Research update: Economics of tillage in Minnesota

 Katie Wilts Johnson, Extension economist, Joleen Hadrich, Extension economist, Anna Cates, State soil health specialist, and Ann Marcelle Lewandowski, MOSH coordinator

Tillage method is one of the primary management decisions a farmer can make. It affects field work timing, input selection and efficiency, and the risk of soil and nutrient loss from the farm. As such, we would expect it to have an impact on a farmer’s bottom line. This makes it risky to consider new tillage systems, as there are a lot of unknowns in how costs of seed, fertilizer, chemical, or fuel will shift- not to mention crop yield. However, using the FINBIN data, we are now able to analyze how yields are impacted and what costs are most altered.

To better understand farm profitability in different tillage systems, we examined crop yield and production expenses reported by participants in Minnesota farm business management programs.

Study details

We analyzed 2009-2019 Minnesota farm financial data provided through a partnership between the Minnesota State Colleges and University Farm Business Management (FBM) program, Southwest Minnesota Farm Business Management Association, and the Center for Farm Financial Management. The aggregated data is available through FINBIN at www.finbin.umn.edu. Anyone can look at regional or statewide data, and FINBIN provides financial benchmarks to farm producers, educators, and lenders.

The financial data includes direct and overhead expenses, yields and returns, and other financial factors such as crop insurance indemnities and government payments. Farmers also provide information about their farming operations like crop rotations, the presence of livestock on their farm, fertilizer practices, and tillage systems. We used the data to determine which factors impact crop yields most, and to estimate crop yields based on those factors for three types of tillage systems: 1) no-till, 2) strip-till, and 3) chisel-till.

To estimate yield associated with a particular tillage system, we used regression analysis, which uses the known value of independent variables to predict the unknown value of a dependent variable. We chose not to average the reported yields of a particular system, since that wouldn’t represent Minnesota’s diversity of crop rotations, soil types, weather patterns over the eleven years, and cultural farming norms. Instead, the regression analysis allowed us to estimate how yields would be impacted by tillage types, while accounting for each farm’s geographic location, size of the farm, input costs, previous crop grown, and varying weather conditions over time. In this case, the dependent variable to be predicted is Yield and the independent variables are Location, Acres, Inputs, Previous Crop, and Year.

(1) Yield = β0 +β1 Location + β2 Acres + β3 Inputs + β4 Previous crop + β5 Year + ε

In addition to estimating yield, we also compared input costs among tillage systems.
In some regions, no-till and strip-till are uncommon and not very many farms reported data over the 10-year study period. Less data means less robust estimates of yield and input costs, so pay attention to the number of observations when comparing results in Tables 1-3.

Tillage effect on cash crop yield

We found that yield was altered by tillage type and by geographic location, especially for corn (Table 1). Compared to chisel-till or strip-till, no-till had little impact on corn yields in the southern part of the state, but lowered yields in the north. Strip tilled corn yields were much closer to chisel till yields across the state. Soybean yields were less sensitive to the type of tillage system (Table 2).

Tillage effects on spring wheat yield differed from corn and soybeans (Table 3). The highest Minnesota spring wheat yield came from no-till farmers in the northwest region. Chisel-till yields for the same region were not far behind. Yields for spring wheat were much lower for no-till farmers from all other regions. Spring wheat is not commonly strip-tilled, so this wasn’t included in the analysis.

Table 1. Estimated Minnesota average corn yields in bushels/acre by region and tillage, 2009 to 2019 using Ordinary Least Squares (OLS) regression. Number of observations noted in parentheses.

Region	No-till yield	Strip-till yield	Chisel-till yield
Northwest	129 (37)	147 (14)	155 (1,790)
Northeast	134 (104)	154 (12)	149 (972)
Central	145 (33)	182 (158)	175 3,883)
South Central	184 (161)	184 (288)	182 (7,363)
Southeast	187 (73)	185 (231)	188 (7,213)
Southwest	167 (116)	179 (76)	183 (3,022)

Source: Estimated yields derived from an OLS regression analysis including region, size of the farm, input costs, the previous crop grown, and crop year. FINBIN, University of Minnesota (2009 to 2019).

Table 2. Estimated Minnesota average soybean yields in bushels/acre by region and tillage, 2009 to 2019 using OLS regression. Number of observations noted in parentheses.

Region	No-till yield	Strip-till yield	Chisel-till yield
Northwest	37 (93)	24 (7)	38 (3,655)
Northeast	39 (95)	45 (5)	39 (658)
Central	47 (79)	50 (129)	47 (2,931)
South Central	52 (433)	53 (130)	53 (6,595)
Southeast	51 (172)	51 (206)	53 (5,804)
Southwest	50 (278)	52 (39)	52 (1,947)

Source: Estimated yields derived from an OLS regression analysis including region, size of the farm, input costs, the previous crop grown, and crop year. FINBIN, University of Minnesota (2009 to 2019).

Table 3. Estimated Minnesota spring wheat soybean yields in bushels/acre by region and tillage, 2009 to 2019 using OLS regression. Number of observations noted in parentheses.

Region	No-till yield	Chisel-till yield
Northwest	64 (48)	62 (2,670)
Northeast	36 (9)	45 (62)
Central	51 (9)	55 (407)
South Central	39 (7)	54 (83)
Southeast	– (1)	48 (141)
Southwest	– (0)	58 (21)

Source: Estimated yields derived from an OLS regression analysis including region, size of the farm, input costs, the previous crop grown, and crop year. FINBIN, University of Minnesota (2009 to 2019).

Tillage effect on input costs

Corn

Figure 1. Minnesota average corn input expenses, 2009 to
2019 ($/acre).

As expected, tillage also altered crop input costs. In corn, the highest input cost was fertilizer (Figure 1). On average, corn no-till farmers reported $3/acre more on fertilizer, $2/acre more on chemical, but spent $9/acre less on seed and $7/acre less on fuel than corn chisel-till farms. 

Strip-till corn farmers were in between no-till and chisel-till input costs for seed, chemical, and fuel costs, and they reported the lowest fertilizer costs of the three tillage methods. This may be due to the efficiency of banding and incorporating fertilizer only in the crop row.

Soybean

Figure 2. Minnesota average soybean input expenses, 2009 to
2019 ($/acre).

In soybeans, the highest expense was seed across all tillage types (Figure 2). Soybean no-till acreage averaged $2/acre more than soybean chisel-till farmers for fertilizer expenses and $3/acre more for chemical costs, but chisel-till reports $4/acre higher fuel costs. 

Strip-till soybean farmers reported the lowest seed and fertilizer costs per acre and the highest chemical and fuel costs per acre of the three tillage methods.

Spring wheat

Figure 3. Minnesota average spring wheat input expenses, 2009 to
2019 ($/acre).

For spring wheat, the most expensive input was fertilizer (Figure 3). Spring wheat no-till farmers reported an average of $3/acre lower seed costs, $8/acre lower fertilizer costs, $2/acre lower chemical costs, and $1.50/acre lower fuel costs than spring wheat chisel-till farmers.

Conclusions

The aggregated results from 10 years of farm financial data show that there are tradeoffs between yields and input costs across the state of Minnesota. Overall profitability will result from a balance between these two factors as well as marketing and other farm financial decisions, so any tillage system could result in a profitable enterprise on an individual farm. We hope that farmers can use these results to inform expectations about growing corn, soybeans, and wheat under different tillage systems.

The competitive yields and low input prices in strip-till make it an attractive middle ground of tillage, where you can achieve some soil health benefits with low risk of yield loss. However, new equipment is expensive and changing tillage methods is no small undertaking; as shown by this dataset, it can change how you fertilize, spray, and choose seed. Reducing the intensity of tillage through fewer tillage passes or shallower tillage depths is another option to improve soil structure and reduce erosion without new equipment. The Upper Midwest Tillage Guide has more technical recommendations on how to approach new tillage systems.