Linking crop- and soil-based approaches to evaluate system nitrogen-use efficiency and tradeoffs

REFERENCE:
Rafael A. Martinez-Feria, Michael J. Castellano, Ranae N. Dietzel, Matt J. Helmers, Matt Liebman, Isaiah Huber, Sotirios V. Archontoulis. Linking crop- and soil-based approaches to evaluate system nitrogen-use efficiency and tradeoffs. Agriculture, Ecosystems & Environment, 256: 131-143, https://doi.org/10.1016/j.agee.2018.01.002.

OBJECTIVE:

1. Develop an approach to  concurrently evaluate cropping system Nitrogen-use efficiency from both crop and soil perspectives
2. Demonstrate the integrated application of the framework by analyzing N cycling of maize and soybean cropping systems of the Midwestern United States​

Fig 1. Conceptual box diagram of the major N fluxes often used to calculate budgets and efficiency indices.

RATIONALE:
At the cropping system-level, NUE is often evaluated using N budgets (Fig. 1). These are accounts of N being added or subtracted from the system, with different methodologies arising depending on where the system boundaries are drawn. NUE calculated from crop-based budgets (NUECrop), works well from an agronomic perspective to identify strategies that maximize yield and minimize inputs. Yet, it has the potential to mischaracterize environmental impacts given the uncertainties related to the fate of N. NUE calculated from soil-based N budgets (NUESoil), can help identify systems where the soil N pool is in decline, threatening the long-term sustainability of soil fertility. Its interpretation, however, places little emphasis on productivity or N losses, and does not necessarily provide a concise approach to elucidate how tightly N is cycled within systems. The new system-level NUE (sNUE), which we define as the ratio of NUECrop to NUESoil, links both crop- and soil-based approaches into an easily interpretable metric, which succinctly characterizes N cycling and facilitates comparison of systems that differ in biophysical controls on N dynamics.

METHODOLOGY:
We used a well-calibrated model (APSIM) to simulate long-term, high-resolution, multi-process data on N cycling. The generated data allowed us to dissect the fundamental assumptions of NUE metrics, and discuss their limitations.

Fig 2. We compared this sNUE to traditional metrics (left pane) and showed how its application can yield insights about N cycling dynamics and tradeoffs (right pane).

​KEY FINDINGS:

1. The maize-soybean rotation was highly efficient from the crop perspective. In the 35 years simulated, 87% of N inputs were removed in grains.
2. N losses (leaching + denitrification) were about 2.2 times greater than predicted by the crop-based budget (net surplus). This indicates that 45% of the N losses were due to inefficient N input use, while the rest originated from  the release of native soil N (poor N retention) due to asynchrony between N mineralization and N uptake.
3. Balancing production-environmental tradeoffs (high yields, low environmental losses) within each cropping system resulted on an overall yield penalty of 5–7% (Fig 2).
4. sNUE produced more stable estimates of system efficiency across weather years (Fig. 2) and provided distinct information from other known metrics.