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This thesis concerns the relationship between norms, property and environmental governance of agriculture. The approach is broadly within the behaviouralist tradition, and addresses the role of what are characterized as stewardship norms in influencing environmental activity in lowland farming. The first half encompasses a theoretical review of models of behaviour, property rights, and value studies in agricultural economics, and develops a theoretical framework establishing the importance of the interrelationship between formal principles of governance and informal normative assumptions regarding implied rights in land and beliefs in the legitimacy of particular governance regimes. In the second half, the results of a farm survey conducted in East Anglia, UK, are reported. The survey utilizes a semi-structured factor analytic method, Q methodology, to identify different perspectives on agricultural stewardship, and combines this with a structured questionnaire which describes the structural and personal characteristics of farmers, some aspects of their environmental management behaviour, and their reactions to a set of proposed policy principles. The results indicate that normative perspectives can be effectively studied using Q methodology, and that some significant relationships can be found with both behaviour and attitudes towards policy. However the nature of the sample, which was non-random, and the small sizes of some attitudinal clusters identified makes much of the analysis suggestive rather than definitive. The rationale for the study can be related to recent work on the boundaries between the old and the New Institutional Economics (NIE) which suggests that the relationship between external institutions and individual motivation is reflexive. In this sense, economic agents take normative cues from the external framing of choices, which lead to the selection of appropriate behaviours in response to framing effects.

The overall aim of this study was to assess the robustness of the lawn ecosystem to increased temperature and imposed drought in line with current scenarios of climate change. The practical aim was to identify everyday lawn treatments (fertilizer and leaving clippings) which are sustainable and increase tolerance of, or recovery from, these climatic conditions. Investigations of lawn growth rates and colour indicated that the lawn recovers from the range of climatic conditions tested (3°C temperature elevation and two or three month summer drought), that recovery is rapid following drought, and under the extreme combination of heat and drought, the lawn ecosystem is still persistent. Warming leads to increased ET and reduced soil moisture and this in turn decreases lawn growth and quality, particularly in the summer. However, growth and colour rapidly return to normal levels when soil moisture increases in the autumn. When soil moisture is non-limiting, warming can lead to increases in lawn growth. Similarly, fertilizer increases growth and greenness when environmental conditions are non-limiting but under drought or warming fertilizer can lower ecosystem robustness. Drought leads to increases in nitrate leaching as does heating to a lesser extent and heating can also increase plant uptake of nitrogen. Nitrate losses from highly fertilized plots are severe and losses can also occur when clippings are left on the lawn. In clippings treatments nitrate leaching is higher in unheated than heated plots where plant uptake of nitrogen is also higher. Leaving clippings is effective in the autumn recovery of heated and unheated plots. This study has shown that a no-management approach to summer drought and warmer temperatures is valid if browning-off is acceptable, that management for autumn recovery rather than summer persistence is appropriate, and that clippings are better for lawn robustness than fertilizer.

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Mechanistic crop simulation models predict crop growth and development in response to different environments, cultivar grown, and agricultural inputs made. They are mathematical descriptions of crop processes at spatial and temporal scales appropriate to the problem they address, such models are indispensable for exploring scenarios which would be prohibitively costly or time-consuming to analyse directly. Assessment of model predictive skill is an integral part of model development which should include model validation: the comparison of measured and simulated data, validation can be used to test hypotheses about the mechanisms of plant growth and development, since a valid model demonstrates that the mechanisms it contains are justified.

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