|
Reduction of Nitrate Leaching and Threats to Surface Water Under Conservation Tillage
Zuhair Masri1,*, Jeremiah Asher2, Jason R. Piwarski3
Published in Journal of the ASABE 67(3): 573-588 (doi: 10.13031/ja.15533). 2024 American Society of Agricultural and Biological Engineers.
1 Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA.
2 Institute of Water Research, Michigan State University, East Lansing, Michigan, USA.
3 Soil Drainage Research Unit, USDA ARS, Columbus, Ohio, USA.
* Correspondence: Shahba.Agri@gmail.com
Submitted for review on 12 January 2023 as manuscript number NRES 15533; approved for publication as a Research Article and as part of the “Advances in Drainage: Selected Works from the 11th International Drainage Symposium” Collection by Community Editor Dr. Zhiming Qi of the Natural Resources & Environmental Systems Community of ASABE on 19 February 2024.
Mention of company or trade names is for description only and does not imply endorsement by the USDA. The USDA is an equal opportunity provider and employer.
Citation: Masri, Z., Asher, J., & Piwarski, J. R. (2024). Reduction of nitrate leaching and threats to surface water under conservation tillage. J. ASABE, 67(3), 573-588. https://doi.org/10.13031/ja.15533
Highlights
Abstract. An edge-of-field subsurface drainage discharge system removes excess subsurface water from agricultural fields to improve the conditions of the soil system making them favorable for plant growth. Conversely, edge-of-fields can increase the nitrate (NO3-) offsite movement to surface water bodies, which ultimately impacts water quality. In St. Johns, MI. Two on-farm large areas were initiated in 2013 for conservation (NT) and conventional till (CT) practices, of which two microscale fields, an area of 4 acres for each, were delineated in 2019 for NT and CT practices under a corn/soybean crop rotation. NT and CT fields were monitored and evaluated through full water years (WYs) 2019-2022. The objective was to use an innovative approach in coupling edge-of-field with soil health assessments to learn how improved soil health functions are influencing nitrate leaching and loads. NT practice enhanced SOM, other physical properties, and soil health parameters, promoting soil health functions compared to CT practice under conditions of adequate precipitation and favorable temperatures. Additionally, it improved the soil's ability to retain and resist nitrate loads in the 2020 water year, with reductions of 9.6 and 18.3 kg/ha, respectively. In 2021, WY nitrate loads reported nearly an equal value in NT vs. CT (4.8 and 4.4 kg/ha, respectively). Factors of uneven weather conditions and drought, variable agronomic practices, and residual nitrogen after corn, rendered soil conditions unfavorable to retain nitrate and induced adverse effects by increasing the nitrate leaching and loads in NT vs CT in 2022 WY (8.1 and 5.8 kg/h, respectively). Therefore, the limited evaluation period (2019-2022 WYs) presented significant challenges and revealed no affirmative evidence that the improved soil health functions under NT reduced the nitrate leaching loads in all WYs of this research. Further research is needed to prove the impact of NT on improving soil health functions to sustainably reduce nitrate leaching and loads in edge-of-fields.
The tile subsurface drainage discharge system removes excess sub-surface water from agricultural fields to improve the conditions of the soil system, making them favorable for plant growth. However, subsurface drainage discharge can increase the nutrient load to surface water bodies and degrade water quality. Nitrate (NO3-N) constitutes the primary form of nitrogen (N) loss in agricultural settings. It has long been recognized as an important contributor to hypertrophic, or eutrophic, conditions due to its mobility, water solubility, and persistence in the presence of oxygen (Daryanto et al., 2017). Nitrate in surface water has also been recognized as a widespread water pollutant and has proven to have a negative impact on the freshwater ecosystem by increasing the nitrate load (Bijay-Singh and Craswell, 2021).
At levels exceeding the permissible limits, nitrate-N makes the groundwater unfit for drinking purposes (Ledoux et al., 2007). In surface waters limited by N, phytoplankton productivity is stimulated by nitrate-N, resulting in eutrophication. This leads to widespread hypoxia and anoxia, loss of biodiversity, and harmful algal blooms that damage fisheries and pristine marine environments such as heritage coral reefs (Bartley et al., 2003).
Lack of active data on field nitrogen credit and application present challenges and exacerbate the problem of nitrogen loss and leaching. In Michigan, growers lacking active data on available nitrogen in their fields tend to over-apply fertilizer. This is done to develop yield to near full potential, a less costly approach than growing a crop that lacks nitrogen (Masri, 2017a). This process is driven by risk aversion and economic concerns, promoted by the low cost of N fertilizers and a growers’ incentive for above-average yields (Masri, 2017b). Over-application of nitrogen fertilizer to maize requirements is considered cheap insurance against excess rainfall, which may move N below the root zone (Martin, 1992). Nitrate-N can readily leach down beyond the root zone in agricultural soils and reach the ground and surface waters (Bijay-Singh and Craswell, 2021).
Leaching nitrogen from agriculture poses an environmental problem through the pollution of groundwater and surface water and the loss of nitrogen, a valuable resource from the production system (Wallman and Delin, 2021). Increased nitrate concentrations in groundwater and surface waters represent one of the most widespread and acute impacts of modern agriculture on the environment (Cardiffet et al., 2022). This leaching is affected by natural processes and management depending on various factors: soil texture, weather, crop, tillage, use of cover crops, and the source, rate, and timing of N inputs (Bergström and Johansson, 1991).
Therefore, several researchers have expressed the need for better tailoring of N fertilization management to meet maize requirements in conventional systems (Cardiff et al., 2022). The need for nutrient management strategies that reduce fertilizer inputs emerged as a cost reduction strategy (Stuart et al., 2014; Xie, 2014).
The aim of conventional agriculture is to produce the highest possible yield of crops. However, the intensive chemical inputs coupled with the necessary machinery increase compaction, erosion, and soil salinization while decreasing the content of organic matter and soil nutrients. Such practices compromise biodiversity, soil fertility, and soil health (Cárceles et al., 2022). The negative effects of agricultural activities such as conventional tillage (CT) on surface and groundwater quality have been a topic of concern in many parts of the world for decades (FAO, 2015). Zhao et al. (2001), quantified the effect of different tillage fields with corn in conventional agricultural systems. Zhao found that subsurface tile drainage was the major pathway for NO3--N transport. To reduce these negative impacts of agricultural systems and guarantee their long-term sustainability, management systems that improve or conserve soil quality are crucial (Doran and Zeiss, 2000). To this end, agronomic practices of conservation agriculture (CA) are promoted as a foundational basis for improved management of N cycling in agro-ecosystems (Dumanski et al., 2006).
Much work has been invested in the development of practices and technologies that reduce nitrate losses from agricultural drainage in the US Midwest (Christianson et al., 2013). The no-till practice was described for improving yields and reducing erosion (Xie, 2014), thereby increasing profitability, including spring fertilizer application (Christianson et al., 2014). And according to Tiessen et al., (2010), conservation tillage reduced the export of sediment in runoff water by 65%. Similarly, concentrations and export of nitrogen were reduced by 41% and 68%, respectively, relative to conventional tillage.
Individual farmers that adopt environmental management practices designed to mitigate or prevent issues such as NO3- losses through drainage to surface waters are motivationally different from production innovations, e.g., large industrial farms, because the short-term economic advantages of adopting a mitigation technology are rare (Battel and Krueger, 2005). It will also be capable of addressing drainage water quality concerns (Lemke and McKenna, 2005; Feyereisen et al., 2022).
Conservation tillage has also been a locally adapted practice that implies a series of economic, agronomic, and environmental benefits (FAO, 2015; Cárceles et al., 2022). Therefore, conservation tillage systems designed to minimize the risk to the environment without forfeiting economic productivity are often recommended to reduce agriculture's contribution to nutrient loss (Bergtold and Sailus, 2020).
Conservation tillage (e.g., zero-till, minimum tillage, reduced tillage, etc.) is broadly defined as any tillage system with at least 30% of the residue from the previous crop remaining on the soil surface after seeding (Lal, 2003). Therefore, no-till (NT) and cover crops are often cited as examples of conservation practices that improve soil health and are used in production agriculture systems (Schmidt et al., 2018; Wood and Bowman, 2021; Evenson et al., 2022; Waring et al., 2022). As cited by researchers, conservation tillage reduces soil erosion, conserves soil moisture, conserves energy, and increases SOM. Consequently, improving soil quality (Fageria and Moreira, 2011) and ensuring a sustainable system for soil health (Rodríguez et al., 202). Weersink et al. (1992) and Archer and Reicosky (2009) found no-till to be the dominant practice in a wide range of situations and assumptions. Their results were based on long-term experimental plots that examined the relative profitability of alternative tillage practices under variable weather patterns. In the same context, under favorable conditions of warm and dry weather early in the growing season, Ogle et al. (2012) reported that no-till can achieve profits comparable to conventional tillage methods. Hodde et al. (2019) highlighted the economics of conservation in relation to the predicted effect of projected climate change (Shokrana, 2022), as a practice to be more attractive for corn in rotation with soybean, in part because the yield penalty to adopt conservation tillage under these crop scenarios is small, and because less intensive tillage implies cost savings.
However, other researchers (Daryanto et al., 2017) found that the leachate of nitrate load was greater under NT than under CT, but in most cases, nitrate load was determined by changes in water flux. And the nitrate leachate concentration was found to be similar under both tillage practices. Other studies (Quincke et al., 2007) have reported that dissolved nutrient transport including nitrate can be greater in surface runoff or groundwater from conservation, reduced till, or no-till systems than from conventional tillage systems due to the stratification of nutrients in the soil surface and the release of nutrients from plant residue that remains on the soil surface after harvest (Hou et al., 2012).
According to Randall and Goss (2008), who evaluated the effect of NT on nitrate concentration vs. the volumetric water flow, they found that the net effect of NT load can be highly variable and dependent on the transport pathway considered. While other studies have documented the positive effects of NT in reducing nitrate concentration in groundwater (Rekha et al., 2011), these researchers identified such physical variables as rainfall intensity, the amount of water leaving the system, soil texture, and other management factors. These include crop species, fertilizer type, tillage practices, frequency, and tillage interactions with soil texture, crop type, and NT duration that ultimately affected the nitrate mobility and export from agricultural fields. Other studies have reported no effect (Follett and Hatfield, 2001), reduction (Syswerda et al., 2012), or increase (Bakhsh et al., 2002) of nitrate load with NT adoption.
However, according to Masri et al. (2015), the long-term duration of conservation tillage can further affect SOM's gradual accrual and improvement of soil structural stability, soil wetting-drying cycle, soil microbial activity, and aeration. This enhances the soil's retention capacity and N cycling. Within the same context, Quincke et al. (2007) suggested conducting an occasional soil harrowing, once every 10 or more years, which may help overcome some of the soil compaction and nutrient stratification problems that are often associated with long-term application of NT, without causing significant loss of SOM and deterioration of soil structure.
This study used an innovative approach by coupling of the edge-of-field monitoring with the soil health system to learn how improved soil health functions under agricultural conservation practices (NT) are influencing microscale fields, which sustainably impacts water quality and offsite movement of nitrate, thus minimizing the threat to the environment.
Materials and Methods
Research Site
The research site was located in St. Johns, MI., in two adjacent fields with subsurface drainage added in 2011. One field was converted from conventional tillage to a no-till system (NT) in 2013, and the other field remained under a conventional tillage system (CT) for the same duration of time. To reduce outside influence, the existing tile drains were broken to separate each of the 4-acre fields from fields to the north and from each other. In 2014, the two fields were planted with soybeans, followed by corn in 2015, creating a soybean-corn rotation under the CT and NT cropping systems. New outlet mains were installed for the two CT and NT fields, along with AgriDrain drainage control structures located at the end of each tile main (figs. 1 and 3).
To reduce outside influence, the existing tile drains were broken to separate each of the 4-acre fields from fields to the north and from each other.
Soils
According to the USDA Soil Survey (SSURGO), the dominant soil series is Sebewa (Sb) overlapped with a small patch of Parkhill (Pr) soil series at the northwest of the CT field (fig. 1). The Sebewa soil series is classified under the Mollisols soil order and is characterized by a thick, dark surface horizon known as a mollic epipedon, resulting from the long-term addition of organic materials derived from plant roots.
Following a standard soil profile description (table 1), soil horizons showed the characteristics of hydric soils (figs. 2A and B) with horizon depths and textures, matrix, and redoximorphic colors.
This soil is characterized by depleted dark surface (F7) to a depth of more than 30 cm and depleted below dark surface (A11) indicators (USDA-NRCS, 2018), and thus the soil is classified as hydric soil.
 |
| Figure 1. Two research fields compromised of conventional till (CT) and conservation no-till (NT) areas. The two fields spread over dominant Sebewa (Sb) soil series. Locations of complete subsurface monitoring equipment are shown in each CT and NT field in St. Johns, Michigan. Points of Six grid-composited soil samples are shown in each CT and NT field. |
The depleted dark surface of hydric soils, in the past, was subject to saturation or inundation and prolonged anaerobic conditions. Such conditions promoted certain biogeochemical processes, the accumulation of SOM, and the reduction, translocation, or accumulation of iron and other reducible elements.
Usually, during the development of the above indicators, soils in the interiors of wetlands are not always examined. Therefore, there are wetlands that lack any of the approved hydric soil indicators in the wettest interior portions (Vasilas et al., 2018).
| Table 1. Hydric soil profile description with horizon depths and textures, matrix, and redoximorphic colors. | ||||||||||
| Depth (cm) | Matrix | Redox Features | Texture | Remarks | Other Remarks | |||||
| Color (moist) | % | Color (moist) | % | Type[a] | Loc[b] | |||||
| 0-6[c] | 10YR 2/2 | Loamy /clay | Silty Clay loam | Manganese deficiency | ||||||
| 6-30[c] | 7.5YR 3/2 | 95 | 2.5Y 6/1 | 5 | D | PL | Loamy /clay | Silty Clay Loam | ||
| 30-60[d] | 10YR 6/2 | 7.5YR 7/1 | 95 | D | PL | Loamy /clay | Silty Loam | Poor soil | ||
| 7.5YR 6/6 | 5 | C | PL | |||||||
[a] Type: C= Concentration, D= Depletion, RM= Reduced Matrix, MS= Masked Sand Grains. [d] Locations: PL = Pore Lining, M = Matrix. [c] Indicators: 0-30 cm, Depleted Dark Surface indicator (F7). [d] Indicators 30-60 cm, Depleted Below Dark Surface indicator (A11). | ||||||||||
These processes of saturation or inundation and prolonged anaerobic conditions result in distinctive characteristics that persist in the soil and are impacted by the wetting-drying cycle (shrinking and swelling). The soil's dark surface overlayed abruptly a grayish depleted below dark surface soil at 30 cm with an estimated redox of 5% (table 1; figs. 2A and 2B) and representing the zone of illuviation.
 |  |
| Figure 2. (A) depleted dark surface layer of more than 30 cm, followed by (B) depleted below dark surface below 30 cm. | |
However, the upper soil layer is rich with the SOM (4%-7%) of a fine loam to silt loam texture, and the estimated soil water holding capacity ranges between 0.31-0.35 w/w (g H2O/ g dry soil) for disturbed soils passed through a 2 mm sieve.
Soil Sampling and Soil Chemical Physical Analysis
A comprehensive soil sampling was conducted on 25 May 2022. Six grid-composited soil samples, each of 5-subsamples (fig. 1), were taken at 0-15 cm (6”), representing the upper depleted dark surface of hydric soils (fig. 2) in each CT and NT field. Sampled soils were mixed, transferred to the lab, air-dried, and divided into two batches for different analyses. The first batch was disaggregated and passed through a 2.0 mm sieve for assessment of soil water holding capacity and wet aggregate stability. The second batch was mailed to AgSource Lab (https://vas.com/soil-testing/) for measurements of soil nutrients and a complete package of soil health parameters.
Soil Physical and Chemical Analysis
Soil organic matter (%) was analyzed by loss of ignition, and soil-nitrate was analyzed by colorimetric and cadmium reduction methods using routine soil testing parameters analyzed in AgSource Lab by the common reference by Brown, 2015. For the disaggregated batch, the soil wet micro-aggregate stability was analyzed by Kemper and Rosenau (1986) and Masri and Ryan, 2005; water holding capacity, w/w (g H2O/ g dry soil), by the gravimetric method according to Pikul (2003), and soil texture by the hydrometer method (Bouyoucos, 1962).
Identification of Soil Health Parameters
At AgSource Lab, a complete package of soil health parameters was analyzed. AgSource uses methods of soil health assays based on those developed by Haney et al. (2018), Soil Health Tool (SHT) version 4.7. (Bradley et al., 2021), but with some adaptations that meet the specific needs of the AgSource lab, as follows:
- Soil Health Score (SHS): SHS = ((CO2 Resp./C: N) + (water extractable organic carbon (WEOC)/100) + (water extractable organic nitrogen (WEON/10)).
- CO2 Respiration (CO2-R): Re-hydrated (not saturated) dried and ground sample incubated for 24 hours, and the CO2 evolved is read with a gas analyzer (CO2 analyzer “LiCor 840A IRGA”) as referenced in the Soil Health Tool 4.7.
- C: N Ratio: WEOC/WEON.
- Water Soluble Analysis of Carbon: 1:10 soil/water, by combustion analyzer.
- Water Soluble Analysis of Total Nitrogen: 1:10 soil/water, by combustion analyzer.
- Water Soluble Analysis of Nitrate-N: 1:10 Soil/Water Colorimetric, Cadmium reduction.
- Mineralizable N is based on stoichiometric relationships between carbon mineralization and N release, calculated from the CO2 respiration rate, WEOC, and WEON. Further support for the mineralizable N calculation is provided in Franzluebbers (2020).
Drainage Control Structure, Automated Water Sampling, and Analysis of Nitrate:
New outlet mains were installed for the two fields of interest, along with AgriDrain drainage control structures located at the end of each tile main (fig. 1). Additionally, each field has a subsurface drainage system for monitoring the subsurface discharge and water quality parameters, including total phosphorus (TP), soluble reactive phosphorus (SRP), and nitrate-nitrogen (NO3-N). Nitrate load and subsurface drainage discharge were evaluated during three full WYs, from 2020 to 2022, under a corn/soybean crop rotation. The WY starts 1 October in the prior calendar year (e.g., 1 October 2019 for the start of water year 2020) and ends on 30 September.
Sampling equipment was installed at each field (CT and NT), which included drainage control structures, automated samplers (ISCO), flow meters, soil moisture probes, housing, power supply, water depth sensors, and tipping bucket rain gauges (fig. 3).
 |
| Figure 3. (A) Complete subsurface monitoring equipment, and (B) ISCO automatic water sampler in weatherproof enclosure. |
To sample subsurface flow, automatic sampler tubing was routed through the drainage control structures, and samples were drawn by an ISCO autosampler at regular intervals. Pressure transducers were used to measure water depth on the upstream and downstream sides of a V-Notched weir within the drainage control structure. This provided real-time estimates of flow and allowed the project technicians to document when backflow was occurring from the drainage ditch during high-water or flooding events. Manual depth measurements were taken using a measuring stick and water-finding paste during each visit to the site to confirm stage height readings from the pressure transducer.ISCO automatic samplers were installed near subsurface drainage control structure locations for both NT and CT sites to sample subsurface flow (fig. 3A). The automatic samplers were stored in an insulated, weatherproof enclosure. A propane heater was added to the enclosure to keep water samples from freezing and allow for year-round data collection. A 12-volt battery and solar panel for recharging the battery were used to power the automatic sampler.
Using a time-interval paced autosampler program, 100 ml of composite sub-surface water samples were collected every three hours to fill up to 800 ml in a bottle using the ISCO ProPak bag from the drainage structure. These samples were collected weekly by the project field technicians.
Water samples were immediately transported from the field site to the MSU laboratory. Samples were then analyzed for nitrate using the NECi nitrate reductase method (EPA, 2014). To stabilize some of the water samples for nitrate analysis, the sample bottle contained a small amount of sulfuric acid to extend the nitrate stabilization time, before analysis.
Statistical Analysis
General statistical analysis of variance (ANOVA) for soil health parameters, water-soluble forms of nitrogen, carbon, and other soil chemical and physical properties (tables 5 and 6) was run by GenStat software. ANOVA analysis of GenStat was based on treatment (NT and CT), replicate (6 reps), and variate (parameters) structure design. F-Probability (F-Pro), L.S.D. at 5%, and STDEV ANOVA options were selected.
The Cumulative Mineralization/Nitrification Using Resin Membrane Strips
Ion exchange resins were used to measure the relative amounts of plant-available nutrient ions in soils and the rates at which they are released from SOM. We have used positively charged ions (cations) exchangers to capture negatively charged ions (anions), such as nitrate NO3-N, as a measure for assessing soil nitrogen availability and its spatial distribution. We have assessed this method thoroughly to compare in situ N mineralization/nitrification rates and N availability between CT and NT fields.
The Michigan State University (MSU) Research Protocol (Jasrotia and McSwiney, 2009) followed using 0.5 M HCl (hydrochloric acid) and 0.5 M NaHCO3 (sodium bicarbonate) to regenerate ion strips and remove existing nutrient ions prior to next use. Before the burial of the resin membrane strips in soil, they were rinsed with deionized H2O for further use. Each set of resins was buried in the rhizosphere at 8-10 cm at the same time and was retrieved sequentially, i.e., one after one on weekly basis; thus, the first resin remained in the soil for one week, the second for 2-weeks, the third for three weeks, before their retrieval. After retrieval, resin strips were washed thoroughly with deionized H2O, then shaken with 2.0 M KCl for one hour at 40 rpm. After shaking, the extract was filtered using a 0.45-micron Millipore filter. Extracts, after filtration, were poured into 30 ml labeled scintillation vials and stored in the refrigerator for nitrate analysis.
Application of Nitrogen to Corn and Soybean
Nitrogen fertilizer has been applied in a dominant soybean-corn cropping system that started in 2014 with soybean. Corn requires significantly more nitrogen than soybean. A rate of 206 kg/ha of N was applied to corn and a rate of 2 kg/ha to soybean for both CT and NT fields. Soybean, a legume crop, forms a symbiotic relationship with rhizobia bacteria residing in root nodules that provide fixed nitrogen to host plants through symbiotic nitrogen fixation. Consequently, unlike other crops, soybeans typically do not require externally supplied nitrogen, a practice common in Michigan. Knowing that legume nitrogen credit is largely due to the reduced immobilization of soil nitrogen and not always due to increased nitrate nitrogen supplied or fixed by the legume plant. In fact, soybean production results in a net removal of nitrogen from the soil system. Therefore, we have discussed the impact of residual nitrogen on nitrate loads in WYs for corn and soybean.
Results and Discussion
Seasonal Weather Conditions
The seasonal weather conditions during crop growth May-October from 2020 through 2022 encountered variable and undesirable climatic conditions resulting in overly wet or dry planting conditions and abnormally hot and cold temperatures (figs. 4, 5, and 6). While ample temperature and precipitation dominated the growing season of soybean in 2020 (fig. 4), uneven averages of daily temperature and precipitation distribution affected the growing season in 2021 (fig. 5). The maximum daily temperature rarely exceeded 30 degrees (°C) from July through August 2021, with excessive daily variability. The minimum daily temperature reported was ±7 of 15 degrees (°C). The corn crop experienced precipitation scarcity in May, June, and July 2021, except for two days of extreme and erosive precipitation events of 45.5 and 75.2 mm on 25 and 26 June 2021. Such erratic weather conditions created adverse conditions for corn growth (fig. 5).
The soybean of the 2022 season encountered the lowest precipitation (301 mm) from May through October, compared to 418 mm and 504 mm in 2020 with soybean and 2021 with corn seasons, respectively. Also, erratic temperatures were observed during the 2022 soybean season (fig. 6).
 |
| Figure 4. Maximum and minimum daily temperature (°C) and precipitation (mm) during the growing season of soybean in 2020. |
Precipitation, Drainage Discharge, and Nitrate Load for WYs 2020 to 2022
Table 2 summarizes the monthly cumulative N load in the CT and NT fields over the WYs 2020 through 2022 with a corn/soybean crop rotation.
A sufficient cumulative precipitation distribution (843 mm) characterized WY 2020. A substantially higher nitrate load (18.3 kg/ha) was reported in the CT (fig. 3) vs. a lower nitrate load (9.6 kg/ha) in the NT field. With the same trend, a higher monthly cumulative drainage discharge was reported in CT (596 mm) vs NT (403 mm) fields.
Detailed nitrate (NO3-N) load (kg/ha) amounts vs. the drainage discharge and precipitation (mm) throughout soybean rotation in the WY 2020 in the CT and NT fields are shown in figures 7 and 8.
 |
| Figure 5. Maximum and minimum daily temperature (°C) and precipitation (mm) during the growing season of corn, 2021. |
 |
| Figure 6. Maximum and minimum daily temperature (°C) and precipitation (mm) during the growing season of soybean, 2022. |
For WY 2021, nearly equal amounts of nitrate loads were measured in the CT (5.4 kg/ha) and NT (5.8 kg/ha) fields, respectively, with cumulative precipitation of 663 mm (table 2). However, higher monthly cumulative drainage (40 mm) was reported in the CT field compared to (30 mm) in the NT field (table 2). Detailed precipitation, nitrate loads, and drainage discharge are presented (figs. 9 and 10).
| Table 2. Monthly cumulative drainage discharge (mm), N load (kg/ha), and precipitation (mm) in the corn (2020) and soybean cropping system for both conventional till (CT) and conservation no-till (NT) fields. | |||||||||
| WY 2020 | WY 2021 | WY 2022 | |||||||
| Soybean | Corn | Soybean | |||||||
| CT | NT | CT | NT | CT | NT | ||||
| Cumulative Precipitation (mm) | 843 | 663 | 559 | ||||||
| Nitrate (NO3-N) Monthly Cumulative Load (kg/ha) | 18.3 | 9.6 | 5.4 | 5.8 | 5.8 | 8.1 | |||
| Monthly Cumulative Drainage discharge (mm) | 596 | 403 | 40 | 30 | 150 | 183 | |||
 |
| Figure 7. The quantities of nitrate (NO3-N) load (kg/ha) amounts vs. drainage discharge and precipitation (mm) throughout soybean rotation in the WY 2020 in the CT field at MSU research site, St. Johns, MI. |
 |
| Figure 8. The quantities of nitrate (NO3-N) load amounts (kg/ha) vs. drainage discharge and precipitation (mm) throughout the soybean rotation in WY 2020 in the NT field at MSU research site, St. Johns, MI. |
 |
| Figure 9. The quantities of nitrate (NO3-N) load amounts (kg/ha) vs. drainage discharge and precipitation (mm) throughout the corn rotation in WY 2021 in the CT field at MSU research site, St. Johns, MI. |
In WY 2022 planted with soybean, the monthly cumulative nitrate load was reported to be lower (5.8 kg/ha) in the CT and (8.1 kg/ha) than in the NT fields, respectively, associated with lower cumulative drainage discharge in the CT (150 mm) vs (183 mm) in the NY fields (table 2). With soybean, the results of 2022 are opposite to WY 2020.
Detailed graphs of nitrate load, precipitation, and drainage discharge for WY 2022 are presented (figs. 11 and 12).
Such inconsistency of nitrate loads, both physically and spatially, may relate to the potential of lateral seepage and surface runoff in the research fields. However, the adverse weather conditions and varying soil edaphic properties created conditions conducive to nitrate leaching, loss, and loads. According to Osterholz et al. (2023), there were trade-offs between soil health and edge-of-field water quality. And based on their results, there is little evidence to determine that fields with greater soil health were associated with better water quality impacts. Based on our field observations and analysis interpretation, these properties include soil fertility and structure, soil type, soil tilth systems, and different agronomic practices in CT vs. NT’s fields, which had strong and deterministic effects on soil matrix and other soil properties and impacted nitrate loads, as follows:
 |
| Figure 10. The quantities of nitrate (NO3-N) load amounts (kg/ha) vs. drainage discharge and precipitation (mm) throughout the corn rotation in WY 2021 in the CT field at MSU research site, St. Johns, MI. |
 |
| Figure 11. The quantities of nitrate (NO3-N) load amounts (kg/ha) vs. drainage discharge and precipitation (mm) throughout the soybean rotation in WY 2022 in the CT field at MSU research site, St. Johns, MI. |
 |
| Figure 12. The quantities of nitrate (NO3-N) load amounts (kg/ha) vs drainage discharge and precipitation (mm) throughout the soybean rotation in WY 2022 in the NT field at MSU research site, St. Johns, MI. |
Hydric Soil Edaphic Properties and Tillage
The response of hydric soil to tillage varied in the CT vs. NT fields. With no tillage since 2013, the dark surface layer in the NT abruptly overlayed the lower subsurface layer, generating distinctive characteristics that persisted in the soil during wetting-drying cycles. The drying period, applied external forces on soil cohesion in the NT field, making the soil susceptible to deformation, compaction, macropore formation, and reduced water infiltration. Different tillage practices are performed annually in the CT field when disc-rippers are used each fall. Followed by vertical tillage for flattening corn stalks and incorporating dry spring fertilizer. A soil finisher was used each spring immediately preceding planting. The use of a disc-ripper in the CT reaches 45 cm in depth, mixing the dark surface layer with subsurface layers, loosening the soil, and improving its aeration. Thus, creating conditions favorable for seed bed emergence and shoot growth, as well as the utilization of the incorporated dry fertilizers, and liquid nutrients applied in subsurface furrows.
Nutrient Deficiency and Crop Resilience
The NT field exhibited notable growth reduction and yellowing of the leaves, exacerbated by the excessively dry conditions mid-summer, specifically during the growing season of soybean in 2022 (301 mm in fig. 5), which altered edaphic Hydric soil properties. In an anaerobic environment, soil microbes reduce iron from insoluble ferric (Fe3+) to the soluble ferrous (Fe2+) form and insoluble manganese from the manganic (Mn4+) to the soluble manganous (Mn2+) form. Soluble ferrous iron and manganous easily enter the soil solution, thus becoming suspectable to leach by mass flow, move, and transport to other areas of the soil. When soil reverts to an aerobic state, iron and manganous in the solution will oxidize and become concentrated in patches, along root channels, and in other pores (US Army Corps of Engineers, 2012). It was determined that there was a micronutrient deficiency, mainly manganese, in the NT (0.78 ppm), as compared to the CT (1.53 ppm), while the soil analysis showed higher iron concentrations (92 ppm) in the NT and (55 ppm) in the CT. The manganese micronutrient deficiency in the NT field, leached under the soil anaerobic conditions, partly driven by non-equivalency of the NT and CT surface applications (broadcast) of dry fertilizers, was a debatable factor for crop growth and yield stability and resilience (tables 1, 2, and 3; figs. 2A and 2B). A process made conditions difficult to equally compare yields and improvements in crop resilience over time, so no yield benefit was derived from the NT field over the CT field (table 3 and fig. 13). Figure 13 shows the strips of corn yield for WY 2021 in the CT and NT fields, where yield decreases in the NT matched the yellow spots with manganese deficiency as observed by us.
| Table 3. Yield data for CT and NT fields through various years in kg/ha. | |||||
| Year | Crop | CT (kg/ ha) | NT (kg/ ha) | Yield Differences | % of Yield Differences |
| 2019 | Corn | 9980 | 8348 | 1632 | 20 |
| 2020 | Soybean | 4775 | 3564 | 1211 | 34 |
| 2021 | Corn | 15880 | 13934 | 1946 | 14 |
| 2022 | Soybean | 3699 | 2354 | 1345 | 57 |
The high rate of nitrogen (206 kg N/ ha) inputs per unit of corn farmland stimulates increased root system development and distribution and biomass composition (Urban et al., 2021). The conditions of the hot and dry growing season impacted the soil nitrogen uptake, and residual inorganic nitrogen (ammonium and nitrate) remained in the soil after corn harvest, which is water soluble and can be leached through the soil and enter groundwater or be lost through tile drainage (Drury et al. 2007). As a result of the uneven weather conditions of WY 2021, there were adverse effects on residual soil nitrogen availability, retention, and leaching after corn. Likely, the hot and dry growing season of 2021 has resulted in high residual soil nitrate-N (Boe, 2022), where corn yield was lower than average. Weather conditions during the season of 2021 (fig. 5) impacted various soil conditions in the NT field, created inconsistent corn growth and yield (table 3 and fig. 13), restricted root establishment, and increased the biotic and abiotic stresses.
From a historical perspective, cold temperatures and a continuous snowpack froze nutrients like nitrogen in place until the watershed thawed in the spring, when plants could help absorb excess nutrients (Patel et al., 2018). This process increases both gaseous (Blankinship and Hart, 2012) and solute (Fuss et al., 2016) fluxes of C and N in the winter. Along with other studies (USGCRP, 2018), climate projections indicate increasing precipitation in the winter and spring; therefore, residual nitrogen in the spring, from March through May 2022, resulted in increased nitrate loads to subsurface leaching of 8.1 kg/h in the NT vs. 5.8 kg/h in the CT (table 2; figs. 11 and 12).
Lower rates of nitrogen application, spring fertilizer application, cover crops, and crop rotation are factors to be considered when discussing nitrate leaching reduction in agricultural fields.
Although the question of nitrate temporal and spatial leaching and reduction is straightforward, the answers are not as simple to interpret. Nitrogen rate management should be the primary focus of discussion, according to Helmers and Baker (2010). Precipitation amounts, intensity, and distribution play a significant role in how water moves through the soil profile and the loss of nitrates (Sawyer et al., 2008). According to Christianson et al. (2013), under consistent N-rates, research data shows that in years with lower precipitation, a higher concentration of nitrate was observed. The water quality benefits of reduced application rates of nitrogen will be a function of the original and modified fertilizer application rates (Huggins et al., 2001).
Therefore, to investigate the nitrate load variability in CT vs. NT across the three WYs from 2020 through 2022, we ran a simple statistical calculation of the correlation coefficient (R) between the two data sets for drainage discharge vs. precipitation, nitrate load vs. precipitation, and nitrate load vs. drainage discharge. Table 4 presents the returns of the correlation coefficient (R).
 |
| (A) Strips of corn yield in 2021 in the CT field, with legend indicating yield levels. |
 |
| (B) Strips of corn yield in 2021 in the NT field, with legend indicating yield levels. |
| Figure 13. Strips of corn yield of WY 2021 with legends indicating yield levels are presented for (a) CT and (b) NT. |
Predictable ranges of a significant correlation coefficient were reported for the three WYs of the study. Significant correlations (0.86 to 0.99) were computed for nitrate load vs. drainage discharge in the CT and NT fields (table 4).
| Table 4. Returns of the correlation coefficient (R) between two data sets of drainage vs. precipitation, nitrate load vs. precipitation, and nitrate load vs. drainage discharge. | ||||
| WY | Field | Drainage Discharge vs Precipitation | Nitrate Load vs Precipitation | Nitrate Load vs Drainage Discharge |
| WY 2020 | CT | 0.54 | 0.62 | 0.92 |
| NT | 0.59 | 0.66 | 0.99 | |
| WY 2021 | CT | 0.08 | 0.04 | 0.89 |
| NT | 0.25 | -0.02 | 0.95 | |
| WY 2022 | CT | 0.22 | 0.17 | 0.86 |
| NT | 0.26 | 0.02 | 0.86 | |
On the contrary, low correlation coefficients were computed for the drainage discharge vs. precipitation (below 0.26) and for the nitrate load vs. precipitation (below 0.17) for the WY 2021 and 2022, except for WY 2020, when the computed correlation coefficients varied between 0.54 and 0.66 in the CT and NT fields, respectively (table 4). Duncan et al. (2017) and Lavaire et al. (2017) testified similar results that nitrate concentrations decrease with increases of stream discharge, or the contrary. Yet, the relationship between precipitation patterns and intensity vs. nitrate load, and precipitation vs. drainage discharge requires additional field research.
Soil Health Parameters Related to Soil Physical Properties
The soil health test of Haney et al. (2018) employs a multifaceted approach to measure various aspects of a living soil system, including the use of organic chemistry designed to mimic natural soil solutions to extract commonly measured nutrients, including N. Although we have provided statistical analysis for soil health tests in this article, the soil health parameters require rigorous regional calibration of the local environment and climate, cropping system, management practices, soil edaphic factors, and type. The expectation of statistical significance for soil health parameters is a matter of long-term adoption (Prokopy et al., 2019) and synthesizing soil health motivations and barriers. Therefore, the statistical analysis revealed no significance for all the parameters and measurements presented (tables 5 and 6), nine years after the experiment began.
Table 5 shows a complete package of soil health parameter assessments, including Soil Health Score (SHS), CO2 Respiration (CO2-R) in ppm, C:N ratios, water-soluble analysis of mineralizable N (kg/ha), total nitrogen, nitrate-N, and carbon in ppm.
Average SHS, soil CO2-R, and C:N ratios were higher in the NT field (19.1, 62.4, and 5.0 vs. 16.7, 51.7, and 4.7) than in the CT field, respectively. With the same context, all water-soluble carbon, total-N, and nitrate-N reported substantial differences in the NT (208.7, 42.2, and 12.7 ppm) vs. the CT (179.6, 37.3, and 11.9 ppm), respectively. Averages of mineralizable N were reported to be substantially higher in the NT (72.1 and 60.9 kg/ha) vs. 59.9 and 27.2 kg/ha in the CT (table 5).
The key factor for improving soil health is the soil health score (SHS), computed by combining five measurements of the soil, including microbial respiration and the availability of carbon and nitrogen, into a simple number that ranges from 0 to 50. Increasing this score indicates an improvement in soil health. Soil respiration is an indicator of microbial biomass and potential activity as it relates to nutrient cycling. The water-extractable organic C:N ratios (not the total soil C: N) when above 20:1 generally indicate no net N mineralization occurs and N is “tied up” within the microbial cell until the ratio drops below 20:1. When the C:N ratio decreases, more N is released into the soil solution, making it more available for plant uptake (Haney et al., 2012).
Therefore, water-extractable organic C:N is a more sensitive indicator and better reflects active soil pools. The WEOC used for our soil testing reflects the quality of soil organic C (Masri and Ryan, 2005; Fageria and Moreira, 2011) and the energy source feeding soil microbes (Haney et al., 2012). In other words, the N pool is highly related to the water-extractable organic C pool and will be easily broken down by soil microbes and released to the soil in inorganic N forms that are readily available to plants. As we know, the soil carbon pool is large and mostly inactive; thus, it provides little information related to soil nutrient cycling (Nakajima et al., 2016).
Soil Physical and Chemical Properties
Based on the substantial average increase in SOM, a principal indicator of soil health, in the NT field (7%) vs. the CT field (4.7%), soil water holding capacity and wet aggregate stability were determined in the lab (table 6). The average measured soil water holding capacity was also higher in the NT field than in the CT field, with measurements of 0.35 and 0.31 g H2O/g dry soil, respectively (table 5).
Following the improvement of SOM and soil water holding capacity, which play a vital role in respect to soil biological activity, overall soil water aggregate stability increased. Water-stable aggregates retained above 200 µm and 500 µm sieves reported higher values in the NT field (18.4% and 43.9%, respectively), compared to (14.4% and 35.8%, respectively) in the CT field (table 6). Between these two sizes of water-stable aggregates retained on 200 µm and 500 µm sieves, the intra-aggregate particulate organic matter (iPOM) fractions positively influence the soil aggregates to withstand the water disruptive forces and dispersion in the NT vs. the CT.
Increases in SOM and stratification in the top layer, or rhizosphere, of NT fields are consistent with the increases in soil water-stable aggregates and the turnover model proposed by Six et al. (2000), whereas the mechanical disruption under intensive tillage in the CT decreased SOC stock combined with the decreased water-stable aggregates.
| Table 5. A complete package of soil health parameters was analyzed for soil samples taken at 0-15 cm in the fields of CT and NT on 25 May 2022. | ||||||||||||||||||||||||||||||||||||||||||
| Soil Health Scores | Water Soluble | |||||||||||||||||||||||||||||||||||||||||
| Soil Samples | Soil Health Score (SHS) | Soil CO2 Respiration (ppm) | C:N (Ratio) | Mineralizable N (kg/ha) | Total N (ppm) | NO3-N (ppm) | Carbon (ppm) | |||||||||||||||||||||||||||||||||||
| Ag. practices | CT[a] | NT[b] | CT | NT | CT | NT | CT | NT | CT | NT | CT | NT | CT | NT | ||||||||||||||||||||||||||||
| Fields mean | 16.7 | 19.1 | 51.7 | 62.4 | 4.7 | 5.0 | 59.9 | 72.1 | 37.3 | 42.2 | 11.9 | 12.7 | 179.6 | 208.7 | ||||||||||||||||||||||||||||
| Statistical Analysis | ||||||||||||||||||||||||||||||||||||||||||
| F pr. [c] | 0.354 | 0.223 | 0.530 | 0.299 | 0.135 | 0.708 | 0.004 | |||||||||||||||||||||||||||||||||||
| stdev[d] | 3.51 | 13.02 | 0.72 | 17.33 | 4.26 | 2.15 | 19.51 | |||||||||||||||||||||||||||||||||||
| l.s.d. (5%)[e] | 5.95 | 19.74 | 1.27 | 27.05 | 7.04 | 4.76 | 14.69 | |||||||||||||||||||||||||||||||||||
| e.s.e. [f] | 1.64 | 5.43 | 0.35 | 4.44 | 1.94 | 1.31 | 4.04 | |||||||||||||||||||||||||||||||||||
[a] Conventional Till. [b] No-Till (NT). [c] F-Probability. [d] Standard deviation. [e] Least significant difference of means. [f] Standard error of means. | ||||||||||||||||||||||||||||||||||||||||||
Table 6. Detailed measurements of SOM, nitrate N, and phosphorus. In addition to soil textural classes, soil water holding capacity, and wet aggregate stability were sampled in the CT and NT fields on 25 May 2022. | ||||||||||||||||||||||||||||||||||||||||||
| Soil C and N, Physical Properties, and Texture | Soil Analysis | Water Holding Capacity | Water Stable Aggregates | Soil Texture | ||||||||||||||||||||||||||||||||||||||
| SOM | Nitrate-N | (WHC) | > 500 µm | > 200 µm | Clay | Silt | ||||||||||||||||||||||||||||||||||||
| (%) | (ppm) | (g H2O/g dry soil) | (%) | (%) | (%) | (%) | ||||||||||||||||||||||||||||||||||||
| Ag. practices | CT[a] | NT[b] | CT | NT | CT | NT | CT | NT | CT | NT | CT | NT | CT | NT | ||||||||||||||||||||||||||||
| Fields mean | 4.73 | 7.03 | 10.68 | 11.58 | 0.31 | 035 | 14.4 | 18.4 | 35.80 | 43.9 | 25.7 | 25.6 | 33.6 | 27.6 | ||||||||||||||||||||||||||||
| Statistical Analysis | ||||||||||||||||||||||||||||||||||||||||||
| F pr. [c] | 0.143 | 0.615 | 0.054 | 0.132 | 0.004 | 0.967 | 0.041 | |||||||||||||||||||||||||||||||||||
| stdev[d] | 2.31 | 2.65 | 0.03 | 3.56 | 4.70 | 3.65 | 5.01 | |||||||||||||||||||||||||||||||||||
| l.s.d. (5%)[e] | 3.41 | 4.31 | 0.034 | 5.77 | 4.04 | 6.95 | 5.63 | |||||||||||||||||||||||||||||||||||
| e.s.e. [f] | 0.94 | 1.19 | 0.009 | 1.59 | 1.11 | 1.91 | 1.55 | |||||||||||||||||||||||||||||||||||
[a] Conventional Till. [b] No-Till (NT). [c] F-Probability. [d] Standard deviation. [e] Least significant difference of means. [f] Standard error of means. | ||||||||||||||||||||||||||||||||||||||||||
Therefore, aggregate formation in soils is regarded as an important process in SOC stabilization (Blanco-Canqui and Lal, 2004; Masri and Ryan, 2005; Du et al., 2014), indicating that the rate of decay of both aggregate sizes (macro and micro) is highly influenced by the intensity of tillage. Thus, the value of NT farming extends beyond enhancing the stability of micro- and macro-aggregates and increasing SOC storage; it also significantly improves the stability of all aggregate fractions under prolonged wet conditions (Al-Kaisi and Licht, 2004; Al-Kaisi et al., 2014). And the transition from intensive tillage to NT enhances carbon sequestration in micro-aggregates of surface soil (Du et al., 2014).
The Cumulative Mineralization/Nitrification Rates of Nitrate Release and Availability
To support the improvement of soil health parameters in the NT field vs. the CT, a resin membrane technology was utilized to compare in situ N mineralization/nitrification rates and N availability between NT and CT in terms of dynamic supply rates of nitrate per day for 2022 WY.
Comprehensive figure 14, shows the in-situ N mineralization/nitrification rates and N availability between CT and NT fields.
Measurements of the supply rate of cumulated nitrate (NO3-N) (ppm/10 cm2/days) were evaluated during the 2022 soybean cropping season using 3 sets of resins membranes from 16 June to 7 July, from 21 July through 18 August, and from 25 August through 7 October.
In both CT and NT fields, the nitrogen mineralization process increased with soybean growth starting from 16 June and reached its maximum during August, which matches the maximum vegetative growth of soybean. Then, nitrogen mineralization decreased during September.
 |
| Figure 14. The figure shows the cumulative mineralization/nitrification rates of N availability between CT and NT fields released from SOM for 2022 WY. |
Figure 14 presents a substantial increase in nitrogen mineralization in the NT field compared with the CT field during the 2022 soybean cropping season. Except for a fleeting period from mid-July to mid-August 2022, the NT field exhibited less active nitrate cycling, influenced by the impact of the drought.
Given that the values of mineralized N are reported as supply rates per unit area, they are not expressed as concentrations because the ion resin measurements cannot be accurately converted to per gram of soil or per unit area of soil. As described above, these data are primarily used to compare in situ N mineralization/nitrification rates and N availability between fields, not to evaluate N stocks.
The increased organic matter, greater soil aggregation and porosity, and more active nutrient cycling in the NT vs CT reflects improvements of soil health functions.
However, the trend of the cumulative mineralization/nitrification rates of nitrate release and availability measured by resin membrane strips provided valuable information for the development of a holistic method to characterize nitrogen cycling and release through all WYs in the future. Knowing that N cycling includes nitrification, denitrification, and N immobilization processes.
In this research, the evaluation period was limited to 3-cropping seasons of corn (2021) and soybean (2020 and 2022). Climatic variability and agronomic management practices, such as soil nutrient disparities between CT and NT, significantly challenged the assessment of soil health benefits and yield increases. An extended research period will help to evaluate and determine an improved relationship between nitrate load, water quality parameters, and soil health functions.
Conclusions
The soil health management approach aims to improve the soil’s physical, chemical, and biological properties to increase nutrient retention and reduce their transport and leaching in edge-of-fields. Therefore, this study used an innovative approach in the coupling of the edge of field monitoring with the soil health system to learn how improved soil health functions under agricultural conservation practices (NT) are influencing microscale fields, which sustainably impacts water quality and offsite movement of nitrate, thus minimizing the threat to the environment.
Application of NT practice improved the SOM, a principal indicator of soil health, and other soil physical properties, promoted soil health functions vs. CT practice under sufficient precipitation distribution and ample temperature conditions and aided the soil's retention ability to resist nitrate leaching and loads, specifically in 2020 WY. However, factors of uneven weather conditions and drought impact, specific soil characteristics, variable agronomic practices, residual nitrogen after corn, and uneven nutrient amendments rendered soil conditions unfavorable to retain nitrate and induced adverse effects by increasing the nitrate leaching and loads in NT vs. CT, specifically in the successive soybean/ corn of WY 2022.
Obviously, variable agronomic practices in CT vs. NT, the soil-plant biotic nutrient non-equivalency with abiotic uneven temperature, and precipitation stresses were exacerbated by residual N in WY 2022 (soybean after corn). This had adverse effects on soil nitrate retention and leaching, which increased the nitrate loads.
The utilized resin membrane strip technology during the summer of WY 2022 for assessment of in-situ N mineralization/nitrification rates and availability proved to be a valid research approach to characterize the soil nitrogen cycling and release processes in the future under variable soil conditions.
This research increased our knowledge of soil health functions and impacts on nitrate loss from agricultural settings as an important contributor to hypertrophic or eutrophic conditions.
An extended research period is recommended due to the significant challenges encountered during the limited evaluation of soil health motivations and barriers between NT and CT. This research is crucial for reducing nitrate leaching and loads in edge-of-field subsurface drainage systems and promoting NT as a key practice for sustainable agriculture.
Acknowledgments
This research was funded by the Michigan Department of Agricultural and Rural Development. We express gratitude to Michigan State University (MSU) employees, Dr. Ehsan Ghane for offering field and lab facilities, Cole Kelly, Quinton Merrill, Alaina Nunn, Sami Shokrana, and Joseph Letavis for data collection and processing. The authors also wish to acknowledge the valuable edits of the text offered by Dr. Anne E. Plovanich-Jones, a former Grant writer at MSU.
References
Al-Kaisi, M. M., Douelle, A., & Kwaw-Mensah, D. (2014). Soil microaggregate and macroaggregate decay over time and soil carbon change as influenced by different tillage systems. J. Soil Water Conserv., 69(6), 574-580. https://doi.org/10.2489/jswc.69.6.574
Al-Kaisi, M., & Licht, M. A. (2004). Effect of strip tillage on corn nitrogen uptake and residual soil nitrate accumulation compared with no-tillage and chisel plow. Agron. J., 96(4), 1164-1171. https://doi.org/10.2134/agronj2004.1164
Archer, D. W., & Reicosky, D. C. (2009). Economic performance of alternative tillage systems in the northern Corn Belt. Agron. J., 101(2), 296-304. https://doi.org/10.2134/agronj2008.0090x
Bakhsh, A., Kanwar, R. S., Bailey, T. B., Cambardella, C. A., Karlen, D. L., & Colvin, T. S. (2002). Cropping system effects on NO3-N loss with subsurface drainage water. Trans. ASAE, 45(6), 1789. https://doi.org/10.13031/2013.11430
Bartley, R., Henderson, A., Prosser, I. P., Hughes, A. O., McKergow, L. A., Lu, H.,... Roth, C. H. (2003). Patterns of erosion and sediment and nutrient transport in the Herbert River Catchment, Queensland. Consultancy Report. CSIRO Land and Water, Herbert River Catchment Group.
Battel, R. D., & Krueger, D. E. (2005). Barriers to change: Farmers’ willingness to adopt sustainable manure management practices. J. Ext., 43(4). Retrieved from http://www.joe.org/joe/2005august/a7.shtm
Bergström, L., & Johansson, R. (1991). Leaching of nitrate from monolith lysimeters of different types of agricultural soils. J. Environ. Qual., 20(4), 801-807. https://doi.org/10.2134/jeq1991.00472425002000040015x
Bergtold, J., & Sailus, M. (2020). Conservation tillage systems in the Southeast: Production, profitability and stewardship. Sustainable Agriculture Research and Education (SARE) Program. Retrieved from http://hdl.handle.net/1903/29527
Bijay-Singh, & Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Appl. Sci., 3(4), 518. https://doi.org/10.1007/s42452-021-04521-8
Blanco-Canqui, H., & Lal, R. (2004). Mechanisms of carbon sequestration in soil aggregates. Crit. Rev. Plant Sci., 23(6), 481-504. 10.1080/07352680490886842
Blankinship, J. C., & Hart, S. C. (2012). Consequences of manipulated snow cover on soil gaseous emission and N retention in the growing season: A meta-analysis. Ecosphere, 3(1). https://doi.org/10.1890/ES11-00225.1
Boe, J. (2022). How much residual soil nitrate is left after the 2021 corn crop? AGVISE Laboratories. Retrieved from https://www.agvise.com/how-much-residual-soil-nitrate-is-left-after-the-2021-corn-crop/
Bouyoucos, G. J. (1962). Hydrometer method improved for making particle size analyses of soils. Agron. J., 54(5), 464-465. https://doi.org/10.2134/agronj1962.00021962005400050028x
Bradley S. Crookston, Matt A. Yost, Maria Bowman, Kristen Veum, Grant Cardon, Jeanette Norton. (2021). Soil health spatial-temporal variation influence soil security on Midwestern, U.S. farms, Soil Security, Volume 3. https://doi.org/10.1016/j.soisec.2021.100005.
Brown, J. R. (2015). North Central Regional Research Publication Bull. No. 221 (Revised): Recommended chemical soil test procedures for the North Central Region. Missouri Agricultural Experiment Station SB 1001. Retrieved from https://extension2.missouri.edu/SB1001
Cárceles Rodríguez, B., Durán-Zuazo, V. H., Soriano Rodríguez, M., García-Tejero, I. F., Gálvez Ruiz, B., & Cuadros Tavira, S. (2022). Conservation agriculture as a sustainable system for soil health: A review. Soil Syst., 6(4), 87. https://doi.org/10.3390/soilsystems6040087
Cardiff, M., Schachter, L., Krause, J., Gotkowitz, M., & Austin, B. (2022). Quantifying annual nitrogen loss to groundwater via edge-of-field monitoring: Method and application. Groundwater, 61(1), 21-34. https://doi.org/10.1111/gwat.13217
Christianson, L., Knoot, T., Larsen, D., Tyndall, J., & Helmers, M. (2014). Adoption potential of nitrate mitigation practices: An ecosystem services approach. Int. J. Agric. Sustain., 12(4), 407-424. https://doi.org/10.1080/14735903.2013.835604
Christianson, L., Tyndall, J., & Helmers, M. (2013). Financial comparison of seven nitrate reduction strategies for Midwestern agricultural drainage. Water Resour. Econ., 2-3, 30-56. https://doi.org/10.1016/j.wre.2013.09.001
Daryanto, S., Wang, L., & Jacinthe, P.-A. (2017). Impacts of no-tillage management on nitrate loss from corn, soybean and wheat cultivation: A meta-analysis. Sci. Rep., 7(1), 12117. https://doi.org/10.1038/s41598-017-12383-7
Doran, J. W., & Zeiss, M. R. (2000). Soil health and sustainability: Managing the biotic component of soil quality. Appl. Soil Ecol., 15(1), 3-11. https://doi.org/10.1016/S0929-1393(00)00067-6
Drury, C. F., Yang, J. Y., De Jong, R., Yang, X. M., Huffman, E. C., Kirkwood, V., & Reid, K. (2007). Residual soil nitrogen indicator for agricultural land in Canada. Can. J. Soil Sci., 87(Special Issue), 167-177. https://doi.org/10.4141/s06-064
Du, Z., Ren, T., Hu, C., & Zhang, Q. (2014). Transition from intensive tillage to no-till enhances carbon sequestration in microaggregates of surface soil in the North China Plain. Soil Tillage Res., 146, 26-31. https://doi.org/10.1016/j.still.2014.08.012
Dumanski, J., Peiretti, R., Benites, J., McGarry, D., & Pieri, C. (2006). The paradigm of conservation agriculture. Paper no. P1-7. Proc. World Association of Soil and Water Conservation, (pp. 58-64).
Duncan, J. M., Welty, C., Kemper, J. T., Groffman, P. M., & Band, L. E. (2017). Dynamics of nitrate concentration-discharge patterns in an urban watershed. Water Resour. Res., 53(8), 7349-7365. https://doi.org/10.1002/2017WR020500
EPA. (2014). NECi method for nitrate reductase nitrate nitrogen analysis (case No. EPA N07-0003).
Evenson, G., Osterholz, W. R., Shedekar, V. S., King, K., Mehan, S., & Kalcic, M. (2023). Representing soil health practice effects on soil properties and nutrient loss in a watershed-scale hydrologic model. J. Environ. Qual., 52(3), 537-548. https://doi.org/10.1002/jeq2.20338
Fageria, N. K., & Moreira, A. (2011). Chapter Four - The role of mineral nutrition on root growth of crop plants. In D. L. Sparks (Ed.), Advances in Agronomy (Vol. 110, pp. 251-331). Academic Press. https://doi.org/10.1016/B978-0-12-385531-2.00004-9
FAO. (2015). Conservation agriculture. Retrieved from https://www.fao.org/conservation-agriculture/en/
Feyereisen, G. W., Hay, C. H., Christianson, R. D., & Helmers, M. J. (2022). Frontier: Eating the metaphorical elephant: Meeting nitrogen reduction goals in Upper Mississippi River basin states. J. ASABE, 65(3), 621-631. https://doi.org/10.13031/ja.14887
Follett, R. F., & Hatfield, J. L. (2001). Nitrogen in the environment: Sources, problems, and management. Sci. World J., 1, 640372. https://doi.org/10.1100/tsw.2001.269
Franzluebbers, A. J. (2020). Soil-test biological activity with the flush of CO2: V. Validation of nitrogen prediction for corn production. Agron. J., 112(3), 2188-2204. https://doi.org/10.1002/agj2.20094
Fuss, C. B., Driscoll, C. T., Groffman, P. M., Campbell, J. L., Christenson, L. M., Fahey, T. J.,... Morse, J. L. (2016). Nitrate and dissolved organic carbon mobilization in response to soil freezing variability. Biogeochemistry, 131(1), 35-47. https://doi.org/10.1007/s10533-016-0262-0
Haney, R.L., Franzluebbers, A.J., Jin, V.L., Johnson, M.V., Haney, E.B., White, M.J., Harmel, R.D. (2012). Soil organic C:N vs. water-extractable organic C:N. Open J. Soil Sci. 2, 269–274.
Haney, R. L., Haney, E. B., Smith, D. R., Harmel, R. D., & White, M. J. (2018). The soil health tool — Theory and initial broad-scale application. Appl. Soil Ecol., 125, 162-168. https://doi.org/10.1016/j.apsoil.2017.07.035
Helmers, M., & Baker, J. (2010). Strategies for nitrate reduction: The Cedar River Case Study. 22nd Annual Proc. of the Integrated Crop Management Conf. Iowa State University. https://doi.org/10.31274/icm-180809-57
Hodde, W., Sesmero, J., Gramig, B., Vyn, T., & Doering, O. (2019). The predicted effect of projected climate change on the economics of conservation tillage. Agron. J., 111(6), 3130-3139. https://doi.org/10.2134/agronj2019.01.0045
Hou, R., Ouyang, Z., Li, Y., Tyler, D. D., Li, F., & Wilson, G. V. (2012). Effects of tillage and residue management on soil organic carbon and total nitrogen in the North China Plain. Soil Sci. Soc. Am. J., 76(1), 230-240. https://doi.org/10.2136/sssaj2011.0107
Huggins, D. R., Randall, G. W., & Russelle, M. P. (2001). Subsurface drain losses of water and nitrate following conversion of perennials to row crops. Agron. J., 93(3), 477-486. https://doi.org/10.2134/agronj2001.933477x
Jasrotia, P., & McSwiney, C. (2009). ION exchange resin strips to estimate nitrogen availability. Michigan State University, Kellogg Biological Station Long-term Ecological Research Program. Retrieved from https://lter.kbs.msu.edu/protocols
Kemper, W. D., & Rosenau, R. C. (1986). Aggregate stability and size distribution. In A. Klute (Ed.), Methods of soil analysis: Part 1 - Physical and mineralogical methods (2nd ed., pp. 425-442). ASA, SSSA. https://doi.org/10.2136/sssabookser5.1.2ed.c17
Lal, R. (2003). Global potential of soil carbon sequestration to mitigate the greenhouse effect. Crit. Rev. Plant Sci., 22(2), 151-184. https://doi.org/10.1080/713610854
Lavaire, T., Gentry, L. E., David, M. B., & Cooke, R. A. (2017). Fate of water and nitrate using drainage water management on tile systems in east-central Illinois. Agric. Water Manag., 191, 218-228. https://doi.org/10.1016/j.agwat.2017.06.004
Ledoux, E., Gomez, E., Monget, J. M., Viavattene, C., Viennot, P., Ducharne, A.,... Mary, B. (2007). Agriculture and groundwater nitrate contamination in the Seine basin. The STICS-MODCOU modelling chain. Sci. Total Environ., 375(1), 33-47. https://doi.org/10.1016/j.scitotenv.2006.12.002
Lemke, D. W., & McKenna, D. P. (2005). Foreword, Acknowledgement, Preface. In Final report: Gulf hypoxia and local water quality concerns workshop (pp. vii-ix). St. Joseph, MI: ASABE. https://doi.org/10.13031/2013.24254
Martin, E. C. (1992). Management strategies to minimize nitrate leaching in seed corn production. PhD diss. East Lansing, MI: Michigan State University. https://doi.org/10.25335/asda-es87
Masri, Z. (2017a). Soil vitality, what does it mean and how we measure it. AgroLiquid Newsletter. Retrieved from https://www.researchgate.net/publication/320689963_Soil_Vitality_Agroliquid_Newsletter_07-06-17
Masri, Z. (2017b). Ammonia volatility and nitrate leaching study of nitrogen fertilizers. AgroLiquid internal report. (17-705).
Masri, Z., & Ryan, J. (2005). Soil organic matter and related physical properties in a Mediterranean wheat-based rotation trial. Soil Tillage Res., 87(2), 146-154. https://doi.org/10.1016/j.still.2005.03.003
Masri, Z., Bird, G. W., Thelen, K., & Steinke, K. (2015). Long-term strategies for improving the productivity of the marginal droughty soils in Michigan amidst global environmental challenges. Research Proposal submitted to ProjectGreen, Michigan State University (MSU).
Nakajima, T., Shrestha, R. K., Jacinthe, P.-A., Lal, R., Bilen, S., & Dick, W. (2016). Soil organic carbon pools in ploughed and no-till Alfisols of central Ohio. Soil Use Manag., 32(4), 515-524. https://doi.org/10.1111/sum.12305
Ogle, S. M., Swan, A., & Paustian, K. (2012). No-till management impacts on crop productivity, carbon input and soil carbon sequestration. Agric. Ecosyst. Environ., 149, 37-49. https://doi.org/10.1016/j.agee.2011.12.010
Osterholz, W. R., Schwab, E. R., Duncan, E. W., Smith, D. R., & King, K. W. (2023). Connecting soil characteristics to edge-of-field water quality in Ohio. J. Environ. Qual., 52(3), 476-491. https://doi.org/10.1002/jeq2.20308
Patel, K. F., Tatariw, C., MacRae, J. D., Ohno, T., Nelson, S. J., & Fernandez, I. J. (2018). Soil carbon and nitrogen responses to snow removal and concrete frost in a northern coniferous forest. Can. J. Soil Sci., 98(3), 436-447. https://doi.org/10.1139/cjss-2017-0132
Pikul Jr, J. L. (2003). Gravimetric measurement of soil water. In B. A. Stewart, & T. Howell (Eds.), Encyclopedia of water science (pp. 879-881). New York, NY: Marcel Dekker.
Prokopy, L. S., Floress, K., Arbuckle, J. G., Church, S. P., Eanes, F. R., Gao, Y.,... Singh, A. S. (2019). Adoption of agricultural conservation practices in the United States: Evidence from 35 years of quantitative literature. J. Soil Water Conserv., 74(5), 520-534. https://doi.org/10.2489/jswc.74.5.520
Quincke, J. A., Wortmann, C. S., Mamo, M., Franti, T., & Drijber, R. A. (2007). Occasional tillage of no-till systems: Carbon dioxide flux and changes in total and labile soil organic carbon. Agron. J., 99(4), 1158-1168. https://doi.org/10.2134/agronj2006.0317
Randall, G. W., & Mulla, D. J. (2001). Nitrate nitrogen in surface waters as influenced by climatic conditions and agricultural practices. J. Environ. Qual., 30(2), 337-344. https://doi.org/10.2134/jeq2001.302337x
Rekha, P. N., Kanwar, R. S., Nayak, A. K., Hoang, C. K., & Pederson, C. H. (2011). Nitrate leaching to shallow groundwater systems from agricultural fields with different management practices. J. Environ. Monit., 13(9), 2550-2558. https://doi.org/10.1039/C1EM10120J
Sawyer, J. E., & Randall, G. W. (2008). 5. Nitrogen Rates. In Final report: Gulf hypoxia and local water quality concerns workshop (pp. 59-71). St. Joseph, MI: ASABE. https://doi.org/10.13031/2013.24244
Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J., & Scow, K. (2018). Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS One, 13(2), e0192953. https://doi.org/10.1371/journal.pone.0192953
Shokrana, M. S. (2022). Predicting the effect of weir management on the discharge of a controlled drainage system in a changing climate. PhD. diss. East Lansing, MI: Michigan State University.
Six, J., Elliott, E.T., Paustian, K. (2000). Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture, Soil Biology and Biochemistry, Volume 32, Issue 14, Pages 2099-2103. https://doi.org/10.1016/S0038-0717(00)00179-6.
Soil Survey Staff. (2019). Web Soil Survey. Natural Resources Conservation Service, United States Department of Agriculture. Retrieved from http://websoilsurvey.sc.egov.usda.gov/
Stuart, D., Schewe, R. L., & McDermott, M. (2014). Reducing nitrogen fertilizer application as a climate change mitigation strategy: Understanding farmer decision-making and potential barriers to change in the US. Land Use Policy, 36, 210-218. https://doi.org/10.1016/j.landusepol.2013.08.011
Syswerda, S. P., Basso, B., Hamilton, S. K., Tausig, J. B., & Robertson, G. P. (2012). Long-term nitrate loss along an agricultural intensity gradient in the Upper Midwest USA. Agric. Ecosyst. Environ., 149, 10-19. https://doi.org/10.1016/j.agee.2011.12.007
Tiessen, K. H., Elliott, J. A., Yarotski, J., Lobb, D. A., Flaten, D. N., & Glozier, N. E. (2010). Conventional and conservation tillage: Influence on seasonal runoff, sediment, and nutrient losses in the Canadian prairies. J. Environ. Qual., 39(3), 964-980. https://doi.org/10.2134/jeq2009.0219
Urban, A., Rogowski, P., Wasilewska-Debowska, W., & Romanowska, E. (2021). Understanding maize response to nitrogen limitation in different light conditions for the improvement of photosynthesis. Plants, 10(9), 1932. https://doi.org/10.3390/plants10091932
US Army Corps of Engineers. (2011). Regional supplement to the Corps of Engineers Wetland Delineation Manual: Northcentral and Northeast Region (Version 2.0). ERDC/EL TR-12-1. (J. S. Wakeley, R. W. Lichvar, C. V. Noble, & J. F. Berkowitz, Eds.) U.S. Army Engineer Research and Development Center. Retrieved from https://usace.contentdm.oclc.org/utils/getfile/collection/p266001coll1/id/7640
USDA-NRCS. (2018). Field indicators of hydric soils in the United States. A guide for identifying and delineating hydric soils. Version 8.2. (L. M. Vasilas, G. W. Hurt, & J. F. Berkowitz, Eds.)
USGCRP. (2018). Fourth National Climate Assessment, Volume II: Impacts, risks, and adaptation in the United States. Washington, DC: U.S. Global Change Research Program. https://doi.org/10.7930/NCA4.2018
Wallman, M., & Delin, S. (2021). Nitrogen leaching from tile-drained fields and lysimeters receiving contrasting rates and sources of nitrogen. Soil Use Manag., 38(1), 596-610. https://doi.org/10.1111/sum.12724
Waring, E. R., Lagzdins, A., Pederson, C., & Helmers, M. J. (2020). Influence of no-till and a winter rye cover crop on nitrate losses from tile-drained row-crop agriculture in Iowa. J. Environ. Qual., 49(2), 292-303. https://doi.org/10.1002/jeq2.20056
Weersink, A., Walker, M., Swanton, C., & Shaw, J. E. (1992). Costs of conventional and conservation tillage systems. J. Soil Water Conserv., 47(4), 328-334. Retrieved from https://www.jswconline.org/content/jswc/47/4/328.full.pdf
Wood, S. A., & Bowman, M. (2021). Large-scale farmer-led experiment demonstrates positive impact of cover crops on multiple soil health indicators. Nat. Food, 2(2), 97-103. https://doi.org/10.1038/s43016-021-00222-y
Xie, Y. (2014). Watershed modeling, farm tenancy and adoption of conservation measures to facilitate water quality trading in the upper Scioto Watershed, Ohio. PhD diss. Columbus, OH: Ohio State University.
Zhao, S. L., Gupta, S. C., Huggins, D. R., & Moncrief, J. F. (2001). Tillage and nutrient source effects on surface and subsurface water quality at corn planting. J. Environ. Qual., 30(3), 998-1008. https://doi.org/10.2134/jeq2001.303998x