Can low-cost NIR reflectometers predict Potential Mineralizable Nitrogen in organic farms?

View the project final report

Commodities

Practices

• Crop Production: cover crops, nutrient cycling, nutrient management
• Education and Training: on-farm/ranch research
• Production Systems: organic agriculture
• Soil Management: nutrient mineralization

Majority of growers report seeking further support in managing nutrients in their farms. Nutrient supply is challenging in organic systems because synthetic fertilizers are prohibited. Knowledge of the biotic and abiotic processes in an organic farm is key in understanding the nutrient demand in organic systems. As such, farmers usually send soil samples to soil testing centers to understand soil properties that help make informed decisions about managing Nitrogen in their farms. These traditional lab analyses are time consuming, expensive, and require the use of harsh chemicals. NIR reflectometers are cheap, rapid, relatively easy to use, and allow non-destructive and repeated soil sampling of near-infrared reflectance which is tested to predict several soil properties of interest to farmers. 

Organic farms sometimes must rely on organic sources of N such as from cover crop residues, as manure demand is very high. Potential mineralizable N is an excellent metric that allows farmers to estimate the amount of N supplied by inputs (manure, residue, etc). While NIR reflectometers are widely used to estimate total soil C and N, their use to estimate PMN is limited. The PMN is a metric for estimating N supply, mediated by microbial communities, and useful to predict soils' inherent capacity to supply N to cash crops via organic sources.  

In this project, we examined the use of a hand-held reflectometer to rapidly estimate soil PMN. Hand-held NIR spectrometers are cost-effective, offer rapid results, non-destructive, and do not involve using chemical reagents. Developing predictive models that tie spectral NIR signal with PMN could enable farmers to make rapid, site-specific N management decisions tailored to their unique farm fields. Our study employed soil samples from a cover cropped study across 20 organic farms planted with three cover crop treatments (cereal rye, crimson clover, and a 4 way-mix of cereal rye, crimson clover, rapeseed, and oats) in the Southwest and the Thumb region of Michigan. The study was designed as a randomized complete block design with four replicates, resulting in a total of 240 samples. 

Lab analysis included 7-day anaerobic incubation PMN, soil characterization at the block level, Sci-ware Neospectra handheld reflectometer (1350-2550 nm range) with six replicate scans per sample and a benchtop attenuated total reflectance- Fourier transform infrared in the MIR (2500 nm - 25000 nm range). Both NIR and MIR spectra were preprocessed using standard normalization (SNV) and Savitzky- Golay smoothing. Principal Component Analysis was used to reduce wavelength dimensions while preserving the variation in the spectra.  We used partial least squares regression (PLSR) to develop models : 1. PMN as a function of NIR, 2. PMN as a function of MIR, 3. PMN as a function of NIR and MIR combined. A leave-one-farm-out cross-validation (LOFO-CV) and 10-fold CV was employed, resulting in six models. We used coefficient of determination (R²) to access model prediction accuracy. 

We found that PMN did not vary significantly across the three treatments. Potential mineralizable N varied across all farms ranging from 9 to 108 mg kg^-1 soil. Soil NIR and MIR spectra in the principal component space showed that the spectral signature for both NIR and MIR differed across farms. Our results showed that the NIR spectra explained 21 % variation in the PMN when generalized across farms. However, within farms, NIR explained 51 % variation in PMN. Prediction of PMN using the MIR spectra produced unreliable estimates of PMN. Combining NIR and MIR spectra also did not improve model performance. Overall, our findings suggest that within farm generalizations of PMN estimates using NIR spectra results in moderate performance but across farm generalizations have limited predictive values. However, the MIR spectra do not contain spectral information helpful in predicting PMN, which is a dynamic soil metric mediated by microbial communities. Either large datasets are needed such that the models are trained on a wide range of farms to extrapolate results to newer farms, or spectral signatures should be confined to predicting static soil properties such as total N and total C, rather than predicting PMN, which is a dynamic soil metric mediated by microbial communities. 

The objectives of this study were to:

1. Evaluate the efficacy of hand-held spectrometers to estimate PMN.
2. Develop predictive models using hand-held spectrometers that provide measures of PMN.
3. Compare PMN estimates from NIR hand-held spectrometers with bench top MIR spectroscopy to develop guides tailored to farmers.

We hypothesized that: 

1. The variation in PMN across farms and cover crop treatments can be explained by the NIR spectral information from the reflectometer.
2. The MIR spectroscopy estimates are better associated with PMN lab measurements than NIR spectroscopy.

This project focused on foundational research to access the feasibility of using handheld NIR reflectometer for PMN prediction in organic farms. The participating growers contributed site and soil sample access, making this collaborative research possible. While farmer learning outcomes and adoptive actions from this study were part of the proposal, our results showed that across-farm generalization of PMN estimates using NIR is not reliable and the educational program was not pursued to avoid promoting an unreliable method for estimating PMN. Research findings will be disseminated  through a poster presentation at the Tri-Societies Meeting 2025, and via a peer-reviewed publication to inform the broader scientific community.