Smallholder agriculture is a major source of income and food for developing nations. With more frequent drought and increasing scarcity of arable land, more accurate land-use planning tools are needed to allocate land resources to support regional agricultural activity. To address this need, we created Land Capability Classification (LCC) system maps using data from two digital soil maps, which were compared with measurements from 1305 field sites in the Dosso region of Niger. Based on these, we developed 250 m gridded maps of LCC values across the region. Across the region, land is severely limited for agricultural use because of low available water-holding capacity (AWC) that limits dry season agricultural potential, especially without irrigation, and requires more frequent irrigation where supplemental water is available. If the AWC limitation is removed in the LCC algorithm (i.e., simulating the use of sufficient irrigation or a much higher and more evenly distributed rainfall), the dominant limitations become less severe and more spatially varied. Finally, we used additional soil fertility data from the field samples to illustrate the value of collecting contemporary data for dynamic soil properties that are critical for crop production, including soil organic carbon, phosphorus and nitrogen.

Authoritative economic growth forecasts are often optimistically biased. One possible reason is that growth often has negatively skewed variation--negative shocks tend to have larger magnitudes than positive shocks, as the Great Recession and COVID-19 crisis illustrate. Negative skewness means that average growth over decades is smaller than typical-year (median or mode) growth, which would positively bias forecasts based on typical years. Here, we quantify this aspect of negative skewness in real per-capita GDP growth since the Industrial Revolution (1820-2016), by comparing medians and means across countries, regions, and time windows. Over decadal periods, we find mean growth rates to be <1%/y smaller than median growth rates in most countries and regions (median 0.23%/y across countries). Surprisingly, we find these differences are driven by negative skewness in both large- and medium-magnitude shocks, rather than only large shocks ('black swan' events). We also find our measure of negative skewness correlated with slow average per-capita GDP and population growth, high per-capita GDP growth volatility, and high per-capita GDP and population, building on previous studies. We find that recent over-projections of growth--by the International Monetary Fund (IMF), the U.S. Congressional Budget Office (CBO), and the Shared Socioeconomic Pathway (SSP) scenarios informing climate change research--have mostly been larger than can be explained solely by negative skewness, suggesting other sources of bias exist.

<p>The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 35 years of multifactorial, spatially-explicit simulation data from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM), we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soils across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-determined meta-models are highly accurate (R² = .97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the meta-model’s performance compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-model accurately predicts broad SOC dynamics while it often  underestimates  the measured SOC responses to management.  Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications.While more accurate input data, calibration, and validation will l be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.</p>

Higher education institutions have long played a key role in solving society's most pressing problems. However, as the scale and complexity of socio‐environmental problems has grown, there has been a renewed debate about the role that academic institutions should play in developing solutions and how institutional structures should be redesigned to encourage greater interdisciplinarity. In the following pages, we present a graduate student perspective on this debate. Specifically, we identify challenges facing interdisciplinary graduate student researchers and present a series of recommendations for how institutions can better prepare them to become the next generation of leaders in interdisciplinary, action‐oriented research focused on solving socio‐environmental problems.

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