Laboratory Exercise Using the Microcomputer to Determine Nutrient ...

A laboratory exercise using the microcomputer to determine nutrient recommendations and 1 least-costfertilizer blends 2Kenneth M. Oates, James Camberato, and M. J. Vepraskas ABSTRACT As microcomputers becomemoreaccessible, agricultural advisors are using themfor makingsoil managementdecisionssuch as the selection of rate andkind of fertilizer materials.Alaboratoryexercisefor an introductorysoil sciencecoursewasdesigned1) to familiarize students with the microcomputer, 2) to use the microcomputerto calculate nutrient recommendations, 3) to use the microcomputer to calculate the least-cost blend of fertilizer materials,and4) to illustratethe errorin the nutrient recommendation resulting fromincorrectly taken soil samples. Themicrocomputer exercise showed that a soil sampletakentoo deeplyresulted in an error for the lime andnutrientrecommendations. In addition, a soil sampletakenwithoutregardto soil variationresulted in an errorin the nutrientrequirement. Soil test results of each soil wereusedIo calculatethe nutrient recommendation for corn (Zea maysL.), tomato(Lycopersicon esculentumL.), and alfalfa (Medicagosativa L.). The nutrient recomendationswere then used to select the least-costfertilizer blendfroma list of several fertilizers. Finally, the valueof usingthe microcomputer for solving farm managementproblems was demonstratedthroughout the exercise. Additional index words: Computers, Fertilizer calculations, Demonstration, Economics.

AS

microcomputers and appropriate software becomes more accessible, agricultural advisors will increasingly use them for making management decisions; therefore, it is essential that agricultural education include student use of microcomputers to stay abreast of contemporary trends. Laboratory exercises with microcomputers can demonstrate practical solutions to complex problems that will aid on-farm decisions. With available software packages such exercises can be easily incorporated into an introductory soil science laboratory. One important soil management decision where microcomputerscan be used effectively is the selection of rate and kind of fertilizer materials for application to specific crops and soils. The first step in this process is the collection of representative soil samples. Results from an analytical laboratory are then used to make a recommendation for the amount of fertilizer supplied nutrient. The fertilizer material to be used is usually not specified because it is assumedthat the grower will have a preference from several fertilizers.

Of the steps involved in this process, the most important one is the selection of a correct soil sampling schemethat utilizes soil variability, previous cultivation, as well as cropping patterns and proper sampling techniques. An error made in sampling will carry through the whole process and cause an incorrect nutrient recommendation. The magnitude of this error can be easily demonstrated by using the microcomputer to compare recommendations based on correctly and incorrectly taken soil samples. In addition to soil sampling, the grower also has control of the fertilizer materials to be used. Usually the grower can choose amongseveral N, P, and K fertilizer sources to satisfy the nutrient requirements. Fertilizer materials vary in cost, and it maynot be readily apparent whichcombinationwill result in the least-cost blend. Use of the microcomputer demonstrates a quick, accurate method to determine which combination will result in the least-cost blend of materials. The purpose of this laboratory exercise was to show students how a microcomputer can be used to make practical farm management decisions. In previous laboratory exercises, students were taught to correctly sample a soil, to read soil test data and nutrient recommendations from a soil test report, and to calculate fertilizer rate using pocket-size calculators. Thus, the microcomputer was used to perform a greater number of similar calculations as well as calculations for multiple nutrient materials. The laboratory exercise had two objectives: 1) to illustrate the variation in nutrient recommendations between representative and nonrepresentative soil samples and 2) to use the microcomputer to calculate the nutrient requirement and the least-cost blend of fertilizer materials for meeting this requirement. METHODS An Apple It Plus microcomputerwith two disk drives was used for this laboratory exercise. Themicrocomputer was pro® (MFUS), an educagrammedwith Micro-FORM-U-SHARE tional product that can be obtained from the National Fertilizer Development Center of the TennesseeValley Authority. The MFUS programhas the capability of calculating nutrient application rates for various crops whengivensoil test results ’ JournalSeries No.8887of the NorthCarolinaAgric. Research Service,Raleigh. 2Visitingassistantprofessor,graduateteachingassistant, andassistant professor,respectively.Dep.of Soil Science,NorthCarolina State Univ.,Raleigh,NC27650.

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LABORATORY EXERCISE USING MICROCOMPUTER of plant nutrients. The programcan also calculate the minimumcost fertilizer blend whengiven the desired nutrient applicationrates and the grade and cost for each fertilizer material. Theprogramhas other capabilities that werenot used for this laboratoryexercise. ~argett (1973)providesa moredetailed discussiondescribingthe least-cost calculationtechnique of Micro-FORM-U-SHARE. Soil sampleswere taken from a 10 ha, intensively managed field located at the UpperCoastal Plain Research Station, RockyMount,NC.The field consisted of three soils: a Norfolk sandyloam(fine-loamy, siliceous, thermic TypicPaleudult), a Norfolk eroded phase, and a Goldsborosandy loam (fine-loamy, siliceous, thermic Aquic Paleudult). A soil samplewas taken for each soil type by compositingfifteen cores from the Aphorizon. In addition to these "correctly" taken soil samples,twosoil samplesweretaken "incorrectly". The first incorrect sampleconsisted of 15 cores composited from the wholefield without regard for soil variation. The secondincorrect samplefrom the Norfolk soil was taken too deeply and included someof the subsurface horizon mixedin with the Aphorizon. All soil samples were submitted to the AgronomicDivision, North Carolina Departmentof Agriculture, for analysis.

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Table 1. Soil test results for five different soil samples taken from a 10 ha field~ Number

1 2

3 4 5

Sample description

Norfolk series Ap horizon Norfolk series eroded phase Ap horizon Goldboro series Ap horizon Field composite Ap horizon Norfolk series Ap and B horizons

Organic matter

Buffer CEC acidity Soil index pH

P~" index

K~" index

Ca

Mg

%

cmol (p’) c

0.7

6.9

1.2

6.6

166+

104

55

20

0.1

7.8

0.8

6.6

044

92

54

30

1.4

8.6

1.8

6.0

166+

150

50

22

0.6

6.6

0.8

6.5

166+

106

54

25

0.3

6.5

1.0

6.2

166+

74

52

28

% of CEC

Index values below 25 are low, between 25 to 50 are medium, and above 50 are high. Results obtained from the AgronomicDivision, North Carolina Dep. of Agric.

Table 2. Fertilizer

materials and average prices from dealers near Raleigh, NC

Material

Grade

Cost

Urea Ammoniumnitrate Diammonium phosphate Ammoniumsulfate Ordinary superphosphate Concentrated superphosphate Muriate of potash Fertilizer mix#1 Fertilizer mix #2 Fertilizer mix#3 Fertilizer mix#4

464)-0 33.5-0-0 18-46-0 21-0-0-24S 0-20-0 0-46-0 0-0-62 8-8-24 10-10-10 0-9-27 4-8-12

25.88 24.77 30.56 12.11 17.62 22.58 19.82 27.86 19.26 20.35 19.28

S/ha

RESULTS Soil test levels varied for the five soil samples taken from the field (Table 1). A comparisonof the test results for samples 1 and 5 show the effect of taking a soil sample too deep. Sample 5 has some of the subsurface horizon mixed with the Ap whereas sample 1 was taken from the Ap horizon. It has been shown that the subsurface horizon is usually more acid and lower in soil K than surface horizons (Fitts and Hanway,1971). Therefore, it would be expected that mixing subsurface soil with the Ap would result in a lower soil test K and a lower soil pH. Indeed, sample 5 has a lower soil pHand a somewhat lower K index than sample 1. The difference in soil P was not detectable because the extremely high amount of P was beyond the range of the methods used. The results show, however, that even on a wellmanagedfield, a soil sample taken too deeply can result in incorrect soil test results. In addition to sampling too deeply, another common mistake is sampling a field without regard to soil type. Soil type is a first approximation for dividing a field into homogenous units (Peck and Melsted, 1973). composite sample from a field with more than one soil type can give results that do not represent any soil type. A comparison of the composite sample, sample 4, with samples 1, 2, and 3 shows the error that can occur. Sample 1 has about the same values as the composite; however, samples 2 and 3 have some different values. Sample 2 has a much lower P level than the composite and 3 has a lower soil pHthan the composite. This variation will affect the nutrient recommendation. The Micro-FORM-U-SHAREprogram, which allows users to change prices and to add soil test recommendations, was set up with the North Carolina soil test calibration curves and fertilizer prices. The first editing,

provided by Dr. Robert Dahle, Department of Economics and Business, North Carolina State University, was the addition of the North Carolina calibration curves used to predict nutrient requirements from soil test data (Hatfield and Hickey, 1981). The second edit change was the addition of fertilizer materials at current prices of nearby dealers (Table 2). With these additions the Micro FORM-U-SHARE program could then calculate the nutrient recommendation and least-cost fertilizer blend for North Carolina crops. Nutrient recommendations for corn (Zea mays L.), tomato (Lycopersicon esculentum L.), and alfalfa (Medicagosativa L.) were calculated for each soil sample (Table 3). The composite sample did not cause error in the nutrient recommendations for soil sample represented by sample 1. However,for all three crops, the composite underestimated the P needs of the soil represented by sample 2; furthermore, it underestimated the K requirement for two crops. For tomato and alfalfa the composite sample underestimated the lime requirement for sample 3. These underestimates of nutrient and lime needs maycontribute to reduced yield. In contrast, the difference in nutrient recommendations between sample 5 and 1 showed that sampling too deeply resulted in an overestimated lime requirement for all

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JOURNAL

OF

AGRONOMIC

EDUCATION

Table 3. Nutrient recommendations computed from the soil Recommendation from soil test data Sample

Crop

Lime

N

P20,

test

Difference between soil type and composite K~O

Lime

N

data Difference between Norfolk Ap and Norfolk Ap & B

P20,

K~O

Lime

N

P~O~

K~O

0 0 0

0 -44.9 0

0 0 0

1795

0

0

0

0 0 0

0 -134.6 0

0 -11.2 0

1795

0

0

44.9

0 - 11.2 0

1795

0

0

33.7

kg/ha l 2 3 4 5

Corn Corn Corn Corn Corn

0 0 0 0 1795

157 157 157 157 157

0 44.9 0 0 0

ll.2 11.2 11.2 11.2 11.2

0 0 0

1 2 3 4 5

Tomato Tomato Tomato Tomato Tomato

0 0 3365 0 1795

123.4 123.4 123.4 123.4 123.4

0 134.6 0 0 0

11.2 22.4 11.2 11.2 56.1

0 0 -3365

1 2 3 4 5

Alfalfa Alfalfa Alfalfa Alfalfa Alfalfa

0 0 3365 0 1795

0 0 0 0 0

0 56.1 0 0 0

11.2 22.4 11.2 11.2 44.9

0 0 - 3365

Table 4. Fertilizer Soil Crop sample

blends for corn, tomato, and alfalfa N Material

Grade

Rate

P Material Grade

kg/ha Corn Corn Corn Corn Tomato Tomato Tomato Tomato Alfalfa Alfalfa Alfalfa

1 2 2 3 1 2 2 3 1 2 3

464)-0 464)4) 464)-0 46-0-0 46-0-0 46--0-0 46--0-0 46-0-0 ........ .... ........

341 303 341 341 267 152 267 267

Rate

0-46-0

96 96

292 292

121

........ ........ 0 0 0

0 - 56.1 0

........ ........

for three soils

Table 5. Student response to questions assessing laboratory exercise.

K Material Grade

Rate

kg/ha .... 18-46-0 0-46-0 .... .... 18~6-0 0--46-0 ....

........ ........

04)-62 0-0-62 0--0-62 0-0-62 0-0-62 04)-62 0-0-62 0-0-62 0-0-62 0-0-62 0-0-62

Response~

Cost

kg/ha

S/ha

18 18 18 18 18 36 36 18 18 36 18

91.82 111.33 113.95 91.82 72.99 135.97 142.65 72.99 3.57 34.46 3.57

three crops, and an overestimated K requirement for tomatoes and alfalfa. The overestimate for K wouldresult in unnecessaryextra expense, while the overestimate for lime wouldnot only result in extra expense, but also mayresult in a high soil pH. This high soil pHmightreduce the availability of other nutrients. In summary, these comparisonsillustrate hownonrepresentative soil samples can overestimate or underestimate the nutrient and lime requirements. After the nutrient recommendationswere calculated, they were used to quickly obtain the least-cost fertilizer blend. As an example, the nutrient recommendations for a tomato crop were used to calculate the fertilizer blends for the three soil types representedby samples1, 2 and 3 (Table 4). Theleast-cost blend for samples1 and 3 was a mix of urea and potash. However,the least-cost blend for sample 2 was a mix of urea, diammonium phosphate, and muriate of potash. Whennutrients other than N, P, and K are required, the choice of materials becomes more complex, and use of a microcomputer wouldbe valuable for quickly determining leastcost fertilizer blends. DISCUSSION To effectively complete this two hour exercise for laboratory sections of 10 to 25 students, several teaching

the

Question

Yes

No

I. Have you ever used a microcomputer? a. for games b. for work c. for home 2. Wasthe laboratory exercise beneficial in helping you use a microcomputer? 3. Do you expect to use a microcomputerin the next few years? 4. Before the exercise, did you think the microcomputer wasdifficult to use? 5. After the exercise, did you think the microcomputer wasdifficult to use?

74 66 32 l0

26 34 68 90

77

23

81

19

55

45

20

80

Percentagesare basedon a total of 116 students.

aids were needed. Before class students were given a written brief that described general computer terminology, field locations and soil sampling schemes, and the soil test results. Worksheetscorrespondingto Tables 3 and4 weregiven to the students at the beginningof the period and were completed in class. Because there was not enoughtime to teach programming skills, a step-bystep flow diagram was given to student so that they could gain some "handson" experience. Finally, to accommodate the large class size, the microcomputeroutput was displayed on a 48 cm(19-in) television monitor as well as the 20 cm(8-in) console screen of the Apple microcomputer. Student response to this exercise was positive (Table 5). Most students (7407o) had some previous exposure a microcomputer while 2607o had not used a microcomputer in any capacity. Regardless of previous experience, student interest was high because most students (81%) expect to be using a microcomputerin the next few years. The exercise was successful in changing the student’s attitude toward using a microcomputer. Before the exercise, a majority of the students (5507o) felt that a microcomputer wasdifficult to use. After the exercise, only 2007oof the students felt that the microcomputerwas difficult to use. However,the fact that

CROPROD IN AGRONOMY COURSES some students still felt that a microcomputer was difficult to use suggests that additional exercises utilizing the microcomputer are needed. In summary, this laboratory exercise demonstrated the error in nutrient recommendations caused by incorrect soil sampling, and it illustrated that the least-cost fertilizer blend can vary depending on the nutrient recommendations. Also, this exercise utilized the microcomputer for solving practical problems of the farm manager. It was not the intent of this laboratory exercise to train students as computer programmers but rather to demonstrate how a microcomputer and soil test information can be used to make soil management decisions. It is hoped that this experience will encourage students who may become future farmers and fertilizer dealers to use microcomputer technology to aid with sound economic decisions.

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