Tracking nitrogen losses in a greenhouse crop rotation experiment in North China using the EU-Rotate_N simulation model

Vegetable production in China is associated with high inputs of nitrogen, posing a risk of losses to the environment. Organic matter mineralisation is a considerable source of nitrogen (N) which is hard to quantify. In a two-year greenhouse cucumber experiment with different N treatments in North China, non-observed pathways of the N cycle were estimated using the EU-Rotate_N simulation model. EU-Rotate_N was calibrated against crop dry matter and soil moisture data to predict crop N uptake, soil mineral N contents, N mineralisation and N loss. Crop N uptake (Modelling Efficiencies (ME) between 0.80 and 0.92) and soil mineral N contents in different soil layers (ME between 0.24 and 0.74) were satisfactorily simulated by the model for all N treatments except for the traditional N management. The model predicted high N mineralisation rates and N leaching losses, suggesting that previously published estimates of N leaching for these production systems strongly underestimated the mineralisation of N from organic matter

Simulation of greenhouse gases following land-use change to bioenergy crops using the ECOSSE model : a comparison between site measurements and model predictions

This work contributes to the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI). We acknowledge the E-OBS data set from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu).Peer reviewedPublisher PD

Grassland carbon sequestration and emissions following cultivation in a mixed crop rotation

Grasslands are potential carbon sinks to reduce unprecedented increase in atmospheric CO2. Effect of age (1 to 4-yr-old) and management (slurry, grazing multispecies mixture) of a grass phase mixed crop rotation on carbon sequestration and emissions upon cultivation was compared with 17-yr-old grassland and a pea field as reference. Aboveground and root biomass were determined and soils were incubated to study CO2 emissions after soil disturbance. Aboveground biomass was highest in 1-yr-old grassland with slurry application and lowest in 4-yr-old grassland without slurry application. Root biomass was highest in 4-yr-old grassland, but all 1 to 4-yr-old grasslands were in between the pea field (0.81±0.094 g kg-1 soil) and the 17-yr-old grassland (3.17±0.22 g kg-1 soil). Grazed grasslands had significantly higher root biomass than cut grasslands. There was no significant difference in the CO2 emissions within 1 to 4-yr-old grasslands. Only the 17-yr-old grassland showed markedly higher CO2 emissions (4.9 ± 1.1 g CO2 kg-1 soil). Differences in aboveground and root biomass did not affect CO2 emissions, and slurry application did not either. The substantial increase in root biomass with age but indifference in CO2 emissions across the age and management in temporary grasslands, thus, indicates potential for long-term sequestration of soil C

MULTIPLE-OBJECTIVE DECISION MAKING FOR AGROECOSYSTEM MANAGEMENT

Multiple-objective decision making (MODEM) provides an effective framework for integrated resource assessment of agroecosystems. Two elements of integrated assessment are discussed and illustrated: (1) adding noneconomic objectives as constraints in an optimization problem; and (2) evaluating tradeoffs among competing objectives using the efficiency frontier for objectives. These elements are illustrated for a crop farm and watershed in northern Missouri. An interactive, spatial decision support system (ISDSS) makes the MODEM framework accessible to unsophisticated users. A conceptual ISDSS is presented that assesses the socioeconomic, environmental, and ecological consequences of alternative management plans for reducing soil erosion and nonpoint source pollution in agroecosystems. A watershed decision support system based on the ISDSS is discussed.Agribusiness,

Understanding soil fertility in organically farmed systems (OF0164)

This is the final report of the Defra project OF0164. Organic farming aims to create an economically and environmentally sustainable agriculture, with the emphasis placed on self-sustaining biological systems rather than external inputs. Building soil fertility is central to this ethos. ‘Soil fertility’ can be considered as a measure of the soil’s ability to sustain satisfactory crop growth, both in the short- and longer-term. It is determined by a set of interactions between the soil’s physical environment, chemical environment and biological activity. The aim of this project was, therefore, to provide a better scientific understanding of soil fertility under organic farming. The approach was to undertake a comprehensive literature review at the start of the project to assess and synthesise available information. Studies were then designed to address specific questions identified from the literature review. The literature review was written during the first year of the project. In addition to submitting written copies to DEFRA, the chapters were posted on a project website: www.adas.co.uk/soilfertility. The Review was based around key questions: • What are the soil organic matter characteristics and the roles of different fractions of the soil organic matter? • Do organically managed soils have higher levels of organic matter (SOM), with a resultant improvement in soil properties? • Is the soil biology different in organically managed soils, in terms of size, biodiversity and activity? • Do organically managed soils have a greater inherent capacity to supply plant nutrients? • What are the nutrient pools and their sizes? • What are the processes and rates of nutrient transfer in relation to nutrient demand? • What are the environmental consequences of organic management? The project also included a large amount of practical work. This necessarily covered a wide range of topics, which were examined in a series of separate studies: • Soil microbiology: a series of measurements focusing on two sites, undertaken by University of Wales Bangor (UWB) • Field campaigns in autumn 1999 and spring/summer 2000: separate field sampling campaigns focusing especially on nutrient pools, undertaken by HDRA, ADAS and IGER • Incubation studies: a series of three separate experiments to look in more detail at N dynamics, managed by ADAS, with support from IGER and HDRA From the literature review and the practical work, the following was concluded: Organic matter is linked intrinsically to soil fertility, because it is important in maintaining good soil physical conditions (e.g. soil structure, aeration and water holding capacity), which contribute to soil fertility. Organic matter also contains most of the soil reserve of N and large proportions of other nutrients such as P and sulphur. Field management data gathered from farmers showed, however, that organic matter returns are not necessarily larger in organic systems. Many non-organically farmed soils receive regular manure applications and the generally higher yielding crops on conventional farms may return larger crop residues. Conversely, many organic fields receive little or no manure, relying on the fertility building ley phase for organic matter input. This observation is important. Management practices within organic and non-organic systems are diverse, and all have consequences for soil fertility. The Executive Summary at the start of the main attached report has additional sections on Soil Structure, Soil Biology, and Nutrient Cycling with some greater detail on comparisons of organic and conventional management and the consequences for soil fertility

An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent.Multifunctional agriculture, Agent based modeling, Genetic Algorithm, Environmental Economics and Policy, Land Economics/Use,