2019 SWCS International Annual Conference July 28-31, 2019 Pittsburgh, Pennsylvania

1. Potato yield and farm revenue as functions of
crop rotation and tillage practice in
New Brunswick, Canada
Eric Ye Liu, Sheng Li*, Van Lantz, and Edward Olale
SWCS 2019 Annual Conference
Pittsburgh, PA, USA

2. Background
• Potato is a major cash crop in Atlantic Canada
• Potato production has high environmental impacts
• High input and high soil disturbance
• Soil erosion
• Nutrient losses
• Pesticide contamination
• BMPs
• Diversion terraces and grassed waterways
• Contour cropping
• Retention pond
• Riparian buffer
• Crop rotation
• Spring tillage

3. Crop rotation and spring tillage
• Current knowledge
• Longer rotations (lower potato frequency) generally have
higher potato yield
• Spring tillage increases potato yield in some cases
• Concerns
• Conclusions drawn under controlled conditions (other
conditions kept the same)
• Real life conditions are much more complicated
• Variations of key affecting factors (e.g., climate, soil, farm inputs)
• Farmers adjust inputs and management practices according to
BMP adopted
• BMP adoption is largely an economic decision
• Need to consider the full cycle
• Cost and revenue
• Analysis better to be based on data collected from operating
farms

4. Objective
• To examine how crop rotation and spring tillage
perform agronomically and economically in
operating farms
• Effects on potato yield
• Effects on farm revenue

5. Study site --- Black Brook Watershed (BBW)
• Location
• North-western NB
• Potato belt
• Area = 14.5 km2
• Conditions
• Humid cold continental
climate
• Rolling and undulating
landscape
• Shallow stony soils
• Potato production
systems

6. Data collection
• Five years (2006, 2008, 2009, 2010, 2012)
• Fields in BBW cropped with Russel Burbank Potato
• Climate: precipitation, temperature
• Soil: texture, pH, fertility
• Farm inputs: fertilizer, pesticides
• Potato yield
• BMP information
• Crop rotation: 1-in-3, 1-in-2 and 2-in-3
• Tillage: Fall plough vs. Spring plough
• A dataset for 67 fields with 558 observations

7. Data analysis – potato yield modeling
• Statistical method
• Just and Pope stochastic production function framework
• A multiplicative heteroscedastic regression model
• Model I: yield as a function of BMP variables only
𝑙𝑛 𝑌𝑖𝑒𝑙𝑑 = 𝑓 𝐶𝑟𝑜𝑝, 𝑇𝑖𝑙𝑙
• Model II: yield as a function of all filtered variables
𝑙𝑛 𝑌𝑖𝑒𝑙𝑑 = 𝑓 𝐶𝑟𝑜𝑝, 𝑇𝑖𝑙𝑙, 𝐹𝑒𝑟𝑡, 𝑃𝑟𝑒cip, 𝐻𝐼, 𝐼𝐼, 𝐹𝐼, 𝑝𝐻, 𝐶𝐸𝐶, 𝑆𝑎𝑛𝑑
• Model III: two step regression
• Step 1: farm inputs as functions of BMP variables
𝑙𝑛 𝐹𝑒𝑟𝑡 = 𝑓 𝐶𝑟𝑜𝑝, 𝑇𝑖𝑙𝑙 ; 𝑃𝐼𝑠 = 𝑓 𝐶𝑟𝑜𝑝, 𝑇𝑖𝑙𝑙
• Step 2: yield as a function of all filtered variables except
BMP variables
𝑙𝑛 𝑌𝑖𝑒𝑙𝑑 = 𝑓 𝐹𝑒𝑟𝑡, 𝑃𝑟𝑒cip, 𝐻𝐼, 𝐼𝐼, 𝐹𝐼, 𝑝𝐻, 𝐶𝐸𝐶, 𝑆𝑎𝑛𝑑

8. Yield modeling – Model I
• 1in2 rotation has higher
yield than 1in3 and 2in3
rotations
• Spring tillage reduces yield
• Do not make much sense
• Impacts from farm input,
climate and soil property
variables
Model I Model II
Intercept 6.106 6.204
BMPs
1in3‡ -0.116*** -0.159***
2in3§ -0.058*** -0.098***
SMP# -0.041*** 0.058***
Farm inputs
Fertilizer -0.00007***
Herbindex 0.074***
Insectindex 0.026***
Fungindex 0.074***
Climatic
Precipitation -0.0002***
Soil properties
pH -0.015***
CEC 0.011***
sand 0.0005***
𝑙𝑛 𝑌𝑖𝑒𝑙𝑑 = 𝑓 𝐶𝑟𝑜𝑝, 𝑇𝑖𝑙𝑙

9. Yield modeling – Model II
• 1in2 rotation has higher yield
than 1in3 and 2in3 rotations
• Spring tillage increases yield
• Fertilizer reduces yield
• Pesticides increases yield
• Still do not make sense
• Interactions between
explanatory variables
• Farm inputs adjusted
significantly according to crop
rotation
Model I Model II
Intercept 6.106 6.204
BMPs
1in3‡ -0.116*** -0.159***
2in3§ -0.058*** -0.098***
SMP# -0.041*** 0.058***
Farm inputs
Fertilizer -0.00007***
Herbindex 0.074***
Insectindex 0.026***
Fungindex 0.074***
Climatic
Precipitation -0.0002***
Soil properties
pH -0.015***
CEC 0.011***
sand 0.0005***
𝑙𝑛 𝑌𝑖𝑒𝑙𝑑 = 𝑓 𝐶𝑟𝑜𝑝, 𝑇𝑖𝑙𝑙, 𝐹𝑒𝑟𝑡, 𝑃𝑟𝑒cip, 𝐻𝐼, 𝐼𝐼, 𝐹𝐼, 𝑝𝐻, 𝐶𝐸𝐶, 𝑆𝑎𝑛𝑑

10. Yield modeling – Model III – Step 1
• 1in3 vs 1in2
• Lower fertilizer and herbicide inputs
• 2in3 vs 1in2
• Higher fertilizer input
• Lower herbicide and insecticide inputs, not as expected, may
reflect outbreaks
• Spring tillage vs fall tillage
• Lower fertilizer and herbicide inputs
• Higher fungicide input
ln(Fertilizer) Herb-index Insect-index Fungi-index
Intercept 7.088 1.02 1.015 1
BMPs
1in3‡ -0.052*** -0.044*** -0.022 0.0002
2in3§ 0.019*** -0.019** -0.131*** 0.0002
SMP# -0.023*** -0.310*** 0.087 0.037***

11. Yield modeling – Model III – Step 2
• Yield
• Increases with farm inputs, CEC
and Sand content
• Decreases with precipitation and
pH
ln(Y)
Intercept 5.87
Farm inputs
Fertilizer 0.00001***
Herbindex 0.117***
Insectindex 0.022***
Fungindex 0.019***
Climatic
Precipitation -0.0001***
Soil properties
pH -0.017***
CEC 0.015***
Sand 0.002***
Trends mostly as expected or can be well explained

12. Data analysis – farm revenue modeling
• Model III for yield estimation
• Crop rotations
• 2in3 (P-P-B), 1in2 (P-B) and 1in3 rotation (P-B-B)
• Variable costs and fixed costs based on average conditions
• Revenue (calculated from potato and barley yields)
• 6 year duration
• Keep the total duration the same while ensure full cycle or
rotations
• Results
• Potato year average net revenue
• Six-year total net revenue
• Sensitivity analysis
• The effects of crop product price change on the ranking of crop
rotations

13. Farm net revenue – Potato year average
• Increases slightly
with longer rotation
• Decreases with the
decrease of potato
price
• Order changes when
potato price drop by
25%
Potato Year Average 6-year Total
2in3 1in2 1in3 2in3 1in2 1in3
Baseline 4389 4437 4477 17279 12890 8392
Potato Price
-46% -69 -44 -73 -553 -553 -707
-25% 2425 2460 2458 7665 5642 3486
-10% 3427 3471 3496 13434 9991 6430
+10% 5350 5403 5458 21125 15788 10354
+25% 6792 6853 6930 26893 20137 13298
+50% 9196 9268 9383 36507 27384 18204
Grain Price
-50% 16896 12226 7507
-25% 17112 12558 7950
-10% 17242 12757 8215
+10% 17415 13022 8569
+25% 17546 13221 8834
+50% 17712 13553 9277
+100% 18146 14217 10161
+963% 25669 25669 25429
Longer rotation increases potato year revenue

14. Farm net revenue – Six year total
• Decreases
significantly with
longer rotation
• Decreases with the
decreases of potato
and grain price
• Rank order changes
when potato price
drops by 46% or
grain price increases
by 963%
Longer rotation decreases six year total revenue
Potato Year Average 6-year Total
2in3 1in2 1in3 2in3 1in2 1in3
Baseline 4389 4437 4477 17279 12890 8392
Potato Price
-46% -69 -44 -73 -553 -553 -707
-25% 2425 2460 2458 7665 5642 3486
-10% 3427 3471 3496 13434 9991 6430
+10% 5350 5403 5458 21125 15788 10354
+25% 6792 6853 6930 26893 20137 13298
+50% 9196 9268 9383 36507 27384 18204
Grain Price
-50% 16896 12226 7507
-25% 17112 12558 7950
-10% 17242 12757 8215
+10% 17415 13022 8569
+25% 17546 13221 8834
+50% 17712 13553 9277
+100% 18146 14217 10161
+963% 25669 25669 25429

15. Conclusions
• Potato yield
• Longer rotation and spring tillage lead to higher yield
• Higher farm inputs lead to higher yield
• Shorter rotation had higher fertilizer inputs
• BMP effects on yield need to be simulated indirectly (the
two-step approach)
• Farm revenue
• Short term loss of revenue for longer rotations
• Other incentives needed for promoting crop rotation
BMPs
• Long term research needed to account for soil
quality and environmental benefits

16. Questions?