Nitrogen fertilizer is generally the largest input cost for producers in Montana and in much of the
western United States. Despite this fact, scientists, producers, and their advisors have not
established a consistent and well-tested system for determining areas of fields that are more or less
responsive to nitrogen fertilizer. This proposal attempts to develop such a system. We will employ
novel remote-sensing techniques to investigate subfield-scale changes in soil nitrate-nitrogen (N)
during winter months. Restricting the period of inquiry to the months between September and
April (henceforth ‘overwinter’) will eliminate or minimize impacts of confounding variables such
as crop uptake, fertilizer inputs, and denitrification, allowing for direct estimates of variables such
as leaching and mineralization. Temporally intensive overwinter sampling will facilitate
development of a model to predict overwinter N changes (ONCs), which are known to range from
+61 to -23 lb/ac in this region. Such a model will serve to minimize over- and under-fertilization
for the many farmers applying constant rates of fertilizer in spite of—or in lieu of—soil test results.
Complicating the situation further is the threat of warmer and wetter Montana winters, which could
feasibly exacerbate ONCs. This research will have implications for 1) grower profits through
enhanced N use efficiency, reduced N surpluses, minimization of suboptimal yields caused by
under-fertilization, and improved methods of variable rate fertilizer application.; 2) water quality
via minimization of N surpluses and N leaching; and 3) climate change impacts on modern farming
practices by strengthening awareness among farmers of how altered precipitation and temperature
patterns interact to affect N use efficiency, water quality, and soil health.
1. Quantify subfield-scale variability in ONCs toward improved precision ag methods such as
variable rate fertilizer application.
2. Develop a model to predict ONCs and to notify farmers when large ONCs are likely to have
occurred in order to minimize economic and environmental injuries resulting from uninformed
fertilizer management decisions.
3. Demonstrate to farmers the economic and environmental benefits of soil testing and the
implications of changing precipitation and temperature patterns for modern farming practices.
Overwinter soil nitrate-nitrogen change (ONC) is influenced by both biological and physical processes. On the Moccasin terrace, mineralization and denitrification are the main drivers of year-round, biologically-induced nitrate-nitrogen (N) changes (John et al., 2017). Thus, we expect these processes will be major contributors to ONC on this landform. Estimates of mineralization (Min) and denitrification (Den) will be obtained via ATP, acid phosphatase, and urease activity assays, as well as anaerobic incubation techniques (See ‘Soil Sampling and Processing’). Similarly, N leaching on the Moccasin terrace is known to be the main driver of year-round, non-biological soil N changes (John et al., 2017; Sigler et al., 2018), so we expect this process will contribute strongly to ONC. Leaching (Lch) will be estimated in the following way:
Eq. 1 Lch = Min – Den – ONC
where ONC is defined as soil nitrate measured in spring minus that measured in fall (kg N/ha).
If these processes are ignored and farmers calculate fertilizer rates based on fall soil test results, over- or under-fertilization is likely to occur when growing dryland cereal crops. Here, over-fertilization is defined as fertilizer applied in excess of the recommended rate, after accounting for ONC. Over- and under-fertilization occur at positive and negative values of Δ:
Eq. 2 Δ = Applied – Recommended
where Applied is applied fertilizer (kg N/ha) and Recommended is recommended fertilizer (kg N/ha), derived from the following equation:
Eq. 3 Recommended = State Guideline*Yield Target – ONC – Soil Test N
where State Guideline is the yield-normalized available N recommendation for the state of Montana (0.055 and 0.045 kg N/kg for winter wheat and spring wheat, respectively), Yield Target is the grower’s target yield (kg/ha), and Soil Test N is soil nitrate measured in fall (kg N/ha).
Modelling and Sampling Schemes
A distinction is made between ‘on-station’ and ‘off-station’ modelling efforts. At the Central Agricultural Research Center (i.e., the ‘on-station’ site), continuous Soil Moisture (0-20cm), Soil Temperature (0-20cm), and whole-profile CO2 Flux data will be collected throughout the winter months. These variables will accompany depth to the gravel layer (DTG) and Sampling Event as explanatory variables in a generalized linear model (Model 1) to predict mineralization and denitrification with larger sample sizes and at a higher frequencies (5 events/yr) than possible at off-station sites. At off-station sites, sampling will occur once in fall and once in spring for each year of the two-year study. Off-station sites will be located along a depth to shale (DTS) gradient, which is a proxy for groundwater storage capacity (Sigler et al., 2018). Three off-station sites will be cultivated to winter wheat and two to spring wheat in the 2019 crop year. The on-station site will also be cultivated to spring wheat in 2019, and a subset of soil samples from this site (i.e. the first and last from each year) will be included in off-station modelling efforts. DTS and DTG will be treated as either continuous or categorical variables in Models 1-3. A categorical approach will allow factor level replication within Previous Crop (2 levels: winter wheat and spring wheat), DTS (3 levels: Shallow, Moderate, and Deep), and DTG (2 levels: Shallow and Deep). These factors will be included as explanatory variables in a generalized linear model (Model 2) for predicting ONC, mineralization, denitrification, and leaching (henceforth, ‘the response variables’) at the subfield scale in at least one year of the two-year study. A third linear regression model (Model 3) will include samples from both years with DTG, DTS, and Year as explanatory variables. In Model 3, effects of Previous Crop on response variables will effectively be ignored. Previous Crop effects on response variables are known to be small relative to those of DTG (Jones et al., 2011). However, it is not known whether effects of Previous Crop are small relative to those of DTS. Therefore, justification for exclusion of Previous Crop from Model 3 is currently lacking. Results of Model 2 will provide evidence for or against this justification.
Multiple ancillary and reference datasets will be used in off-station site selection and in classification of on- and off-station subfields, including CropScape (i.e., for information regarding crop rotations), a shale surface digital elevation model of the landform (Sigler et al., 2018), meter and sub-meter imagery from multiple years (National Agricultural Imagery Program), as well as 5-cm multi-band imagery obtained via an unmanned aerial vehicle. Supervised classification will be used to predict DTG at all subfields, where the 12 randomized DTG sampling points will serve as reference data in training and test area selection for subsequent cross-validation accuracy assessments.
In the process of off-station site selection, priority will be given to fields within the boundary of the Moccasin Terrace (Sigler et al., 2018) for which winter wheat or spring wheat is planned during the 2019 crop year. Priority will also be given to those fields for which high spectral variability in addition to uniform, cereal crop stands are observed in 2017 and/or 2015 NAIP imagery. Furthermore, selection will occur such that distance between fields is minimized (ca. 15 km) while DTS range is maximized (ca. 1-20 m). This approach will serve the dual purpose of controlling for potential effects of weather differences among sites and increasing the likelihood of capturing true differences in ONC across DTS environments. Within each 20-80 ha field, a 0.4-ha subfield will be targeted based on crop stand uniformity, spectral variability, and degree of subfield-to-field spectral representation at time of image acquisition, as determined by single-band histogram comparisons. DTG in each subfield will be randomly sampled at 12 locations. A Giddings hydraulic soil probe retrofitted with a pressure gauge will allow for soil depth observation accuracy assessments using different instruments and different pressure thresholds. The depths at which a 3-cm soil corer and a modified Paul Brown probe achieve downward pressures of 1000 and 500 psi, respectively, will be averaged to obtain the DTG approximation. The 12 observation points will serve as reference data for training and test area selection prior to supervised classification of NAIP imagery. Two DTG classes (i.e., shallow and deep) will be assigned three test areas and three training areas, no less than 36 pixels each. This ensures both training and test areas will encompass a cumulative 108 pixels, satisfying the 100 pixel guideline put forth by Campbell and Wynne (2011). Given the strong correlation between DTG and ONC in this area, supervised classification techniques could feasibly be used to predict ONC at the subfield scale. Such techniques may broaden the currently narrow profit margins associated with variable rate (VR) technology, especially if implemented in combination with split-application and/or slow-release technologies. In future work, subfields may serve as potential locations of small-plot trials aimed at improving N use efficiency, including fertilizer source/timing/quantity studies and even cropping system studies.
In summer 2017, 5-cm resolution, 4-band imagery (i.e., red, red edge, green, and near-infrared) of a 10-ha field of spring wheat at the Central Ag Research Center was obtained via an unmanned aerial vehicle. The field was planted back to spring wheat in 2018. We intended to re-image the spring wheat crop to obtain duplicate imagery in relatively wet conditions compared to the very dry conditions of 2017. These datasets would have allowed for non-destructive characterization of soils using binary image-change detection techniques outlined in Zipper et al. (2015). However, a drone malfunction in 2018 led to the exploration of alternative classification methods. Past work has shown that Normalized Difference Vegetation Index (NDVI) of spring wheat is a strong predictor of DTG on the Moccasin Terrace when image acquisition occurs at 50% crop senescence (Sigler et al., 2018). However, timing of 50% crop senescence can be difficult to assess and image acquisition at this exact growth stage is challenging. Thus, the viability of alternative classification techniques for predicting DTG on this landform will be assessed using imagery acquired post-50% crop senescence, as well as post-harvest, by the National Agricultural Imagery Program (NAIP). DTG will be randomly sampled at 42 points on a 0.4-ha subfield, selected for its high spectral variability, uniform crop stand at the time of image acquisition, distance from historical management boundaries, and accessibility.
In order to find the optimal spatial resolution for predicting DTG on the Moccasin Terrace, we will use cross-validation techniques with observed DTG values and predictions from classifiers discussed above at increasingly coarse image resolutions. These results will inform whether DTG prediction is feasible at resolutions >3 m, offering insight into compatibility with precision ag technologies such as VR fertilizer application, the effective resolution of which is currently determined by equipment specifications. Moreover, this assessment will heighten confidence in the methods of image classification implemented off-station.
On-station soil sampling will occur in five field visits from October, 2018 to 2019 spring thaw, and 5 visits from October, 2019 to 2020 spring thaw. Only the first and last visits of each on-station winter sampling effort will include off-station sampling. A Giddings hydraulic soil probe will be used to collect 20-cm deep cores on- and off-station from 12 randomly assigned locations within each 0.4-ha subfield. A weighed amount of subsample will be removed from all bulk soil samples. Subsamples will be analyzed with cell viability, urease activity, and acid phosphatase assay kits at the Central Ag Research Center Soil Microbiology Lab in Moccasin, MT. Anaerobic incubations will also be performed for additional estimates of mineralizable nitrogen (Vigil et al., 2002; McDonald et al., 2013). Bulk soil samples will then be shipped to Ward Laboratories, Inc. for texturing and basic chemical analyses. Samples from the first trip will be analyzed for total nitrogen, total carbon, pH, buffer pH, soluble salts, organic matter, Olsen phosphorus, total phosphorus, cation exchange capacity, potassium, calcium, magnesium, sodium, and sulfate-sulfur, while samples from all trips will be analyzed only for nitrate-nitrogen and ammonium-nitrogen.
Results from the first year of this two-years study will be available in April, 2019.
Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press.
John AA, Jones CA, Ewing SA, Sigler WA, Bekkerman A, Miller PR (2017) Fallow replacement and alternative nitrogen management for reducing nitrate leaching in a semiarid region. Nutr Cycl Agroecosyst. Volume pages. doi: 10.1007/s10705-017-9855-9
McDonald NT, Watson CJ, Lalor STJ, Laughlin RJ, Wall DP (2013) Evaluation of soil tests for predicting nitrogen mineralization in temperate grassland soils. Soil Sci Soc Am J 78: 1051-1064
Sigler WA, Ewing SA, Payn RA, Jones CA, Weissmann GS (2013) Linking soil water balance and water age with leaching of nitrate to groundwater in an agricultural setting. AGU, Dec. 9-13 2013, San Francisco, CA; Poster H53F-1482
Sigler WA, Ewing SA, Jones CA, Payn RA, Brookshire ENJ, Klassen JK, Jackson-Smith D, Weissmann GS (2018) Connections among soil, ground, and surface water chemistries characterize nitrogen loss from an agricultural landscape in the upper Missouri River Basin. J Hydrol. 556, 247-261
Vigil MF, Eghball B, Cabrera ML, BR Jakubowski, Davis JG (2002) Accounting for seasonal nitrogen mineralization: an overview. J Soil & Water Conserv Soc. 57: 464-469
Zipper SC, Soylu ME, Booth EG, Loheide II SP (2015) Untangling the effects of shallow groundwater and soil texture as drivers of subfield-scale yield variability. Water Resour Res 10.1002/2015WR017522
Educational & Outreach Activities
Details of the project were presented to an audience of 25 agricultural professionals as part of a tour of the Central Agricultural Research Center in July, 2018. Face-to-face consultations regarding overwinter soil nitrate-N changes and implications for soil sampling practices have been held with at least 20 farmers in the central Montana region since the project’s inception. Moreover, a project description and a survey was mailed to 250 central Montana farmers to assess preexisting knowledge of the research topic.
In February 2019, a meeting will held with the research team and the six cooperating farmers who have provided land for the project. Factsheets and progress reports will be distributed, and growers will have the opportunity to provide written feedback on evaluation forms. On April 1, 2019, details of the project will be presented to an audience of 20 students and professionals on the campus of Montana State University. In June, 2019, results from the first year of the study will be presented at the Central Agricultural Research Center field day to an audience of 20 agricultural professionals and 50 farmers. In December 2019, findings will be presented to 50 farmers and 20 agricultural professionals in Lewistown, MT.