Strategies for variable rate nitrogen fertilization in hummocky terrain

Transcription

1 Strategies for variable rate nitrogen fertilization in hummocky terrain H. J. Beckie 1, A. P. Moulin 2, and D. J. Pennock 3 1 Agriculture and Agri-Food Canada Research Centre, 107 Science Place, Saskatoon, Saskatchewan Canada S7N OX2; 2 Agriculture and Agri-Food Canada Research Centre, Box 1000A R. R. #3, Brandon, Manitoba Canada R7A 5Y3; and 3 Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan Canada, S7N 5A8. Contribution no. 1224, received 28 November 1996, accepted 2 July Beckie, H. J., Moulin, A. P. and Pennock, D. J Strategies for variable rate nitrogen fertilization in hummocky terrain. Can. J. Soil Sci. 77: A study was conducted from 1994 to 1996 in a hummocky landscape near Prince Albert, Saskatchewan in the moist Black soil climatic zone to determine the best criterion for defining fertilizer management zones within a field and how much fertilizer to apply in each zone. A uniform rate fertilization (CF) treatment was compared with three variable rate fertilization (VRF) treatments that used management zones based on soil residual nitrate-n (VRF rn ), organic carbon (VRF om ) and topography (VRF t ). For VRF om, fertilizer recommendations were based on soil residual N levels within zones and yield potentials that differed between zones. Flax (Linum usitatissimum) was grown in 1994, spring wheat (Triticum aestivum) in 1995, and canola (Brassica rapa) in Fertilizer use efficiency (FUE), defined as kilograms seed per kilogram fertilizer N, was markedly higher for VRF om than CF or VRF rn. This enhanced FUE resulted in net returns, defined as crop revenue minus fertilizer cost, of about $10 ha 1 more than that of CF. Three successive years of VRF in this study suggests that this practice can enhance the efficient use of fertilizer N and has potential to increase profitability of fertilizer use, by more closely matching fertilizer N inputs with crop nutrient requirements. Key words: Brassica rapa, Linum usitatissimum, Triticum aestivum, nitrogen, variable rate fertilization, precision agriculture Beckie, H. J., Moulin, A. P. et Pennock, D. J Stratégies de fumure azotée à taux variable en terrain bosselé. Can. J. Soil Sci. 77: L objet de recherches conduites de 1994 à 1996 en pédopaysage bosselé dans la zone climatique humide à sols noirs, aux environs de Prince Albert en Saskatchewan était de déterminer le meilleur critère d établissement de zones de fertilité variable dans un même champ ainsi que les quantités d engrais à apporter dans chacune de ces zones. Nous avons comparé la fumure à taux uniforme (TU) à la fumure à trois taux variables basés sur l établissement de zones selon 1, la teneur résiduelle en N nitrique du sol (FTVnr), 2, la teneur en C organique du sol (FTVm.o.) et 3, la topographie (FTVt). Pour FTVm.o. et FTVt, les recommandations de fumure étaient basées sur les niveaux de N résiduel du sol à l intérieur de chaque zone et des différences de productivité observées entre les diverses zones. La culture utilisée était, en 1994, le lin (Linum usitatissimum), en 1995, le blé de printemps (Triticum aestivum) et, en 1996, la navette canola (Brassica rapa). L efficacité d utilisation de l engrais (EUE), mesurée en kg de graines de semence par kg de N de fumure était sensiblement plus forte dans le cas de FTVm.o. et de FTVt que dans celui de TU ou de FTVrn. Ce gain d efficacité s exprimait par des revenus nets (recettes nettes de la culture moins les coûts en fumure) d environ 10 $ ha 1 de plus qu en régime de fumure uniforme. Après 3 années successives de FTV, on peut conclure que cette pratique peut améliorer la valorisation du N de fumure et, éventuellement aussi, sa rentabilité en ajustant de façon plus étroite les apports de fertilisant aux besoins nutritionnels de la culture. Mots clés: Brassica rapa, Linum usitatissimum, Triticum aestivum, azote, fumure à taux variable, agriculture de précision Precision agriculture employs variable management practices within a field according to site or soil conditions. Variable rate fertilization (VRF) largely constitutes precision agriculture; fertilizer rates are varied within a field to minimize over- and under-application that can occur with conventional fertilization (CF) using a uniform rate. VRF has potential to increase fertilizer use efficiency (FUE) and profitability and to reduce nutrient losses in the environment, provided application rates correctly match crop nutrient requirements in the field. These potential benefits of VRF have not been restricted by technological developments, such as the Global Positioning System (GPS), geographic information systems, automatic fertilizer rate adjustment and yield monitors, but by lack of basic agronomic information. Information is needed on which fields would most benefit from VRF, how to subdivide fields into different fertilizer 589 management zones, i.e., areas of similar soil/site qualities that determine crop productivity and how much fertilizer to apply in each zone. VRF can be profitable if there is a sufficiently large field variation of the factor(s) affecting crop yield to justify the extra costs of collecting information and managing parts of fields differently. If sufficient variation does exist, the factor must be correctly mapped, analyzed and interpreted, and managed. Factors that affect yield include topography; soil properties, such as organic matter, available nutrients, texture, salinity, acidity, available water, drainage and crop rooting depth; weed, insect and disease levels; cropping history; and most importantly, weather namely precipitation and temperature (Sawyer 1994). Yield is determined by the factor most limiting to productivity. Yield maps can be used to delineate fertilizer management zones only if there is a consistent

2 590 CANADIAN JOURNAL OF SOIL SCIENCE yield variation pattern from year-to-year and crop-to-crop, or the factor(s) causing yield variation are fertility related and can be identified. However, yield variation patterns generally are not consistent from season to season (Sawyer 1994; Stafford et al. 1996). The most important function of yield mapping is evaluating CF or VRF strategies using FUE as the criterion. Soil fertility and amount of water available to the crop usually are the two main factors affecting crop yield potential and thus nutrient requirement. The first is strongly related to organic matter concentration in, and thickness of, the topsoil, whereas the second depends on precipitation, net infiltration, evapotranspiration and the ability of the soil to hold water in the crop rooting zone. The two are interrelated, as soils high in organic matter can store more water than loworganic matter soils, other factors being equal. Topography is a primary factor contributing to soil variability, and influences both fertility and available water (Mulla 1993). Thus, soils on similar slope positions tend to have similar qualities. Soils on the prairies were developed on material laid down by glaciers and their meltwaters, with the result that the majority of fields have significant topographic variation (Moss 1965). These fields are well suited to VRF. Historically, there has been a debate about whether it is better to apply less nitrogen (N) fertilizer on the lower (L)- slope/level-depressional areas that are typically high in N and organic matter, or more on upper- (U) slope/shoulders that are typically low in N and organic matter. If both areas of the field are assumed to have the same yield potential, then less fertilizer should be applied on the L-slope areas and more on the U-slope areas to obtain similar yields. However, yield potentials usually are lower on U-slope positions, which are less fertile and have less water, and greater on mid- (M) and L-slope areas, which are typically more fertile and have more water (Kachanoski et al. 1985; Elliott and de Jong 1992; Moulin et al. 1994; Nolan et al. 1995). Therefore, fertilizer recommendations should take into account both soil residual N levels prior to seeding and yield potentials, i.e., match crop N demand with soil plus fertilizer N supply. Yield response to fertilizer N usually is greater on L- and M-slope than on U-slope positions, because of the greater positive contribution of increased available water to yield relative to the less negative contribution of higher soil fertility (Kachanoski et al. 1985; Elliott and de Jong 1992). However, unpredictability of weather causes unreliable predictions of yield responses on different landscape positions (Pennock and Anderson 1992). There often is a significant interaction between N and water. In dry years, low areas may yield best; in wetter years, mid-slope areas may excel. To determine the best criterion for defining fertilizer management zones within a field and how much fertilizer to apply in each zone, a landscape study was conducted from 1994 to 1996 near Prince Albert, Saskatchewan in the moist Black soil climatic zone. MATERIALS AND METHODS The study was conducted at the Conservation Learning Centre (53.0 N, W) located near Prince Albert, Saskatchewan. The soil is classified as an Orthic Black Fig. 1. Elevation contour map of the study area at the Conservation Learning Centre near Prince Albert, Saskatchewan. Chernozem (Udic Haploboroll) with a loam texture and an average ph of 7.1. The hummocky landscape has slopes of 9% or less (Fig. 1). The study area consisted of 20% U slopes (shoulders), 50% M slopes (footslopes) and 30% L slopes (level-depressional areas). The experiment was arranged in a randomized complete block design with three replications. Plot size was m. The center (mid-width) of each plot was sampled to a depth of 0.15 m at 10-m intervals in the spring of 1994 to determine percent soil organic carbon (Fig. 2). Soil organic carbon was measured using a LECO CR-12 carbon determinator. Soil was sampled in late April prior to seeding each year at 0 to 0.15-m and 0.15 to 0.30-m depths at the same grid-point locations to determine residual NO 3 -N levels (Hamm et al. 1970; Winkleman 1994) (Fig. 3). Fertilizer treatments were: 1) CF using a uniform rate based on the plot-average soil residual N level and a yield goal listed for a normal precipitation growing season in the moist Black soil climatic zone as determined from a computer program (Anonymous 1993); 2) VRF rn using three management zones based on soil residual NO 3 -N levels and the same yield goal as for the CF treatment; 3) VRF om using three management zones based on soil organic carbon levels and 4) VRF t using three management zones based on topography: U-, M- and L-slope positions, delineated from the digital elevation model (Pennock et al. 1994) of the site. Elevation was surveyed using a 10-m square grid. Soil residual N and organic carbon were interpolated using an inversedistance weighting method (distance power 1). Demarcations for the three soil NO 3 -N zones of VRF rn were chosen such that fertilizer N recommendations, calculated for each kg ha 1 increment in soil residual NO 3 -N, differed by at least 10 kg N ha 1 between zones. The zones in 1994

3 BECKIE ET AL. VARIABLE RATE NITROGEN FERTILIZATION 591 Fig. 2. Map of soil organic carbon ( m). and 1996 were 0 7, 7 14 and kg ha 1, whereas those in 1995 were 0 6, 6 12 and kg ha 1. Fertilizer N recommendations for management zones of VRF om and VRF t were based on average residual NO 3 -N levels within zones and yield goals (Anonymous 1993) that varied between zones. In 1994, the high-organic matter (VRF om ) and L-slope (VRF t ) zones were assigned a high yield goal, listed under fertilizer recommendations for a growing season with above-normal probability of precipitation, because of the greater moisture and fertility levels that generally characterize these areas. The medium-organic matter and M-slope zones were assigned a medium yield goal, and the low-organic matter and U-slope zones were assigned a low yield goal (listed for a growing season with below-normal probability of precipitation). However, based on 1994 yield results, a medium yield goal was assigned to both the medium- and high-organic matter zones of VRF om and M- and L- slope zones of VRF t in 1995 and Flax (cv. Norlin) was direct seeded into wheat stubble at a rate of 56 kg ha 1 on 10 May 1994 using an air drill with 23 cm row spacing. Canada Prairie spring wheat (cv. AC Taber) was seeded at a rate of 128 kg ha 1 on 25 May 1995, and canola (cv. AC Parkland) was seeded at a rate of 9 kg ha 1 on 4 June Alternating broadleaf and cereal crops is recommended in direct-seeding systems for optimal crop establishment and growth because of improved residue and pest management. Fertilizer N (urea, ) was sidebanded in all years. Rates were manually adjusted in VRF plots. All plots received fertilizer P (monoammonium phosphate, ), which was placed with flax, wheat and canola seed at a rate of 10, 15 and 10 kg P ha 1, respectively. Fertilizer S was applied as ammonium sulphate with canola seed at a rate of 11 kg S ha 1 to all plots. Three days prior to seeding, Fig. 3. Soil residual NO 3 -N map for 1994, 1995 and 1996.

4 592 CANADIAN JOURNAL OF SOIL SCIENCE glyphosate was applied for non-selective weed control. Herbicides were applied postemergence for in-crop weed control as required. At crop maturity, seed yield was determined by harvesting 5-m intervals (cells) across the entire length of each plot, using a small plot combine that had a 1.25-m cutting width and was equipped with a distance meter. The site was swathed and combined using field-scale equipment to harvest the remaining crop and to uniformly spread crop residue. The FUE for each cell was calculated as yield divided by fertilizer rate, and expressed as kilograms seed per kilogram N (Cowell and Doyle 1993). Yield and FUE data were analyzed by nonparametric statistics, using the Kruskal-Wallis test; treatment effects were considered significant at P 0.20 (SAS Institute, Inc. 1985). RESULTS AND DISCUSSION Growing season precipitation at the Conservation Learning Centre was above the long-term average in 1994, and was below average in 1995 and 1996 (Table 1). Although total precipitation received during the 1995 and 1996 growing seasons were similar, distribution was much more favorable for crop establishment and growth in 1996 than in Monthly mean temperatures generally were near normal for all 3 yr. However, the spring of 1996 was markedly cooler than normal. The VRF om treatments had higher FUE than CF or VRF rn treatments in all 3 yr (Tables 2 and 3). The FUE for VRF om were the same. This was expected, given that organic matter content usually is highly correlated with topography (Mulla 1993), as illustrated in Figs. 1 and 2. As well, the spatial pattern of thickness of the A horizon was similar to the spatial pattern of percent organic carbon (data not shown). Higher FUE for VRF om than CF or VRF rn were attributed to gains in efficiency in the U-slope position in all years and in the L-slope position in 1995 and Table 1. May to August precipitation and mean temperatures at the Conservation Learning Centre near Prince Albert, Saskatchewan from 1994 to 1996 Precipitation Temperature mm % z C % 1994 May June July August Total Avg May June July August Total Avg May June July August Total Avg z Percent of 30-yr mean from 1961 to 1990 (Source: Canadian Climate Normals : Prairie Provinces, Atmospheric Environment Service, Environment Canada, Catalogue No. En56-61/2-1993, Ottawa, ON) Crop responses to fertilizer N were the same among the treatments on M-slope areas. The proportion of area that the three slope positions occupied for each of the four fertilizer treatments was similar to that of the site as a whole. Reducing the yield goal (i.e., fertilizer recommendations) for the high-organic matter (VRF om ) and L-slope (VRF t ) zones in 1995 and 1996 from that used in 1994, when FUE was lower than that of the CF treatment, effectively compensated for Table 2. Comparison of flax, spring wheat and canola yields and FUE between VRF and CF at the Conservation Learning Centre near Prince Albert, Saskatchewan from 1994 to 1996 Yield (t ha 1 ) Fertilizer (kg N ha 1 ) FUE (kg seed kg 1 N ) CF z VRF rn VRF om VRF t CF VRF rn VRF om VRF t CF VRF rn VRF om VRF t Flax 1994 All slopes U slope M slope L slope Spring wheat 1995 All slopes U slope M slope L slope Canola 1996 All slopes U slope M slope L slope z Abbreviations: CF, conventional fertilization; L, lower-slope position; M, mid-slope position; U, upper-slope position; VRF rn, variable rate fertilization based on soil residual N; VRF om, based on organic matter; VRF t, based on topography.

5 BECKIE ET AL. VARIABLE RATE NITROGEN FERTILIZATION 593 Table 3. Probabilities (P > CHISQ) for flax (1994), spring wheat (1995) and canola (1996) yield and fertilizer use efficiency (FUE) z By fertilizer tmt: Flax 1994 Spring wheat 1995 Canola 1996 Yield FUE Yield FUE Yield FUE All slopes y CF x vs. VRF rn CF vs. VRF om CF vs. VRF t VRF rn vs. VRF om VRF rn vs. VRF t VRF om vs. VRF t U slope CF vs. VRF rn CF vs. VRF om CF vs. VRF t VRF rn vs. VRF om VRF rn vs. VRF t VRF om vs. VRF t M slope L slope CF vs. VRF rn CF vs. VRF om CF vs. VRF t VRF rn vs. VRF om VRF rn vs. VRF t VRF om vs. VRF t By slope position: CF L vs. M M vs. U L vs. U VRF rn L vs. M M vs. U L vs. U VRF om L vs. M M vs. U L vs. U VRF t L vs. M M vs. U L vs. U z Significant at P y Probabilities for pairwise treatment comparisons are not given when the Kruskal-Wallis value of among-treatment differences was not significant. x Abbreviations: CF, conventional fertilization; L, lower-slope position; M, mid-slope position; U, upper-slope position; VRF rn, variable rate fertilization based on soil residual N; VRF om, based on organic matter; VRF t, based on topography. the greater N mineralization of soils in these zones (Qian and Schoenau 1995). VRF rn had either a lower (1994) or similar FUE (1995 and 1996) compared with CF. Clearly, fertilization based on soil residual N management zones was not beneficial. This may reflect the fact that these zones can change markedly, both spatially and temporally, and may or may not have different yield potentials. Similar or greater amounts of fertilizer was applied in the U-slope areas compared with the L-slope areas of this treatment, according to the relative levels of soil residual N. In contrast, the least amount of fertilizer was applied in the U-slope areas of VRF om. Even in a wet year when water was not limiting to yield, FUE of flax in the U-slope position of VRF rn tended to be lower relative to the M- and L-slope positions. Thus, basing fertilizer management zones strictly on soil residual N levels, without taking into account the different crop yield potentials within a field, does not increase FUE and therefore

6 594 CANADIAN JOURNAL OF SOIL SCIENCE profitability relative to CF. For fields without significant variation in topography or organic matter, it is questionable whether VRF of N would be more efficient than CF. For the CF treatment, there was no consistent association between slope position and yield between years. In 1994, flax yields were highest on M-slope positions. Yields in L- slope positions were possibly reduced because of excess water, increased weed pressure, and/or greater N losses. In contrast, in 1995, which was a relatively dry year, wheat yields in L-slope positions were the highest. In 1996, canola yields tended to be highest on U slopes. These results indicate how factors other than nutrients, principally weather, can strongly influence yield response to N fertilization across the landscape from year to year and the difficulty in using yield maps to accurately construct fertilizer application maps. The results of this study suggest that varying fertilizer rates according to management zones based strictly on soil available N levels may not be beneficial. The FUE of VRF rn was the same or less than CF. The extra costs of grid soil sampling, mapping, etc. would make this VRF practice less profitable than CF. Instead, the results suggest that if a field has significant variation in topography and therefore organic matter, overall FUE can be increased by subdividing the field into management zones based on slope position and determining soil available N levels within these zones. A yield goal for the low-organic matter U-slope zones, as specified in prairie fertilizer recommendations for a growing season with below-normal probability of precipitation (Anonymous 1993), and yield goals for the other management zones based on a growing season with normal probability of precipitation, have been shown to be superior to using a uniform yield goal. Image analysis and benchmark sampling, although not performed in this study, may enhance the cost-effectiveness of VRF at the field scale. Slope position within a field can be determined relatively quickly, reliably and cost-effectively using image analysis of scanned black and white aerial photographs (McCann et al. 1996). Sampling using benchmarks representative areas of each management zone may be a viable alternative to grid soil sampling and would reduce time and costs associated with sampling and testing (Keyes and Gillund 1995). Based on the enhanced FUE of VRF, gross returns were about $60 ha 1 more than that of CF, by tailoring fertilizer rates according to crop yield potential across the landscape within the study area (using average prices for flax, wheat and canola of $310 t 1, $180 t 1 and $400 t 1, respectively). However, net returns that were calculated as crop revenue minus fertilizer cost (fertilizer price of $815 t 1 N), were only about $10 ha 1 more than that of CF. Fertilizer requirements are highest in the subhumid Parkland region of the prairies. This region accounts for 85% of the fertilizer N applied on the prairies, yet constitutes only 55% of the cultivated area (Harapiak et al. 1993). Therefore returns would be expected to be lower in the more arid regions of the prairies where less fertilizer N is required. The costs of varying fertilizer rates according to topography would be similar to CF, provided benchmark sampling can reflect soil residual N levels among slope positions with sufficient accuracy and fertilizer rates are manually adjusted within the field. Costs would rise with grid soil sampling, automatic fertilizer rate adjustment and using digitized nutrient application maps in conjunction with GPS or other positioning system. Three successive years of VRF in this study indicate that this practice can enhance the efficient use of fertilizer N, and has good potential to increase profitability of fertilizer use, by more closely matching fertilizer rates with crop nutrient requirements. ACKNOWLEDGMENTS This study was supported through funding by Agriculture and Agri-Food Canada (AAFC). Technical assistance from Colleen Kirkham and Glenn Galloway, AAFC, Melfort; Patricia Flaten, Manager of the Conservation Learning Centre; and AAFC-PFRA, Melfort, is gratefully acknowledged. Appreciation is extended to the internal reviewers, Dr. C. A. Grant and S. A. Brandt, and to the anonymous reviewers for their constructive and helpful comments. Anonymous F.A.R.M. phase II user s guide: moisture/ target yield & economic analysis utility software version 2.0 Saskatchewan, Alberta & Manitoba. Plains Innovative Laboratory Services, Saskatoon, SK. Cowell, L. E. and Doyle, P. J Nitrogen use efficiency. Pages in D. A. Rennie, C. 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7 BECKIE ET AL. VARIABLE RATE NITROGEN FERTILIZATION 595 Nolan, S. C., Goddard, T. W., Heaney, D. J., Penney, D. C. and McKenzie, R. C Effects of fertilizer on yield at different soil landscape positions. Pages in P. C. Robert, R. H. Rust, and W. E. Larson, eds. Proc. Site-specific management for agricultural systems. ASA, CSSA, SSSA, Madison, WI. Pennock, D. J. and Anderson, D. W Landscape-scale effects of cultivation on soil quality and crop yields in the black soil zone. Pages in Proc. Soils and Crops Workshop. University of Saskatchewan, Saskatoon, SK. Pennock, D. J., Anderson, D. W. and de Jong, E Landscape-scale changes in indicators of soil quality due to cultivation in Saskatchewan, Canada. Geoderma 64: Qian, P. and Schoenau, J. J Assessing nitrogen mineralization from soil organic matter using anion exchange membranes. Fert. Res. 40: Sawyer, J. E Concepts of variable rate technology with considerations for fertilizer application. J. Prod. Agric. 7: SAS Institute, Inc SAS user s guide: Statistics, Verson 5 Edition. SAS Institute, Inc., Cary, NC. Stafford, J. V., Ambler, B., Lark, R. M. and Catt, J Mapping and interpreting the yield variation in cereal crops. Comput. Electron. Agric. 14: Winkleman, G. E Methods manual support services laboratory. Research Branch, Agriculture and Agri-Food Canada, Swift Current, SK. 222 pp.