Data from: Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC 1 models

Process-based models are increasingly used to study mass and energy fluxes from agro-ecosystems, including nitrous oxide (N2O) emissions from agricultural fields. This data set is the output of three process-based models – DayCent, DNDC, and EPIC – which were used to simulate fluxes of N2O from dairy farm soils. The individual models’ output and the ensemble mean output were evaluated against field observations from two agricultural research stations in Arlington, WI and Marshfield, WI. These sites utilize cropping systems and nitrogen fertilizer management strategies common to Midwest dairy farms.

The models were calibrated and validated using data collected at Arlington and Marshfield over five years (nine years for crop yield). Calibration and validation used observations of soil temperature (n = 887), volumetric soil water content (VSWC, n = 880), crop yield (n = 67), and soil N2O flux (n = 896). The observed data are presented here with the model output to document model calibration and validation; most of these observed data are also held by Ag Data Commons in separate data sets from field experiments at Arlington and Marshfield (http://dx.doi.org/10.15482/USDA.ADC/1361194, http://dx.doi.org/10.15482/USDA.ADC/1401975, http://dx.doi.org/10.15482/USDA.ADC/1399470). The remaining observed data is described in Osterholz et al. 2014.

Model simulations were run from 2010-2015 for the Arlington site and 2013-2015 for the Marshfield site. The three models were parameterized (i.e. calibrated) for each site using the same climate, initial soil physical and chemical conditions, hydraulic properties, initial soil carbon, and management schedules. Weather data for each site (daily minimum and maximum temperature, precipitation, relative humidity, wind speed, and solar radiation) was reconstructed using the NOAA online climate database (NOAA, 2016). Initial soil physical and chemical properties were constructed from available on-site measurements and supplemented using the Web Soil Survey (Soil Survey Staff, 2016). Soil carbon data was available for each site, and to prioritize model agreement initial soil carbon for the 0-20cm layer was set at 55.7 Mg C ha-1 for Arlington (Sanford et al., 2012), and at 52.6 Mg C ha-1 for Marshfield. Following parameterization of soil C, a 17 year spin-up period (1993-2009) at each site was simulated prior to the years during which data was collected (2010-2015). While DayCent developers typically recommend a spin-up of at least 1000 years, DNDC has been run with spin-up periods as low as 2 years (Zhang et al., 2015). Given that observations of soil C were available, a 17 year spin-up was chosen to reflect the duration between initial soil C sampling (Sanford et al., 2012) and the first measurement of N2O in our data set (Osterholz et al., 2014). Management and input schedules were constructed from on-site data and record-keeping; these are available in the supplementary online data of the primary journal paper. All other initial parameters, such as crop-specific productivity or soil carbon turnover rate, were independently established by each model in calibration.

This work was part of “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP), funded by the United States Department of Agriculture – National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP was to improve understanding of the magnitudes and controlling factors over greenhouse gas (GHG) emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP has improved life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities.

Data from: Gas emissions from dairy barnyards

To assess the magnitude of greenhouse gas (GHG) fluxes, nutrient runoff and leaching from dairy barnyards and to characterize factors controlling these fluxes, nine barnyards were built at the U.S. Dairy Forage Research Center Farm in Prairie du Sac, WI (latitude 43.33N, longitude 89.71W). The barnyards were designed to simulate outdoor cattle-holding areas on commercial dairy farms in Wisconsin. Each barnyard was approximately 7m x 7m; areas of barnyards 1-9 were 51.91, 47.29, 50.97, 46.32, 45.64, 46.30, 48.93, 48.78, 46.73 square meters, respectively. Factors investigated included three different surface materials (bark, sand, soil) and timing of cattle corralling. Each barnyard included a gravity drainage system that allowed leachate to be pumped out and analyzed. Each soil-covered barnyard also included a system to intercept runoff at the perimeter and drain to a pumping port, similar to the leachate systems.

From October 2010 to October 2015, dairy heifers were placed onto experimental barnyards for approximately 7-day periods four times per year, generally in mid-spring, late-spring / early summer, mid-to-late summer and early-to-mid autumn. Heifers were fed once per day from total mixed rations consisting mostly of corn (maize) and alfalfa silages. Feed offered and feed refused were both weighed and analyzed for total nitrogen (N), carbon (C), phosphorus (P) and cell wall components (neutral detergent fiber, NDF). Leachate was pumped out of plots frequently enough to prevent saturation of surface materials and potential anaerobic conditions. Leachate was also pumped out the day before any gas flux measurements. Leachate total volume and nitrogen species were measured, and from “soil” barnyards the runoff was also measured. The starting bulk density, pH, total carbon (C) and total N of barnyard surface materials were analyzed. Decomposed bark in barnyards was replaced with new bark in 2013, before the spring flux measurements. Please note: the data presented here includes observations made in 2015; the original paper included observations through 2014 only.

Gas fluxes (carbon dioxide, CO2; methane, CH4; ammonia, NH3; and nitrous oxide, N2O) were measured during the two days before heifers were corralled in barnyards, and during the two days after heifers were moved off the barnyards. During the first day of each two-day measurement period, gas fluxes were measured at two randomly selected locations within each barnyard. Each location was sampled once in the morning and once in the afternoon. During the second day, this procedure was repeated with two new randomly selected locations in each barnyard.

This experiment was partially funded by a project called “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP). The Dairy CAP is funded by the United States Department of Agriculture – National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP is to improve understanding of the magnitudes and controlling factors over GHG emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP is improving life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities.

Whole-farm Model: Detailed Data on Nine Farms for Impact Assessment of Africa RISING Technologies

The zip.file contains the Farm DESIGN model (software) as well as the input data. A user manual of the software can be found online. The zipped Farm DESIGN model contains the whole-farm representation of nine farms in Northern Ghana. A low, medium and a high resource endowed farm per site, namely in Duko (Northern Region), Nyangua (Upper East) and Zanko (Upper West). There are several models per farm:


1. The current/ actual farm configuration

2. The baseline (reset to a situation with traditional (no Africa RISING) farming practices)

3. The farm, where Africa RISING Package 1 (Maize) is implemented

4. The farm, where Africa RISING Package 2 (Cowpea) is implemented

5. The farm, where Africa RISING Package 3 (Soybean) is implemented

6. The farm, where Africa RISING Package 4 (Maize-Legume Rotation) is implemented

7. The farm, where Africa RISING Package 5 (Maize-Legume Strip Crop) is implemented

8. A farm-model that is ready for an exploration (containing additional options the model may choose)

Uganda−Policies for Improved Land Management Dataset, 1999-2001

This survey was conducted during the “Policies for Improved Land Management Project in Uganda, 1999-2003.” The long term objective of the project was to contribute to improved land management in Uganda, in order to increase agricultural productivity, reduce poverty and ensure sustainable use of natural resources, with an immediate purpose of helping policy makers identify and assess policy, institutional and technological strategies to improve land management.

The questionnaires were administered to 107 communities, the lowest administrative units in Uganda called Local Council 1 or LC1. The study region covered most of Uganda, including more densely populated and more secure areas in the southwest, central, eastern and parts of the north, representing seven of the nine major farming systems of the country. Within the study region, communities were selected using a stratified random sample, with the stratification based on population density and development domains defined by the different agro-ecological and market access zones. One hundred villages were selected in this way. Additional communities were purposely selected in southwest Uganda, where the African Highlands Initiative is conducting research, and in Iganga district, where the International Center for Tropical Agriculture (CIAT) is conducting research.

Topics covered in the LC1 survey included community concerns and priorities, establishment and change of local council boundaries, population change, use of local council revenue, infrastructure and services, programs and organizations, land rights, and collective resource management.

Usually, each LC1 had one village, i.e. a cluster of households living in the neighborhood. In the case where the LC1 had more than one village, a village was randomly selected for the village level survey. Topics in the village survey included livelihood strategies, land use, land tenure and land markets, labor, wage rates and credit, crop production, commercialization and management, livestock management and commercialization, tree product and commercialization. In both the LC1 and village surveys, interviews were conducted with a group of representative people from each selected community.

Ethiopian Rural Household Surveys (ERHS), 1989-2009

The Ethiopia Rural Household Survey (ERHS) is a unique longitudinal household data set covering households in a number of villages in rural Ethiopia. Data collection started in 1989, when a team visited 6 farming villages in Central and Southern Ethiopia. In 1989, IFPRI conducted a survey in seven Peasant Associations located in the regions Amhara, Oromiya and the Southern Ethiopian People’s Association (SNNPR). Civil conflict prevented survey work from being undertaken in Tigray. Under extremely difficult field conditions, household data were collected in order to study the response of households to food crises. The study collected consumption, asset and income data on about 450 households. In 1994, the survey was expanded to cover 15 villages across the country. An additional round was conducted in late 1994, with further rounds in 1995, 1997, 1999, 2004, and 2009. In addition, nine new villages were selected giving a sample of 1477 households. The nine additional communities were selected to account for the diversity in the farming systems in the country, including the grain-plough areas of the Northern and Central highlands, the enset-growing areas and the sorghum-hoe areas. Topics addressed in the survey include household characteristics, agriculture and livestock information, food consumption, health, women’s activities, as well as community level data on electricity and water, sewage and toilet facilities, health services, education, NGO activity, migration, wages, and production and marketing.

Ethiopia Nile Basin Climate Change Adaptation Dataset

The household survey was carried out in the Nile River Basin in Ethiopia. The household sampling frame in Ethiopia was developed to ensure representation for the Nile River Basin at the woreda (district) level regarding level of rainfall patterns in terms of both annual total and variation; the four classes of traditionally defined agro-ecological zones (AEZs) found in the basin; vulnerability of food production systems (through the proxy of frequency of food aid in the past ten years); and irrigation prevalence. All data used for the sample frame is from the Atlas of the Ethiopian Rural Economy (Benson et al., 2006). Each woreda was classified based on : agroecological zone (Kolla, Weynadega, Dega, and Bereha), the percent of cultivated land under irrigation (no data, 0-2%, 2-4%, 4-8%, and 8% or greater), average annual rainfall (0-854mm, 854-1133mm, 1133-1413mm, 1413-1692mm, 1692mm or greater), rainfall
variability (coefficient of variation for annual rainfall), and vulnerability (number of years of food aid received in the past 10 years). Twenty woredas were selected such that across each of the above dimensions the proportion falling into each class for the sample matched as closely as possible the proportions for the entire Ethiopian Nile basin. Peasant associations (administrative units lower than districts) were also purposely selected to include households that irrigate their farms. One peasant association was selected from every woreda for a total of 20 peasant associations. Random sampling was used in selecting 50 households from each peasant administration within the 20 woredas. Thus, the final dataset contains 1,000 observations from 20 woredas in 5 regional states in Ethiopia (Tigray, Amhara, Oromiya, Benishangul Gumuz, and Southern Nations Nationalities and Peoples (SNNP)). The related South Africa Limpopo Basin Climate Change Adaptation dataset is also available from IFPRI’s website at www.ifpri.org/dataset/south-africa-limpopo-basin-climate-change-adaptation-dataset.