Bianca N. Moebius-Clune

Use of an integrative soil health test for evaluation of soil management impacts.

Understanding the response of soil quality indicators to changes in management practices is essential for sustainable land management. Soil quality indicators were measured for 2 years under established experiments with varying management histories and durations at four locations in New York State. The Willsboro (clay loam) and Aurora (silt loam) experiments were established in 1992, comparing no-till (NT) to plow-till (PT) management under corn ( Zea mays L.)–soybean ( Glycine max L.) rotation. The Chazy (silt loam) trial was established in 1973 as a factorial experiment comparing NT versus PT and the crop harvesting method (corn silage versus corn grain). The Geneva (silt loam) experiment was established in 2003 with vegetable rotations with and without intervening soil building crops, each under three tillage methods (NT, PT and zone-till (ZT)) and three cover cropping systems (none, rye and vetch). Physical indicators measured were wet aggregate stability (WAS), available water capacity (AWC) and surface hardness (SH) and subsurface hardness (SSH). Soil biological indicators included organic matter (OM), active carbon (AC), potentially mineralizable nitrogen (PMN) and root disease potential (RDP). Chemical indicators included pH, P, K, Mg, Fe, Mn and Zn. Results from the Willsboro and Aurora sites showed significant tillage effects for several indicators including WAS, AWC, OM, AC, pH, P, K, Mg, Fe and Mn. Generally, the NT treatment had better indicator values than the PT treatments. At the Chazy site, WAS, AWC, OM, AC, pH, K and Mg showed significant differences for tillage and/or harvest method, also with NT showing better indicator values compared to PT and corn grain better than corn silage. Aggregate stability was on average 2.5 times higher in NT compared to PT treatments at Willsboro, Aurora and Chazy sites. OM was also 1.2, 1.1 and 1.5 times higher in NT compared to PT treatments at Willsboro, Aurora and Chazy sites, respectively. At the Geneva site WAS, SH, AC, PMN, pH, P, K and Zn showed significant tillage effects. The cover crop effect was only significant for SH and PMN measurements. Indicators that gave consistent performance across locations included WAS, OM and AC, while PMN and RDP were site and management dependent. The composite soil health index (CSHI) significantly differentiated between contrasting management practices. The CSHI for the Willsboro site was 71% for NT and 59% for PT, while at the Aurora site it was 61% for NT and 48% for PT after 15 years of tillage treatments.

Developing Standard Protocols for Soil Quality Monitoring and Assessment

Africa’s agricultural viability and food security depend heavily on its soil quality. However, while approaches to measuring air and water quality are widely established, standardized, publicly-available soil quality assessment protocols are largely non-existent. This chapter describes the process we have used in selecting and developing a set of inexpensive, agronomically meaningful, low-infrastructure-requiring indicators of soil quality (SQ), which make up the Cornell Soil Health Test (CSHT). In 2006, the CSHT was made available to the public in New York State (NYS), United States, similar to the widely available soil nutrient tests. Case studies show the CSHT’s success at measuring constraints in agronomically essential soil processes and differences between management practices in NYS. It thus helps farmers to specifically target management practices to alleviate quantified constraints. Such indicators have the potential to be developed into standardized soil quality tests for use by African agricultural non-governmental and government organizations and larger commercial farmers to better understand agricultural problems related to soil constraints and to develop management solutions. Low cost and infrastructure requirements make these tests excellent tools for numerous low-budget extension and NGO-based experiments established in collaboration with local small farmers, as well as to quantify the status and trends of soil degradation at regional and national scales.

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over-apply N fertilizers in maize ( L.) production environments, often resulting in large environmental N losses. Static Stanford-type N recommendation tools are typically promoted in the United States, but new dynamic model-based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt-N, a dynamic simulation tool that combines soil, crop, and management information with real-time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N-rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower-estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site-specific conditions, the Adapt-N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt-N can help reduce environmental impacts while sustaining farm economic viability.

Dynamic model-based recommendations increase the precision and sustainability of N fertilization in midwestern US maize production

Abstract The US Midwest encompasses one of the largest intensive maize (Zea mays L.) production environments in the world. Managing these lands in a more sustainable way is essential to reducing environmental stresses. This study explores the potential of Adapt-N, a dynamic biogeochemical model, to more precisely manage N inputs compared to a static N management approach, the Maximum Return to N (MRTN). Data from 16 multiple N rate trials conducted over two years (2013–2014) in three Midwest states were used to reconstruct two yield response functions: quadratic (QD) and linear-plateau (LP), allowing estimation of the Economic Optimal N Rate (EONR), and yields resulting from Adapt-N and MRTN recommendations. Model-based N rates were better correlated with the EONR based on the LP function, and were similar based on the QD function. Applying a dynamic approach to N recommendations allowed a significant reduction in applied N (averaging 28 kg ha−1; 13%) without compromising yield, thereby maintaining farmer’s profits while reducing simulated environmental N losses. Longer-term simulations showed that the largest reductions in N rates by Adapt-N compared to the MRTN occurred in dry seasons when early season N losses were small. This study shows that model-based N recommendations can have both economic and environmental benefits compared to a static N management approach.