Non-Peer ReviewedTechnological advances in variable rate application and global positioning systems (GPS) make site-specific management technically feasible. No longer do fertilizer and pesticides need to be applied uniformly across a field, they may be varied to match the soil productivity across the field. However, successful site-specific management depends on identifying management zones and the determining optimum input application rate for those zones Unfortunately, identifying management zones that are reasonably consistent from year to year has been the greatest problem to successful site-specific management. In this study we found that crop vigor measured with normalized difference vegetation index (NDVI) based on satellite (LANDSAT) remote sensing for two different years was successful for identifying management zones. Based on the NDVI, we divided a field near Shaunavon in southwestern Saskatchewan into two zones: zone 1 with low crop and vigor zone 2 with high crop vigor. The zones corresponded to differences in soils, particularly, soil moisture, but it would be too costly and impractical to try to delineate these zones from detailed soil sampling. Wheat yield and protein were more responsive to N in zone 1 than in zone 2. The optimal N fertilizer rate was 95 kg/ha for zone 1 and 75 kg/ha for zone 2. Satellite remote sensing is a cost-effective method to delineate management zones for site-specific management

Basnyat, P.

McConkey, B.G.

Meinert, L.B.

English

University of Saskatchewan Research Archive

Optimum Strategies for Mapping Management Zones UsingTemporal Remote Sensing InformationP. Basnyat1, B.G. McConkey1,  and L.B. Meinert21Semiarid Prairie Agricultural Research Centre, Swift Current,  Saskatchewan S9H 3X22Meinert Enterprises Ltd., Shaunavon SK S0N 2M0AbstractTechnological advances in variable rate application and global positioning systems (GPS) makesite-specific management technically feasible.  No longer do fertilizer and pesticides need to beapplied uniformly across a field, they may be varied to match the soil productivity across thefield. However, successful site-specific management  depends on identifying management zonesand the determining optimum input application rate for those zones  Unfortunately, identifyingmanagement zones that are reasonably consistent from year to year has been the greatest problemto successful site-specific management.  In this study we found that crop vigor measured withnormalized difference vegetation index (NDVI) based  on satellite (LANDSAT) remote sensingfor two different years was successful for identifying management zones. Based on the NDVI, wedivided a field near Shaunavon in southwestern Saskatchewan into two zones:  zone 1 with lowcrop and vigor zone 2 with high crop vigor. The zones corresponded to differences in soils,particularly, soil moisture, but it would be too costly and impractical to try to delineate thesezones from detailed soil sampling.  Wheat yield and protein were more responsive to N in zone 1than in zone 2.  The optimal N fertilizer rate was 95 kg/ha for zone 1 and 75 kg/ha for zone 2. Satellite remote sensing is a cost-effective method to delineate management zones forsite-specific management.IntroductionOne way of describing the interaction between the organism and its environment is to consider itas a system influenced by three factors: controlled; uncontrolled and noise. Controlled variationcomprises manipulation of inputs such as seed, nutrition, water or chemicals. These are adjustedby producers to increase the beneficial function of the plant. Uncontrolled variation describesmeasurable factors of known significance, such as incident radiation, temperature, rainfall or soilcondition. A third source of variation, noise, is indeterminate. Fourth factor which may haveinfluence in   plant growth is temporal variation  which reflect the interaction of spatial variationin soil condition with temporal variation of climate. Site specific management accepts that variability occurs within agriculture fields spatially as wellas temporally. Understanding the variability allows fields to be divided into relatively uniformunits, which can be managed using techniques such as variable-rate fertilization or spraying.Information related to the in-field nutrient variation is a critical step, both in identifying thevariability within the field and in providing appropriate fertilizer recommendations. ObjectivesThe primary objective of this study was to analyze in-field spatial and temporal variability forbetter understanding of field productivity using remote sensing information. The secondaryobjective was to identify factors affecting field productivity, and to develop a methodology tominimize the effect of temporal variability by identifying management zones.Materials and MethodsField StudyThe study site was near highway 13, 11 km NE of Shaunavon (NE10-09-18W3) covering 23 ha.(Figure 1) . Spring wheat (Triticum aesitivum L. ) were planted with uniform fertilization rate(40 kg of N/ha) before the start of the experimentation in 1997.  The experiment area wereplanted with spring wheat during the four years of the experiment. At the start of the experimentin 1997, the study area was divided into 25 plots (plot number assigned east to west from 0 to 24). Each plot inside the study area (bounded by white polygon (Figure 1) with  plot width 14.8meters) received different amount of nitrogen fertilizer in 1997, 1998, and  1999, but in  2000 itwas planted under uniform nitrogen fertilizer. The field topography was mapped using globalpositioning system(GPS) (Figure 1) .                                                                               Figure 1. Topography and Sample location of the Study area NDVIValueofInfraredBand Valueof dBandValueofInfraredBand Valueof dBand=−+ReReXx x S DS Dii=− −( . .). .24Maximum local relief is 29 meters and slopes ranging from 0 to 5 percent. Soils of the areaconsists of Amulet Association and Wymak Association developed in clay loam glacial till andsilty loessial over till  (Saskatchewan Soil Survey 1988). For most part, soils of the AmuletAssociation are expressed on shoulders and tops of the knolls, while soils of WymarkAssociation are expressed in the mid- and lower-slope positions. The calcareous soils on the topsof the knolls (Meinert 1996). During the experiment herbicides were applied as required.Weather conditions and grain yieldWater and N are the primary factors determining stubble crop yields in the Canadian prairie(Campbell et al. 1997). The health of a crop gives a fairly reasonable about the availability ofthese limiting factors with the assumption that other factors influencing the crop conditions areunder control. Hence, we have analyzed these factors in terms of their spatial as well as temporalvariability.  These variability were accounted for using the information extracted from satelliteremote sensing and by analyzing soil samples collected each year. Soil moisture was determinedgravimetrically 0-15, 15-30, 30-60, 60-90, 90 -120 cm increment and converted to volume andnitrate information were extracted from the soil samples collected during April-May beforeplanting and yield information were collected using a plot combine representing all landscapeposition.  These point observations were used to create surface covering the study area usingKriging interpolation (Goovaerts 1997).  The cell size of the each surface created were matchedto that of Landsat TM information.  Weather data came from Environment Canada weatherstation at Shaunavon.Management Zones, Crop Vigor, and Grain Yield Normalized difference vegetation index (NDVI)  were extracted using Landsat TM informationof the  growing season of the year 1996 (year before the start of the field experiment), 1997 firstyear of the experiment), 1999 (last year of the experiment) and year 2000 (year after the fieldexperiment). The NDVI is the difference of near-infrared and visible red reflectance valuesnormalized over total reflectance of the two channels (Lillesand and Kiefer 1994) as follows:                                                                                                                       (1)                                                                                                         To compare the temporal information, NDVI and grain yield information were normalized inorder to remove difference in scale among the variables (Stevenson et al. 2001) as follows:                                                                                                                                                                                                                                                                                                                                  (2)                                                                                                                                                                                                                                                                                                                                      Where:Xi = The transformed value.xi = The observation i.x    = The mean of the observations.S.D. = Standard DeviationThe temporal variation were analyzed by comparing management zones identified by the NDVI.The difference in management zones delineated using 1996 and 2000 information showed thevariation before and after the variable rate fertilizer treatment, whereas difference between  1997, and 1999  showed the the temporal variability due to climate and treatment effects. Based on theaverage of 1996 and 2000 NDVI information, optimal management zones were delineated.Normalized grain yield of 1997, 1999 and 1999 inside each management zones were compared tovalidate the management zones. Least significant difference tests were used to Determinedifferences in means of normalized grain yield and normalized grain protein for the year andmanagement zone effects.  Crop Response to N-fertilizer in each management zone The magnitude of the yield and protein response to the fertilizer treatments were obtained byanalyzing  pooled data from the year 1997, 1998,1999 and 2000. Equations were fitted torepresent the relationship between wheat yield or grain protein and N fertilizer applied.  Theequation fit to the yield-N supplied and protein-N used were used to derive the optimal N rate toproduce maximum yield and grain protein.  Separate N rate response curves were developed forthe two management zones.                                                                                                                     Results and DiscussionWeather conditions and grain yieldGrowing season precipitation are presented in the Table 1 along with maximum and averagegrain yield. Except for 1996, growing season precipitation during the study period were at aboutthirty years(1959-1987) average(172 mm)  or above average.  From the Table 1, it can also beseen that not only the amount of precipitation before or during the growing season that matters,but also the timing of in-season precipitation matching the important phenological events matterstoo. When overall precipitation (from harvesting of previous year crop to physiological maturityof current season crop) and grain yield of each year were compared, it was seen that overallcumulative high precipitation in a year does not necessarily mean high yield  In addition to thespring soil moisture availability, the amount of precipitation received up to the time of 4 to 5leaves stage(Table 1) may play an important role in determining grain yield levels. Sum of springsoil moisture and precipitation after seeding and up to the  4 to 5 leaves stage reveal therelationship between moisture and grain yield of 0.101 ton per hectare per centimeter of increasein soil moisture. Table 1. Relationship between Grain Yield and Moisture Level.                     Regression CoefficientsExplanatory Variable                 R2            Constant    ExplanatoryVariable       CumulativePrecipitation up to 10days after seeding0.51 1587* 7.96CumulativePrecipitation up to 38days after seeding0.76 373.24* 14.91*CumulativePrecipitation up to 115days after seeding0.55 3142* 6.48CumulativePrecipitation beforeseeding0.62 730.91* 4.33Spring Soil Moisture 0.73 -488* 15.78*Spring Soil Moisture+CumulativePrecipitation up to 38days after seeding0.98 -608.65* 10.07*   *Significant at 5%                                                                                                                                                                                                                          Figure2. Management Zones based on Spatio-Temporal Variation in Crop Vigor.Management Zones, Crop Vigor, and Grain Yield NDVI extracted using 1996 and 2000 Landsat TM data is presented in Figure 2. The vegetationindex were normalized using the relationship shown in the equation 2 for comparison. The dataare then grouped into two different classes. Area with low NDVI (average or below averagevalues) were grouped together as  “management zone 1" and high NDVI area (area with aboveaverage values) were assigned as “management zone  2". Individually, classification of 1996 datarevealed the spatial variability in relatively dry weather condition, and classification of 2000 datashowed the pattern in relatively wet weather condition. The variations in the spatial patternamong these two years were due to the past treatment effects (lag effect of the past fertilizerapplication) and climatic variation or other variations which were not accounted for. The finalmanagement zones were created using average of information from 1996 and year 2000. Whenthe management zones identified using individual year information were compared with the finalmanagement zones(average of 1996 and 2000), it was observed that only 9 percent to 25 percentof the total area are showing inconsistency in classification (Table 2).  Grain yield normalized (calculated  using equation 2 ) for each year of the study and theinteraction of the management zone is presented in Table 3. Grain yield were always lower in thezone 1 (below average) whereas zone 2 always yielded more (above average) except for the year1997. In 1997 average grain yield from each zone were near average. This may be due to the factthat in 1997 N rate varied from 0 to 112 kilogram per hectare and in-season precipitation wasnear long term average. Therefore, low vigor zone could match the productivity of high vigorzone. In the second year (1998) and third year (1999) of the variable rate trial,  the rate werechanged to 0, 44.8 and 89.6 kg per hectare. The change in N rate and variation in in-seasonprecipitation has resulted in lower yield from the zone 1 where as zone 2 has shownimprovement.   Crop Response to Variable N-fertilizerThe magnitude of the yield and protein response to the fertilizer treatments were obtained byanalyzing  pooled data from the year 1997, 1998,1999 and 2000. Figure 3 shows the grain yieldand grain protein response patterns to variable N-rate. response using pooled information .  It canbe seen from these figure that fertilizer response of grain yield can be described using quadraticequations, whereas the response of grain protein can be approximated with linear functions(Figure 3). The presence of a positive linear and a negative quadratic coefficient for N rate in thecase of grain yield regressions indicate that fertilization with N increases yields at a decreasingrate with each extra unit of N rate until a maximum yield increase is reached (Table 4), furtherincrease in N rate resulted in reduction in yield.           Table 2. Changes in the Coverage of Management Zones in Comparison to “Average” Condition        Year                                                 Change 1996 1997 1999 2000Decreased 7% 20% 19% 8%Same 91% 75% 75% 83%Increased 2%  5% 6% 9%Figure 3. Relationship between Normalized Grain Yield and Normalized Grain Protein and NFertilizer.   Table 3. Between Zone Spatial and Temporal Comparison of Grain Yield & Protein.      Year                                                        Zone 1997 1998 1999 2000GrainYield1 0.49a (0.23)Z 0.44a (0.18) 0.46a (0.22) 0.41a (0.15)2 0.50a (0.25) 0.62b (0.25) 0.60b (0.24) 0.71b (0.31)GrainProtein1  0.47a (0.10)Z 0.50a (0.11) 0.48a (0.11) 0.48a (0.11)2 0.55b (0.13) 0.51b (0.07) 0.48a (0.11) 0.48a (0.12)Z Standard deviations are presented in parenthesis.a and b Means followed by same letter within each column are not significantlydifference according to LSD(0.05). Table 4. Relationship between Grain Yield and Protein with Variable N fertilizerCoefficient of Explanatory VariableDependentVariableZone Constant CurrentFertilizerPreviousFertilizer(CurrentFertilizer)2NormalizedYield1 0.1731*a 0.00304*a 0.00328*a2 0.3874*a 0.00229*a 0.00039 aNormalizedYield1 0.313*a 0.00328*a2 0.408 a 0.00225*aNormalizedYield1 0.228*a 0.00761*a -0.00004*2 0.297*a 0.0075*a -0.00005*NormalizedProtein1 0.268* 0.00458*2 0.249* 0.00476*NormalizedProtein1 0.164* 0.0044* 0.00244*2 0.0939 0.0051* 0.00294*Spring SoilN1 43.62* 1.14*2 47.75* 0.77**Significant at 5 %.  a Coefficients of Zone 1 and Zone 2 are statistically not equal (at 5%)ConclusionsThe management units developed in this study, were based on the crop response (NDVI is usedas a proxy of this) and simple clustering process. T he variable rate fertilization is only applicableto fields with distinct area behaving differently due to variation in soil or topographicalproperties. The success of site-specific management rests on the ability to identify the area withdifferent crop response to added inputs which are manageable practically and cost of suchchanges in the management will have to be comparable to conventional practices. The beststrategy is to fertilize for a  yield goal balancing both quality and quantity, realizing that anyadditional nitrogen will result in either a yield increase, if additional water is available or aprotein response if yield requirements have already been satisfied.ReferenceCampbell, C.A., Selles, F., Zentner, R.P., McConkey, B.G., Brandt, B.G. and McKenzie R.C.1997. Regression model for predicting yield of hard red spring wheat grown on stubble in thesemiarid prairie. Can J. Plant Sci. 77: 43-52.Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford University Press. NewYork.483ppLillesand , T.M., and R.W. Kiefer.  1994. Remote sensing and image interpretation (thirdedition). John Wiley and Sons, Inc., New York, 750 p.  Meinert, B. L.  1996. Characterization of the Landscape using image analysis of panchromatic airphotos for a farm in southwest Saskatchewan for the purpose of precision farming. A master ofagriculture thesis. University of Saskatchewan, SaskatchewanSaskatchewan Soil Survey  1988. Preliminary soil map and report: rural municipality of bonecreek number 108. Saskatchewan Institute of Pedology and University of Saskatchewan,Saskatoon, Saskatchewan. Stevenson, F.C., J.D. Knight, O. Wendroth, C.van Kessel, and D.R. Nielsen 2001. A comparisonof two methods to predict the landscape-scale variation of crop yield. Soil and Tillage Research.58:163-181.

Optimum strategies for mapping management zones using temporal remote sensing information

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Optimum strategies for mapping management zones using temporal remote sensing information

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