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We synthetise large amount of data to assess whether farming practices have positive or negative effects on the environment, climate and productivity. Our starting point is the relatively large number of meta-analyses (MAs) published in agricultural science. A meta-analysis is the systematic statistical synthesis of the results of many independent individual experiments. Therefore, MAs allow to explore general trends beyond the context-dependence of large numbers of experimental studies and identify key moderating factors. MA Meta-analysis has become a gold standard method for quantitative research synthesis, and scientists can use the growing number of MAs available to inform decisions of policy makers.

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We assess the overall effect of each farming practice on a specific impact based on the scientific evidence found in the published MAs. As each MA involves a number of individual studies, the assessment of impacts relies on the statistical analysis of a large number of results obtained mainly in field experiments, and sometimes in lab experiments or from model simulations.

According to the statistical analysis provided by MAs, we categorize classify the results as ‘Positive’, ‘Negative’ or ‘No-effect’, where proper when an adequate statistical analysis is available, testing comparing the effect of a farming practice , as compared to with a control. When statistical analysis is not available, we rate classify the results as ‘Uncertain’.

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Three types of reports are generated from the extracted data:

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