus high density (HM) with 40,000 plants ha−1. For cassava, low density (LC) had had 10,000 plants ha−1 versus the high density (HC) with 12,500 plants ha−1.; The fertilizer application followed a regime favouring either the maize crop (FM: 90 kg N, 20 kg P and 37 kg K ha−1) or the cassava crop (FC: 75 kg N, 20 kg P and 90 kg K ha−1), next to control without fertilizer application (F0). Higher maize density (HM) increased marketable maize cob yield by 14 % (3700 cobs ha−1) in the first cycle and by 8% (2100 cobs ha−1) in the second cycle, relative to the LM treatment. Across both cropping cycles, fertilizer application increased cob yield by 15 % (5000 cobs ha−1) and 19 % (6700 cobs ha−1) in the FC and FM regime, respectively. Cassava storage root yield increased by 16 % (4 Mg ha−1) due to increased cassava plant density, and by 14 % (4 Mg ha−1) due to fertilizer application (i.e., with both fertilizer regimes) but only in the first cropping cycle. In the second cycle, increased maize plant density (HM) reduced cassava storage root yield by 7% (1.5 Mg ha−1) relative to the LM treatment. However, the negative effect of high maize density on storage root yield was counteracted by fertilizer application. Fresh storage root yield increased by 8% (2 Mg ha−1) in both fertilizer regimes compared to the control without fertilizer application. Responses to fertilizer by cassava and maize varied between fields. Positive responses tended to decline with increasing yields in the control treatment. The average value-to-cost ratio (VCR) of fertilizer use for the FM regime was 3.6 and higher than for the FC regime (VCR = 1.6), resulting from higher maize yields when FM than when FC was applied. Revenue generated by maize constituted 84–91% of the total revenue of the cropping system. The highest profits were achieved with the FM regime when both cassava and maize were grown at high density. However, fertilizer application was not always advisable as 34 % of farmers did not realize a profit. For higher yields and profitability, fertilizer recommendations should be targeted to responsive fields based on soil fertility knowledge.ISSN:0378-4290ISSN:1872-685

Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soilNO3− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3− and NH4+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.</p

What does it take to detect a change in soil carbon stock? A regional comparison of minimum detectable difference and experiment duration in the north central United States

Variability in soil organic carbon (SOC) results from natural and human processes interacting across time and space, and leads to large variation in the minimum difference in SOC that can be detected with a particular experimental design. Here we report a unique comparison of minimum detectable differences (MDDs) in SOC, and the estimated times required to observe those MDDs across the north central United States, calculated for the two most common SOC experiments: (1) a comparison between two treatments, e.g., moldboard plow (MP) and no-tillage (NT), using a randomized complete block design experiment; and (2) a comparison of changes in SOC over time for a particular treatment, e.g., NT, using a randomized complete block design experiment with time as an additional factor. We estimated the duration of the two experiment types required to achieve MDD through simulation of SOC dynamics. Data for the study came from 13 experimental sites located in Iowa, Illinois, Ohio, Michigan, Wisconsin, Missouri, and Minnesota. Soil organic carbon, bulk density, and texture were measured at four soil depths. Minimum detectable differences were calculated with probability of Type I error of 0.05 and probability of Type II error of 0.15. The MDDs in SOC were highly variable across the region and increased with soil depth. At 0 to 10 cm (0 to 3.9 in) soil depth, MDDs with five replications ranged from 1.04 g C kg−1 (0.017 oz C lb−1; 6%) to 7.15 g C kg−1 (0.114 oz C lb−1; 31%) for comparison of two treatments; and from 0.46 g C kg−1 (0.007 oz C lb−1; 3%) to 3.12 g C kg−1 (0.050 oz C lb−1; 13%) for SOC change over time. Large differences were also predicted in the experiment duration required to detect a difference in SOC between MP and NT (from 8 to >100 years with five replications), or a change in SOC over time under NT management (from 11 to 71 years with five replications). At most locations, the time required to detect a change in SOC under NT was shorter than the time required to detect a difference between MP and NT. Minimum detectable difference and experiment duration decreased with the number of replications and were correlated with SOC variability and soil texture of the experimental sites, i.e., they tended to be lower in fine textured soils. Experiment duration was also reduced by increased crop productivity and the amount of residue left on the soil. The relationships and methods described here enable the design of experiments with high power of detecting differences and changes in SOC and enhance our understanding of how management practices influence SOC storage.This article is from Journal of Soil and Water Conservation 69 (2014): 517–531, doi:10.2489/jswc.69.6.517.</p