Documenting and modeling the accretion of surface and subsoil organic carbon in agricultural Inceptisols reclaimed from Mediterranean sea marshes in Sardinia

High input agriculture in productive Inceptisols that were reclaimed from sea marshes offers an opportunity to study the increase of soil organic carbon (SOC) in soils with originally low SOC. We documented the current SOC content and its distribution with depth for several soil profiles

Soil Carbon and Nitrogen Dynamics of Integrated Crop-Pasture Systems with Annual and Perennial Forages

Increased demand for food and bioenergy crops and the subsequent intensification of crop production creates a challenge for the conservation of natural resources in Latin America and the world. In Uruguay, no-till cash-crop production area has increased from 0.4 to 1.5 million ha in the last decade (DIEA 2011) mostly at the expense of pastureland through expanding grain production to soils with lower land use capability. Production systems based on crop-pasture rotations shifted to a longer annual cropping phase with a shorter pasture phase, or to continuous annual crop-ping. Long-term experiments in the country have shown that the rotation of annual crops and perennial pastures minimizes soil erosion in tilled systems, maintaining a positive long-term soil carbon (C) and nitrogen (N) balance that contrasts with C and N losses in annual cropping systems (García-Préchac et al. 2004). Research and extension on soil conservation in crop-pasture systems have led to a massive adoption of no-tillage practices, reaching about 90% of cash crop area by the 2009 growing season (DIEA 2011). However, the gradual increase in no-till adoption by farm operators has been associated with a dramatic increase in continuous annual cropping to the detriment of the pasture phase of the rotation. Our overarching question is: What is the impact of an increased frequency of annual crops in the C and N cycling of these systems? The objective of this study was to assess the impact of the pasture phase and cropping intensity on the soil C and N cycling of an Oxyaquic Argiudoll soil of eastern Uruguay using long term field experimental data and a cropping systems simulation model

USCID fifth international conference

Presented at the fifth international conference on irrigation and drainage, Irrigation and drainage for food, energy and the environment on November 3-6, 2009 in Salt Lake City, Utah.Includes bibliographical references.The urban water demand in Southwest Texas has grown rapidly in recent years due to large population increase. Regulated deficit irrigation (RDI) is one important measure for saving water while maintaining crop yield/ net benefit. An RDI field experiment was conducted at the Texas AgriLIFE Research and Extension Center at Uvalde in the summer of 2008 to examine the water saving potential. Seven irrigation schemes and four varieties were assigned to the experimental field to test their effects on lint yield. The results showed that: 1) The threshold of the replacement ratio is between 0.7 and 0.8 in fixed ratio irrigation schemes. Dynamic irrigation schemes showed a higher potential to save irrigation water. 2) The fiber quality was affected more by varieties than by irrigation schemes. A 50X (fixed 50% ratio) scheme has the potential risk to produce relatively lower quality cotton fiber by affecting fiber length and fiber yellowness. Considering its negative effect on lint yield as well, the 50X scheme is definitely not recommended. The two dynamic irrigation schemes, 50D and 70D, showed no negative effect on fiber quality. The 70D scheme has some potential to increase the fiber quality in fiber length, uniformity, fiber strength and reflectance; however, this scheme uses more irrigation water that the 50D scheme. Although further research is needed before making definitive conclusions, both dynamic schemes could be applied to maintain lint yield and fiber quality while saving more water, compared to the fixed ratio irrigation schemes

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.Office of the VP for Researc

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

How do various maize crop models vary in their responses to climate change factors?

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

Priming of soil organic carbon decomposition induced by corn compared to soybean crops

The rate of soil organic carbon (CS) loss via microbial respiration (decomposition rate k, y1), and the rate of stabilization of vegetation inputs (CV) into CS (humification rate h, y1) are usually considered inde- pendent of CV. However, short-term laboratory studies suggest that the quality and quantity of CV con- trols k, which is often referred to as a priming effect. We investigated how the chemical composition of different residues, (corn and soybean) controls k and h under field conditions in a no-till ecosystem. Using CV-driven shifts in d13C, we estimated changes in carbon (C) stocks, k and h of both the labile particulate organic matter fraction (CPOM) and the stabilized mineral associated organic matter fraction (CMAOM). After two years of high C inputs (corn: 4.4 Mg ha1 y1 aboveground and C:N 1⁄4 78; soybean: 3.5 Mg ha1 y1, C:N 1⁄4 17), we found no changes in CPOM and CMAOM stocks in the top 5-cm of soil or in deeper layers. However, CMAOM in corn had higher k (0.06 y1) and C output fluxes (0.67 Mg ha1 y1) than in soybean (0.03 y1 and 0.32 Mg ha1 y1), but similar rates and fluxes in CPOM in the top 5-cm of soil. In addition, while C inputs to CPOM were also similar for both crops, C inputs from CV to CMAOM were higher in corn (0.51 Mg ha1 y1) than in soybean (0.19 Mg ha1 y1). Overall, corn plots had higher k and C inputs into CMAOM and therefore higher C cycling in this fraction. Our data suggests that the type of crop residue strongly influences C cycling in the topsoil of no-till cropping systems by affecting both the stabilization and the decomposition of soil organic matter.Fil: Mazzilli, Sebastian R.. Universidad de la Republica. Facultad de Agronomía. Estación Experimental Mario Alberto Cassinoni; UruguayFil: Kemanian, Armen R.. Pennsylvania State University. Department of Plant Science; Estados UnidosFil: Ernst, Oswaldo R.. Universidad de la Republica. Facultad de Agronomía. Estación Experimental Mario Alberto Cassinoni; UruguayFil: Jackson, Robert B.. Duke University. Nicholas School of the Environment and Center on Global Change; Estados UnidosFil: Piñeiro, Gervasio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentin