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The spatial configuration of the agricultural landscape and the organization of farming practices have strong effects on the flows and balances of nutrients and organic matter and their environmental impacts. This has been acknowledged to varying degree in the different generations of action plans, legislation and strategies implemented in Denmark to reduce the impact on the terrestrial and aquatic environment. The aim of this paper is to analyse three decades of environmental regulation of the farming sector in Denmark, with a focus on the underlying landscape paradigms and agro- and landscape ecological models. The study includes analyses of policy documents, action plans, legislation, guidelines and other implementation papers. We identify spatially targeted measures and underlying assumptions regarding the landscape and assess the landscape component of models used for policy design and evaluations. Additionally, we interview key stakeholders involved in the different stages of implementation to identify the main drivers and motives in policy design. There has been a considerable development of ecological models and availability of data during the three decades of N, P and OM related regulation in Denmark. In the early plans of the 1980s focus was on simple measures such as improving manure storage facilities and handling with no or few links to landscapes. This changed with the introduction of fertilization plans (1987) and norm based fertilizer rules (1991). However, the measures were still largely based on simple field-scale models focusing on leaching from the root-zone ignoring spatial variation and horizontal fluxes. Gradually more advanced models that included the landscape level were introduced along with the implementation of measures with spatial components such as wetland restoration, environmental approval of husbandry farms, buffer-zones and voluntary schemes targeted to designated areas. Today tools include the spatial configuration and organization of the landscape in the modelling and assessment of nutrient fluxes and balances. However, the use of modelling tools is increasingly questioned as to their reliability arguing that on-site specific measurements rather than model results are needed. The models and data required to design and assess spatially differentiated measures to reduce the nutrients load from Danish agriculture has been developed and improved considerably since the implementation of the first mitigation measures in the mid-1980s. Also, the need for spatially differentiated policies was identified at an early stage of the process we have described. However, the implementation of spatially differentiated measures has been slow as a consequence of the Danish approach to (non)targeting. Today there is a broad consensus among stakeholders and farmers on the need for spatially differentiation and the next generation of measures are likely to reflect this. This will target the mitigation efforts towards high load areas affecting vulnerable ecosystems, whereas it is still uncertain if it also will include fewer restrictions on farming in areas which have less environmental impact. This work is partly funded by the Strategic Research Alliance DNMARK (2013–2017) funded by The Danish Innovation Foundation.

Maize (Zea mays L.) is the most prominent crop in the Northeast Farming Region of China (NFR), and drought has been the largest limitation for maize production in this area during recent decades. The question of how to adapt maize production to drought has received great attention from policy makers, researchers and farmers. In order to evaluate the effects of adaptation strategies against drought and examine the influences of policy supports and farmer households' characteristics on adopting decisions, a large scale household survey was conducted in five representative maize production counties across NFR. Our survey results indicated that using variety diversification, drought resistant varieties and dibbling irrigation are the three major adaptation strategies against drought in spring, and farmers also adopted changes in sowing time, conservation tillage and mulching to cope with drought in spring. About 20% and 18% of households enhanced irrigation against drought in summer and autumn, respectively. Deep loosening tillage and organic fertilizer are also options for farmers to resist drought in summer. Maize yield was highly dependent on soil qualities, with yields on land of high soil quality approximately 1050 kg/ha and 2400 kg/ha higher than for normal and poor soil conditions, respectively. Using variety diversification and drought resistant varieties can respectively increase maize yield by approximately 150 and 220 kg/ha under drought. Conservation tillage increased maize yield by 438–459 kg/ha in drought years. Irrigation improved maize yield by 419–435 kg/ha and 444–463 kg/ha against drought in summer and autumn, respectively. Offering information service, financial and technical support can greatly increase the use of adaptation strategies for farmers to cope with drought. However, only 46% of households received information service, 43% of households received financial support, and 26% of households received technical support against drought from the local government. The maize acreage and the irrigation access are the major factors that influenced farmers' decisions to apply adaptation strategies to cope with drought in each season, but only 25% of households have access to irrigation. This indicates the need for enhanced public support for farmers to better cope with drought in maize production, particularly through improving access to irrigation.

AbstractThis article discusses the problem of implementing scientific knowledge in practice. The discussion is based on a case study of barriers to implementing research‐based principles of sustainable organic arable farming. The current literature tends to see this problem either as a dissemination issue (the linear approach) or as a failure of science to address real‐world problems (the co‐constructive approach). We argue that Luhmann's theory of social systems offers a way to understand the shortcomings of existing approaches and enables us to identify knowledge gaps that have often been overlooked so far. This theoretical claim is substantiated by an empirical analysis based on 10 in‐depth interviews with owners of organic arable farms. The farmers were interviewed about barriers to the implementation of research‐based knowledge in practice. The analysis points to the operational closure and the observational blindness of both research systems and farming systems as the keys to understanding knowledge gaps and suggests new ways of dealing with the problems of implementing research knowledge in practice.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Process-based modelling of crop growth is an effective way of representing how crop genotype, environment and management interactions affect crop production to aid tactical and strategic decision making1. Process-based crop models are increasingly used to project the impact of climate change on crop yield2. However, current models produce different results, creating large uncertainty in crop yield simulations3. A model inter-comparison study within the Agricultural Model Inter-comparison and Improvement Project (AgMIP)4 of 29 widely used wheat models against field experimental data revealed that there is more uncertainty in simulating grain yields from the different models than from 16 different climate change scenarios3. The greatest uncertainty was in modelling crop responses to temperature3,5. Similar results were found with rice and maize crops6,7. Such uncertainty should be reduced before informing decision making in agriculture and government policy. Here, we show contrasting differences in temperature response functions of key physiological processes adopted in the 29 crop models. We reveal opportunities for improving simulation of temperature response in crop models to reduce the uncertainty in yield simulations. We aim to reassess the scientific assumptions underlying model algorithms and parameterization describing temperature-sensitive physiological processes, using wheat, one of the most important staple crops globally, as an example. We hypothesized that: (1) the difference among models in assumed temperature responses is the largest source of the uncertainty in simulated yields; and (2) the uncertainty in the multi-model ensemble results can be reduced by improving the science for modelling temperature response of physiological processes. Temperature affects crop performance primarily through its impact on (1) the rate of phenological development from seed germination to crop maturity, including the fulfilment of cold requirement (vernalisation); (2) the initiation and expansion of plant organs; (3) photosynthesis and respiration, considered either separately or combined as net biomass growth simulated using radiation use efficiency (RUE)8; and (4) the senescence, sterility or abortion of plant organs. All 29 models simulate these processes, except for sterility and abortion, in response to temperature change. Here, we compare the temperature functions of these four categories of physiological processes built into the 29 wheat models and identify the representative response types. We analyse how different temperature response functions affected simulations of wheat growth compared to observations in a field experiment8,9,10, in which well-fertilized and irrigated wheat grew under contrasting sowing dates and temperature environments (Hot Serial Cereal (HSC) experiment). We further evaluate the impact of the different response types by implementing them in two models (APSIM and SiriusQuality) and analysing their results against the HSC data and an additional global dataset from the International Heat Stress Genotpye Experiment (IHSGE)8 carried out by the International Maize and Wheat Improvement Center (CIMMYT). More importantly, we derive, based on newest knowledge and data, a set of new temperature response functions for the key physiological processes of wheat and demonstrate that when substituted in four wheat models the new functions reduced the error in grain yield simulations across seven global sites with different temperature regimes covered by the IHSGE data.