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• W2912232914 abstract "Achieving global agricultural sustainability while maximizing yield and yield quality is a key issue for which we should consider all options. We here show that agricultural ‘classical’ management options are likely underexplored: we estimate that the most intensely researched sites cover only a fraction of a percent of available options. Since some of these untested options may prove important for yield, yield quality and sustainability, we suggest the use of distributed trials, exploring a broader range of options; this should occur in parallel to employing new technologies. Agroecosystem management relies on a rather limited suite of management factors, mainly fertilization regime and application of pesticides, tillage, and crop rotation. Given the formidable challenge to achieve agricultural sustainability, while feeding a growing number of people, new approaches have been proposed, and are currently being actively explored. These include microbiome engineering (Mueller & Sachs, 2015) or core microbiome inoculation (Toju et al., 2018), and the use of advanced technological options (drones, robotics) among others (Walter et al., 2017). However, have we fully explored the ‘classical’ options? Each of these few major management factors mentioned earlier may at first sight seem monolithic, but upon closer scrutiny it is clear that each factor harbors a bewildering range of treatment levels. Importantly, these factor levels can essentially be linked in a virtually combinatorial fashion, with perhaps only a few combinations excluded on first principles (e.g. nitrogen (N)-fixing plants and certain levels of N fertilization). This means there is actually a rather wide range of factor combinations. We provide an estimate of possible treatment combinations by reviewing the existing literature. Specifically, for each of the major management practices (tillage, crop rotation and fertilization) we identified meta-analyses or synthesis papers, which we used to subdivide these practices into different categories and then specific levels (Fig. 1; Table 1; for more detail also see Supporting Information Table S1). We thus estimate over 285 000 different management combinations. This calculation did not consider cover crops or other aspects of agroecosystem diversification, and only coarsely represented pesticide use. We also did not consider crop-specific chemical fertilization rates other than recommended, and higher or lower than recommended (e.g. we did not include fertigation). We also did not consider factorial combinations of levels within categories. We thus believe that this estimate is rather conservative. However, some of the combinations are perhaps to be excluded from first principles or because they are locally not feasible (e.g. because of unavailability of technical means). Either way, it is clear that this number is going to be in the hundreds of thousands. The agriculture we see today is the result of perhaps 10 000 years of trial and error and continuous learning. Nevertheless, it is also clear that there is a lot of room for improvement in terms of optimizing sustainability and yield, and that the scientific method is required to establish such improvement (Cui et al., 2018). Therefore, it is helpful to ask: how many of the factor combinations we estimated earlier have actually been formally scientifically tested at any one site or region? To ascertain this, we turned to perhaps one of the best-researched agroecosystems, Rothamsted, site of the longest-running agricultural experiments (Rothamsted Research, 2006). Rothamsted has overall tested 314 treatment levels, which we calculated as the sum of Broadbalk, Park Grass and Hoosfield experiments (see Table S2). Most agricultural experiment stations cover a lot fewer treatment combinations, especially in terms of long-term experiments necessary to ascertain key sustainability parameters. Thus, we believe this number of over 300 to be an absolute maximum estimate. This means that for even the most well researched site we have currently only explored c. 0.11% of the possible parameter space of agricultural management, which means that there is a lot of room for optimization and potential discovery. Importantly, when the focus moves from crop yield, or yield quality, to other parameters relevant for agricultural sustainability, like soil aggregation and soil carbon storage, soil biodiversity, or socio-economic factors, this percentage will be much lower. What can we do? Clearly, conducting full or fractional factorial field experiments at an agriculturally relevant scale is not an option with this many treatment levels, not even on a strongly reduced set of combinations, because of statistical and experimental design considerations (Scheiner & Gurevitch, 2001), such as lack of power to detect factor interactions. The only tractable solution seems to include distributed trials, as exemplified by the massive scale of the study of Cui et al. (2018). In this study, many millions of farmers participated, using over 13 000 sites, an achievement only possible by a coordinated network involving an interaction of scientists, extension staff, agribusiness personnel and the farmers. Such distributed trial approaches should use local farmer's knowledge to narrow down management options by excluding combinations that are unlikely to be successful or that are not locally feasible or applicable for a number of reasons (e.g. soil type, climate characteristics, and economic and technological situations). Many design options can be discussed for setting up such a trial system. For example, in a comparable setting (e.g. part of a country or region with comparable soil and site conditions), one of the management categories (Fig. 1), for example pesticide use, could be systematically varied while keeping all others constant. Alternatively, one could use randomly selected combinations of levels across a broader suite of sites. Either way, it will be essential to include a very large number of sites to more systematically cover the entire parameter space of management options. Support from global satellite monitoring could be very useful in this endeavor (e.g. Sentinel-2 biomass data). It will also be important to design standardized measurement protocols that not only include yield aspects but also parameters informative for sustainability (e.g. greenhouse gas emission, soil aggregation). This way we could discover novel, and perhaps unexpected, management combinations to enhance crop yield, yield quality, soil biodiversity or sustainability, or generally, agroecosystem multifunctionality. Such promising candidates, standing out in this broad-scale screening process, could then be more rigorously tested in classical trial approaches, or directly more widely employed to test the initial results for robustness. We suggest that implementing such distributed trials, aimed at further exploring the parameter space of ‘classical’ management options, will be an important step towards agricultural sustainability and food security. The data supporting the findings of this study are provided in Tables S1 and S2. MCR acknowledges funding from the German Federal Ministry for Research and Education (BMBF) for the project INPLAMINT and from an European Research Council (ERC) Advanced Grant (Gradual Change). Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office. Table S1 Assessing the agricultural parameter space, categorized by management practice, and their respective categories and levels. Table S2 Management practices covered at Rothamsted (Broadbalk, Park Grass, and Hoosfield). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article." @default.
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• W2912232914 date "2019-03-07" @default.
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• W2912232914 title "Exploring the agricultural parameter space for crop yield and sustainability" @default.
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