Measuring the immeasurable: A structural equation modeling approach to assessing soil health.

Soil health is recognized as an important ecosystem property sensitive to human impact. As a concept, soil health cannot be directly measured, and so assessment and modeling efforts largely rely upon key biological, chemical, and physical indicators. Efforts to develop an overall soil health index are largely lacking due to significant statistical challenges and the necessity for regional calibration. Taken from the field of educational research, structural equation modeling (SEM) is an attractive approach to enhance the reliability and validity of soil health scoring. Therefore, SEM may be utilized to advance research efforts to understand management practices impacts on soil health. Our objectives were to develop a robust scoring function that (i) captures the concept of soil health and latent variables, (ii) adjusts scores by inherent soil properties and legacy of intensive land use to adequately reflect our regional conditions and contemporary land management, and (iii) meets the diverse practitioner needs. Through this process, we refined our minimum dataset of soil health indicators and reconceptualized soil health indicators into functional properties. Our results support the development of a robust single level or a multilevel SEM model-depending on the practitioner's goals-that accounts for repeated sampling or pseudoreplication. While the SEM scoring functions were highly related to the conventional scoring approach, SEM outperformed the conventional methods in terms of its wider distribution of scores-and thus enhanced discriminatory power on the lower and higher range of scores. We also confirmed that the SEM scoring function that includes adjustments for mineralogy and legacy of intensive land use successfully differentiates among contemporary management practices and land use. Therefore, we have confidence that the tool is reliable and appropriate to further examine more nuanced impacts of land use change and management practices within a given land use across time and space covering a diversity of soils. (300 words).

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency.

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

Quantification of global and national nitrogen budgets for crop production.

Input-output estimates of nitrogen on cropland are essential for improving nitrogen management and better understanding the global nitrogen cycle. Here, we compare 13 nitrogen budget datasets covering 115 countries and regions over 1961-2015. Although most datasets showed similar spatiotemporal patterns, some annual estimates varied widely among them, resulting in large ranges and uncertainty. In 2010, global medians (in TgN yr-1) and associated minimum-maximum ranges were 73 (64-84) for global harvested crop nitrogen; 161 (139-192) for total nitrogen inputs; 86 (68-97) for nitrogen surplus; and 46% (40-53%) for nitrogen use efficiency. Some of the most uncertain nitrogen budget terms by country showed ranges as large as their medians, revealing areas for improvement. A benchmark nitrogen budget dataset, derived from central tendencies of the original datasets, can be used in model comparisons and inform sustainable nitrogen management in food systems.

Inconsistencies undermine the conclusion that agriculture is a dominant source of NO x in California.

Almaraz et al. reported that agricultural soils are a dominant source of NO x pollution in California (20 to 32% of total statewide NO x emissions). However, this conclusion may be undermined by the lack of agreement between their modeled estimates and previously reported empirical measurements, the extrapolation of NO x fluxes during hot moments to derive annual estimates, and the overestimation of nitrogen fertilizer consumption in California.