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Agricultural drought impact evaluation model: Description of components
Agricultural Systems 30 (1989) 117-138
Agricultural Drought Impact Evaluation Model: Description of Components*
K. K. Klein Department of Economics, University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
S. N. Kulshreshtha Department of Agricultural Economics, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 0W0
& S. A. Klein Medicine Hat College, Brooks, Alberta, Canada T0J 0J0 (Received 8 January 1988; revised version received 8 August 1988; accepted 26 September 1988)
ABSTRACT Various components of the Agricultural Drought Impact Evaluation Model are described in this paper, which is a secondpaper in a series of three. Various components describe methodology to estimate yields of various cereal grain and forage crops under drought conditions; to simulate the effects of the drought on economic andfinancial performance of thefarm firm; to aggregate micro level results into regional (or provincial) level entities; to estimate secondary impacts of the drought both on economic activity and employment levels. * Financial support received from the Prairie Farm Rehabilitation Administration is
gratefully acknowledged. Authors are thankful to Mr George Pearson, Murray Jones, Ray Fautley, and Ted O'Brian for assistance received during the course of the study. 117 Agricultural Systems 0308-521X/89/$03.50 © 1989 Elsevier Science Publishers Ltd, England. Printed in Great Britain
118
K. K. Kle&, S. N. Kulshreshtha, S. A. Klein
INTRODUCTION In the first paper in this series (Kulshreshtha & Klein, 1988), the methodology for developing an agricultural drought impact evaluation m o d e l - - A D I E M - - w a s described. This model has four components--yieldhydrology simulator, a set of farm business simulation models, an inputoutput model, and an employment model. Each of these components is described in detail in this paper.
Y I E L D - H Y D R O L O G Y SIMULATOR Previous studies on estimating a relationship between yield and amount of moisture available to plants have varied from simple weather index formulation (Stalling, 1958), to more sophisticated measurement of climatic variables, soil properties, and management practices. In this study the relationship between yields of a given crop at a specific location and moisture stress at that location was estimated. The level of soil moisture stress was measured at five key stages of plant growth: seeding to emergence, emergence to jointing, jointing to heading, heading to soft kernel development, and hardening of the kernels. The soil moisture at each of these stages was derived using meteorological data, date of seeding, and the Versatile Soil Moisture Budget (VSMB) model. (For more details of the Versatile Soil Moisture Budget Model, see Kraft, 1982.) The VSMB model is a computerized mathematical model which estimates changes in soil moisture resulting from net evapotranspiration. The level of soil moisture is calculated each day by subtracting the moisture withdrawn and adding the daily precipitation to that available initially. Since the water retaining capacity of different types of soils is different, the VSMB model was run individually for three soil textures: coarse, medium, and fine. (Coarse soils have a holding capacity of 125-150 mm of moisture; medium soils have between 200 and 225 mm holding capacity; and the fine soils have a holding capacity of over 280 mm.) Two types of yield prediction models were developed: one, for cereal crops, and the other for forage crops. The latter type of model was also used for predicting the impact of the drought on quality of pastures. The cereal crop yield model was estimated for five major crops: wheat, oats, barley, canola, and flaxseed. In general, the relationship was estimated as: k __ YDrt - f ( T ~ k, FRT~tk , SMut)
(1)
Agricultural drought impact evaluation model
119
where: k YDrtyield per
acre during period t, in location r, for the k th crop. = index of technological change for crop k, during year t. FR~, = level of fertilizer for crop k, in location r, during year t. SMrt = level of soil moisture stress during year t in region r. Since data used in the estimation of the yield were of a time series nature, they needed adjustments for variations other than weather in order to determine the magnitude of the weather influence. The non-weather related variables were: technological change as approximated by an index of varietal yield improvement, and rate of application of fertilizer. The first variable was calculated by weighting the relative yield of different varieties of that crop by their share of total acreage. (The yield of one dominating variety was taken as one, and the others were normalized against that base. These normalized yields were subsequently multiplied by the share of each variety's total acreage under that crop.) Fertilizer application rates were those that were recommended to farmers in different regions during that year. Although the model has the capability of estimating the crop yields given available moisture at various stages of plant growth, this procedure can be by-passed for any ex post drought impact evaluation. The reason for this is the fact that yields are already known and therefore need not be estimated in a manner described above.
FARM SIMULATION MODELS The key to estimating the cost of drought is to measure its financial effects on the individual farm operator. It must be determined how seriously the drought affects the farmer's level of well being, as measured by such indicators as net income, net worth and cash flow. The financial incentives for adopting specific production and management strategies that reduce the impact of drought on the individual farm must be evaluated and understood. The farm simulation models were developed quantitatively to measure the consequences of drought and drought-mitigating strategies at the farm level. These models are comprehensive enough to deal with production of all major agricultural commodities in the province. They also consider the time effects of production and the lingering effects of drought on production in the ensuing years. They have the capacity to provide a full financial analysis of a given farm situation, including calculations of net worth, net farm income, receipts and costs by enterprise, cash flow analysis, changes in debts,
120
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
value of different assets, family living expenditures, and income tax payments. Four simulation models were developed or modified in this study: crops, beef-forage-grain, hog-grain, and dairy-forage-grain. The first three are modified versions of farm-level models described in detail in Zentner et al. ( 1978), Klein & Sonntag (1982) and Sonntag ( 1971), respectively. The fourth model is described in Klein & Klein (1983). Though very complex in detail, the simulation models proceed through a series of steps not unlike the decision process used by any farm operator-decision maker. Every farm operator must choose among many alternative methods of production, e.g. rotation, crops to plant, number of pre-seed tillage operations, machines to use, planting time, fertilization rate, herbicide use, and other jobs that require different combinations of resources. The manager must ensure that adequate resources (machines, buildings, labor, cash) are available to carry out the production plan. And it is hoped that wise choices are made so that suitable rewards are obtained from the farming operation. The simulation models mirror this decision process. They develop complete budgets for a specific production plan for periods up to ten years in length. The models are computerized. This permits rapid calculations and comparisons of dozens of production plans, if desired. The simulation models can compare budgets for a wide array of production and management strategies for a farm. They incorporate literally millions of combinations of production and management strategies for a representative farm and have the facility to substitute or add newly developed production technologies. The farm level models have the capacity to differentiate among good and bad growing years and incorporate probabilistic estimates of their frequencies. They reflect closely the actual production and management decisions on the farm, e.g. integration of enterprises, multi-year planning horizons, 'lumpy' investments, and institutional constraints like income tax. Of major importance in this study, the simulation models generate an economic analysis consisting of: a. b. c. d.
projected net income for each of the production plans considered, projected net worth for each of the production plans, riskiness (variability in net income, net worth, cash flow) of selected production plans over time, additional resources required to accomplish selected production plans.
The economic analysis generated by the simulation models permits discovery of the best time path available for changes in the organization of the representative farm's resources.
Agricultural drought impact evaluation model
121
Major steps in the operation of each of the farm business simulation models are described in the flowchart of Fig. 1. Two important cycles are prominent in each model's operation. An outside cycle permits the model to evaluate a number of production plans for the representative farm and determine the 'best' one. Best refers to highest terminal net worth among the production plans evaluated. There is no guarantee in the simulation models that a better production plan could not be found. A probabilistic statement about how close the best plan may be to the true optimum is all that can be made. The second or inside cycle revolves over the number of years specified by the user of the model in the input form. Each of the simulation models can be run for a period of one to ten years. Each of the steps in Fig. 1 is done for each year of the simulation (inside cycle) and for each of the production plans to be evaluated (outside cycle). Thus, a single run of the model could evaluate and compare dozens of production plans for a single representative farm over a time period up to 10years in length.
Selection of representative farms Twenty-six representative farms were developed to model production of the major agricultural commodities in the major soil zones of Saskatchewan: nine crop farms, twelve beef cattle farms, two dairy farms, and three hog farms. Representative farms in all categories were assumed to have the same characteristics of tenure as reported in the Agricultural Census 1981 by Statistics Canada: 1. 2. 3.
brown soil zone--63% cropland owned, 27% rented, dark brown soil zone--70% cropland owned, 30% rented. black soil zone--73% cropland owned, 27% rented.
Machines and machine sizes were selected for these farms on the basis of discussion with extension workers in Saskatchewan and from previous studies (unpublished) conducted by Agriculture Canada. Farm assets and debts for each representative farm were based on survey results of Farm Credit Corporation. The following sections summarize the major characteristics of representative farms used in this study. Complete details on each farm are available in the Technical Appendix to Klein & Klein (1983).
Cropfarms Three representative farms were chosen to represent crop farms in each of the major soil zones that characterize the main grain growing area of
122
K. K. Klein, S. N. Kulshreshtha, S. A. Klein T
Buy or rent additional inputs
( START ) Read input form --set key variables --set parameters on run
Prepare one-year L budget for production plan ["
Modify base data
.I-]
Print annual tables, if desired
Begin cycle over number of production plans
I
Store identification of plan and value of objective funcUon
Select production plan from data provided m input form
Print summary tables, if desired
Begin cycle over number of years No Compute job matrices
Ves
~ Print output tables for 'best' or only production plan
Account for joint use of inputs
( Compute input requirements for production plan
END )
h Fig. 1. Flowchart for simulation model.
Determine production plan that is 'best' and regenerate it
Agricultural drought impact evaluation model
123
Saskatchewan (Fig. 2). The representative farms included a small, medium and large farm in each of the three soil zones. The resource bases represented by each of the representative farms are shown in Table 1. (The size categories of each of the representative crop farms were determined from data obtained from Statistics Canada and Saskatchewan Agriculture (1979).) Farms in the dark brown soil zone had higher assets and debts than did farms in the other two soil zones. The proportion of summerfallow on actual farms in Saskatchewan has varied between 0.40 and 0"45 in recent years. However, actual farms typically
UNCULTIVATABLE REGION
BLACK SOIL DARK BROWN SOIL
BROWN SOIL
Fig. 2.
Soil zones of Saskatchewan.
124
K. K. Klein, S. N. Kulshreshtha, S. A. Klein TABLE 1 Representative Cereal Farms, in Saskatchewan, Selected Characteristics, 1980 Farm size Small
Medium
Large
Brown soil zone Average Acres Farmland Cropland Assets ('000 $) Debts ('000 $)
477 415 161.6 27.8
1 029 896 288"3 49.9
2 465 2 146 679"8 118-1
Dark brown soil zone Average Acres Farmland Cropland Assets ('000 $) Debts ('000 $)
457 407 207.1 35.1
1 139 1 015 459"4 79.2
1 855 1 654 812'6 127.7
958 795 316"5 54.6
2 204 1 829 625.6 108.5
Black soil zone Average Acres Farmland Cropland Assets ('000 $) Debts ('000 $)
425 353 152'4 26.1
use rotations that include one-half, one-third, or one-quarter summerfallow. Individual representative farms therefore had to be given conventional rotations that in the aggregate would produce the required proportion of summerfallow. All representative crop farms in the brown soil zone were given a one-half summerfallow, one-half crop rotation. The small and large representative farms in the dark brown soil zone were also given a one-half crop, one-half summerfallow rotation. The medium-sized representative farm in the dark brown soil zone was given a two-thirds crop, one-third summerfallow rotation. All representative farms in the black soil zone used a one-third summerfallow, two-thirds crop rotation. Since the livestock models (beef-forage-grain, hog-grain, and dairyforage-grain) contained land area for feed grain production, only wheat, canola, and flax were grown on the representative crop farms. In aggregate, this accounted for the entire area of major crop production in Saskatchewan in the base year of 1980. Field operations were begun in early May for farms in the brown and dark brown soil zones; they were started two weeks later for farms in the black soil
Agricultural drought impact evaluation model
125
zone. Two pre-seed tillage operations were performed on farms in the brown and dark brown zones; an extra operation was performed on farms in the black zone. Pre-emergent weed control with chemical spray was used in the spring. Both wild oats and broadleaf seeds were controlled with herbicides. Fertilizer was applied at a rate of 5 lbs N and 20 lbs P205 per acre on summerfallow and 201bs N with 201bs P205 per acre on stubble crops. These correspond to average application rates used by farmers in Saskatchewan in 1980 (Saskatchewan Agriculture, 1979, 1981). Four tillage operations that used a cultivator and rod weeder were simulated for summerfallow area of representative farms in the brown soil zone. One extra tillage operation was used on farms in the dark brown zone; two extra operations were performed on farms in the black zone. No postharvest tillage operations were done. Seed was purchased from off-farm sources. No crop insurance was carried on either cereal or oilseed crops but premiums for the Western Grain Stabilization Program were paid. Machines were replaced when they reached 66% of their maximum useful life. Planting and harvesting operations were assumed to differ by size of farm and by soil zone. In the brown soil zone, small and medium-sized farms planted crops with a discer; the large farm used a hoe drill for planting operations. In the dark brown soil zone, the small farm planted with a discer, the two larger sized farms used a hoe drill. In the black soil zone, all farms had a press drill. Discer seeding was assumed to be used for all stubble crops. Most farms had a power take-offswather; however, medium and large farms in the black zone had a self-propelled swather. All farms in the black zone plus the large farm in the brown zone had self-propelled combines; the other farms had power take-off combines.
Beef cattle farms Twelve farms were chosen to represent different types of beef-grain farms in the province: four 'mixed', four cow-calf, and four feeder farms. The four mixed farms were identified by different ratios of cattle to grain enterprise in two zones (brown-dark brown and black soil zones): 1. 2. 3. 4.
brown~tark brown soil--low grain, high cattle (LG-HC); brown-dark brown soil--high grain, low cattle (HG-LC); black soil-low grain, high cattle (LG-HC); and black soil-high grain, low cattle (HG-LC).
The type of beef enterprise used on these mixed farms was cow-calf, where weaned calves are maintained over the winter, and are sold in the spring as year-old stockers.
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
126
Four farms Saskatchewan: 1. 2. 3. 4.
were constructed
to
represent
cow-calf farms
in
brown-dark brown soil--small (69 cows, 369 crop acres); brown-dark brown soil--large (137 cows, 732 crop acres); black soil-small (40 cows, 213 crop acres); and black soil-large (59 cows, 315 crop acres).
The type of beef enterprise on the cow-calf farms was raising and selling of weaned calves. Only the breeding herd and replacements were kept over the winter season. Four farms were selected to represent feeder farms in Saskatchewan: 1. 2. 3. 4.
small unfinished feeder--70 cow herd and 353 acres of cropland; large unfinished feeder--144 cows and 440 acres of cropland; small finished feeder--70 cows and 343 acres of cropland; and large finished feeder--144 cows, 40 purchased feeders, and 446 acres of cropland.
The unfinished feeder farms produced and sold long yearling feeders, i.e. offspring are maintained over the winter, placed on pasture for the summer and sold as unfinished feeders off-pasture. The other two representative farms produced long yearling feeders as well but finished them to slaughter weight on their own farms. The overall resource base of these representative farms is documented in Table 2. No community pasture was made available to mixed farms but it was available for 34% of the cow-calf units on cow-calf farms. (This is consistent with available level of cattle on Prairie Farm Rehabilitation Administration pastures, provincial pastures, private leases and grazing association pastures.) Therefore, only 66% of the cattle had to be pastured on land resources available to the representative cow-calf farms. To account for available community pasture, 30 cow-calf units for each of the small feeder farms and 61 cow-calf units for each of the large feeder farms was made available. The number of cows per feeder farm placed on community pastures was proportionately greater than for the representative cow--calf farms. This was to allow for the estimated 62 000 yearlings on community pastures in Saskatchewan in 1980. The beef-forage-grain simulation model (Klein & Sonntag, 1982) permits only cow-calf units to be placed on outside grazing sources. Therefore, the total number of feeders on community pastures was converted to cow-calf units at the rate of two feeders per cow-calf unit. This meant that 34% of the cow herd on these farms could graze on off-farm pasture.
310 85 500 50 335-0 49.9
1 000
a Low-grain acres, high cattle numbers. b High-grain acres, low cattle numbers.
Average Acres Farmland Improved Cropland Tame Hay and Pasture Unimproved Average Cows Assets ('000 $) Debts ('000 $) 739 215 319 27 446-6 66.5
1 280
LG-HC ~ H G - - L C
Brown, dark brown
Black
176 44 265 28 237.9 35.5
488 723 194 169 18 479.2 71.4
1 099
2 728
369 732 156 311 828 1 644 69 137 384-3 770-0 57-3 114-7
1 374
Large
213 92 480 40 265.8 39.6
796
315 133 708 59 355.2 52-9
1 174
Large
Black
Small
Cow-calf Brown, dark brown
L G - H C ~ H G - L C ~ Small
Mixed
TABLE 2 Representative Cattle Farms in Saskatchewan, Selected Characteristics, 1980
2054
Large
353 440 108 139 714 1 468 70 144 374.6 631.8 55-8 94-1
1 184
Small
Unfinished
2061
Large
343 446 105 136 714 1468 70 144 415-2 684.3 61.9 101.9
1 170
Small
Finished
Feeder farms
7_,
e~
e5
128
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
Cropping rotations were one-third fallow, two-thirds crop on each of these representative beef-producing farms. It was assumed that feed grain was grown on all land not used for cereal forage. Fertilizer and chemical herbicides were applied on all crops.
Dairy farms Representative dairy farms were developed for fluid milk producers only. Cream and manufactured milk production were not included in these representative farms. The two farms chosen to represent fluid milk production were: 1. 2.
small--30 cows with 205 acres of cropland, and large 48 cows with 324 acres of cropland.
The resource base for each of the representative farms is in Table 3. A typical dairy herd has 83% of the cows lactating and 17% of cows dry at any point in time (based on a 305 day lactation period). An average farm contains 4% high producing cows (31.8kg milk per day), 30% medium producing cows (23.6 kg milk per day), and 66% low producing cows (16.8 kg milk per day). These data were incorporated into each of the representative farms.
Hog farms Three farms were chosen to represent hog farms in the province (Table 4): (1) Small--10 sows producing one litter per year (80 hogs) with 328 acres of cropland,
TABLE 3 Representative Dairy Farms in Saskatchewan, Selected Characteristics, 1980 Farm
Average Acres Farmland Improved Cropland Tame Hay and Pasture Unimproved Average Cows Assets ('000 $) Debts C000 $)
Small
Large
397 205 72 111 30 459.5 60.6
629 324 115 178 48 556.2 73.4
Agricultural drought impact evaluation model
129
TABLE 4 Hog Farms in Saskatchewan, Selected Characteristics, 1980
Farm size
Average Acres Farmland Improved Cropland Average Hogs Assets ('000 $) Debts ('000 $)
Small
Medium
Large
422 328 80 154.6 59.6
557 435 192 205.5 79.3
960 748 900 421-3 162.5
(2) Medium--16 sows producing an average of 1.5 litters per year (191 hogs) with 435 acres of cropland, and (3) Large--60 sows producing two litters per year (900 hogs) with 748 acres of cropland. All representative farms were modelled as farrow-to-finish types of hog enterprise. The farrowing schedules were altered among representative farms to provide a somewhat even flow of hogs to market. Hogs were marketed at 190 lbs liveweight. The one-third fallow, two-thirds crop rotation was used on these representative farms. The only crop grown on these representative farms was barley.
Drought adjustments The farm business simulation models were programmed to permit analysis of farm-level adjustments during a drought period. Most of these adjustments were related to agronomic and cultural practices, or management decisions typical during a drought year. They included advancing the starting date of spring field operations (to gain a yield advantage), reducing summerfallow tillage operations (to conserve moisture), chemical summerfallow (to conserve moisture and prevent soil erosion), reducing purchases of depreciable assets (to assist cash flow), early weaning of calves (to conserve fodder), and heavier culling of cows (to save forage). In total, over 50 possible farm production adjustments were included in the farm business simulation models. The drought adjustment strategies are under the control of the user of the simulation models. When invoked by the user, they provide for a comparative financial and economic analysis of the benefits of such actions.
130
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
They do not predict what farmers will do; they provide for an evaluation of the consequences of particular strategies. Aggregation
o f f a r m results
To permit regional assessments of the impacts of a drought and droughtmitigating strategies, coefficients were developed to aggregate the output from farm level models. The procedure consisted of estimating the numbers of each type of representative farm (Distribution of farms by type of farm and regions within Saskatchewan is shown in Appendix A.) in each of four economic zones in the province (Fig. 3). If a drought situation was to affect yields on cereal farms in Region I (southeast), the effects on six representative farms would be evaluated. The changes in financial and physical variables on the small representative farm in the dark brown soil zone would be multiplied by the estimated 2409 actual farms that were represented. These results would be added to changes that occurred on the 3303 medium-sized representative farms, and so on.
18
17 16
14
Is IIII 13 12 11
.
(
"
I
,
\
3
10
I
.
I
j
2
5
I
'
Fig. 3. Regionalboundaries for sub-provincial input-output tables.
Agricultural drought impact evaluation model
131
This procedure allows for differential drought adjustment strategies on different sizes of farms within and among soil zones in the same region in the province. It also greatly increases the effective number of representative farms. The small representative farm in the dark brown soil zone represents farms in three different regions in the province. Drought conditions of different levels of severity could be simulated for each of the regions. That particular representative farm then effectively becomes three representative farms. The aggregation of farm level economic indicators into regional or provincial level indicators assumes that farmers are facing a perfectly elastic demand for their product. Thus, change in product prices as a result of a local drought would be minimal, if at all. (This assumption is tenable in the light of the fact that most grain markets are international in scope; market prices are established through an interplay of international supply and demand.) Similarly, it is also stipulated that, due to relatively small changes in feedgrains prices, changes in the supply of livestock would also be minimal.
I N P U T - O U T P U T MODEL An input-output (I-O) model portrays the interdependence of one economic sector on other economic sectors, or on forces outside the region. The economy is broken into several industries (also called sectors) which both produce and purchase several products (also called commodities). Besides the industries, the economy contains consumers and various levels of government, each one of which purchases goods and services (or commodities) produced by industries. Since no region is totally selfsufficient, imports are allowed into the region to cover any shortfall, and similarly excess production (over and above local demand) is disposed as exports. The I-O models adapted for the ADIEM model contained 12 major sectors and 72 commodities. This commodity-by-industry framework, known as a rectangular input-output model, is an improvement over the traditional square I-O models. An I-O model is based on two assumptions: one, constant and fixed proportions of inputs are used in the production of various commodities by an industry, and two, if output of an industry changes, level of production of all commodities increases proportionately. The output of industries and commodities are related in the following manner: Q = U+ F
(2)
132
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
where, Q is a vector of commodity output U is a 12 × 12 matrix of intermediate demand, and F is a vector of final demand. The two assumptions of rectangular I-O models are written as: U = BG
(3)
where, B is the technology matrix of 72 × 12 dimension, and G is a vector of commodity output. Intermediate demands for various inputs are derived from level ofindustry's output (G), and the production function, as represented by the B matrix. The second assumption dictates that: G = DQ
(4)
where, D is a 12 x 72 matrix of market share coefficients. Substituting eqn (2) into (4) and solving for G: G = D [ U + F]
(5)
Substituting eqn (3) into (5): G = DEDG + F]
(5.1)
G = (DB)G + (DG)
(5.2)
or
Equation (5.2) states that total output of an industry is split into the portion demanded by other industries as intermediate inputs (i.e. I-DB]G), and the portion destined for final use (i.e. DF). This equation represents the transactions table. An example of a transactions table is shown in Table 5. The equation can be solved for G, as shown in eqn (6). G = ( I - D B ) - 1(OF)
(6)
In other words, industry output levels can be estimated by a multiplier m a t r i x - - ( I - D B ) -~ and industry's final demand--DF. Thus, if final
demand for a sector changes, by a process of'ripple effects', all other sectors would be affected. The model can be used for estimating impacts of a drought in the following manner. The model is converted into a regional input-output model by using the method of location quotient. (For details of this method, see Miller & Blair (1985) pp. 296-9. The method is viewed as a measure of the ability of regional industry i to supply the demands placed upon it by other industries in the region and by regional final demand. If the location quotient (LQ) is less than one, the industry is less capable of satisfying
232.56 475-93
562.64
998-00 913.00 496.79 -6-95 -7.48
2956-00
Sub-total
G D P households G D P other Imports Comm. tax subsidy Residual
Total inputs
1318.70 951.60
523.00 226.01 479.00 207.01 67.69 26.36 18.77 20.97 -2.32 -4.68
0-06 0.18 26-18 42.87 3.09 0-11 0.00 0.00 0-40 0.02 12.53 1.72 18.82 26-03 26.77 27.29 6.89 0'68 19.86 3.91 57.98 351.70 59.99 21.42
48.40 872.90
13-00 90-00 309-00 11.00 82.00 283-00 2.26 118-33 484.16 1.85 4.66 - 5 4 . 2 4 -0.16 -5.90 -5.82
20.45 583.82 501.70
0.10 393.85 1-58 0.02 0-30 68-99 0.01 0-03 25.88 2.56 0.00 22.31 0.00 62.90 5-58 2.80 7.27 132.02 0.11 2.36 18-65 0.14 7.33 25.53 0.66 13-67 14.03 6.29 45.84 75.18 1.93 2.00 8-96 5.84 48.28 103.00
0-06 35-45 1.15 0-71 1-06 1.47 28.84 1.20 2-53 0.00 0.01 0"03 0.01 0-32 0.24 2.46 20-79 44-95 81.82 9-89 90-92 14-66 25.29 13.98 1.31 9.27 20.06 23.59 95.29 166-38 5.82 36-05 15.04 21.59 186-33 114.20
593.00 492.00 543.00 451-00 109.23 78.31 60-90 23.39 -3-97 -3'85
16.63 11.15 0-60 0.75 120.38 129.58 5.66 15-67 150.17 268.59 34.24 480.97
71.39 6.30 3.20 4-80 75-19 184.68 345.56 156.48 860.50 372.56 111.79 777.21
2385-39 811-02 872.57 17.22 564.26 549.77 1318.13 113-60 581.77 288.63 1 673-01 1023.62
739.00 600.02 471.95 579-00 676.00 549.02 0.00 530-00 105-16 566.802582.61 270.35 421.52 197.47 241.77 344-94 -7-73 -12-88 0.00 - 9 2 . 1 7
582.85 1 234-372969-6710 108.91
2.19 3.39 0.99 0.01 0.03 5-99 275-89 15.78 2.94 56.41 62-66 156.57
2213-00447.30 1723.10 1511.80 2516.803134.806266.00 11931.03
500.93 130.00 458.94119.00 456.09 25.03 158.40 - 6 . 0 2 -7-96 -1.58
Final demand
2956.00 1318-70 951.63 48.40 872-90 1517-90 2213.00 447-30 1723.10 1511.80 2516.80 3134-80
Total output
156.50 37465-73
0-00 6264-91 0.00 5301-97 17.91 5407-07 8.65 1436.08 0.00 --156-50
129.94 19212-20
24.46 10-29 8"32 0.31 6.48 9.87 15.22 2.25 6-84 8.17 20-12 17-92
Trans. Finance Service House- Other Residual comm. insur, hoM final storage r. estate demand
646.60 180-87 420-95 470.94
0-66 130-30 2.69 0-18 0-88 249.54 3.94 2.14 53-02 80.54 10.72 112.00
Const. Utility Trade
Purchasing sectors
TABLE 5 T r a n s a c t i o n s M a t r i x f o r S a s k a t c h e w a n , 1980 (All v a l u e s in '000,000 $)
Agric. Non-fuel Fuel Forestry Agrie. Other mines mines fish process manuf
22-94 204-66 1.84 0"21 36-21 163-84 0.00 0-40 1-29 0.59 124.77 5-88
Agriculture Non-fuel mines Fuel mines Forestry/fish Agric. process Other manuf. Construction Utility Trade Trans./comm./store Finan./insur./r. est. Service
Production sectors and primary inputs
An Input-Output
~~"
~" ~".
_~" ~5
~'~
:x 0~
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
134
regional demand, and its local supply coefficient is reduced by the amount LQ. If LQ is greater than one, it is viewed as satisfying entire regional demand, and the regional input-output coefficient in (DB)matrix is set equal to the provincial coefficient.) For a given region in the regional input-output framework, each industry's output is determined by its and other industries' final demand. The second step in determining drought impacts is to estimate change in the final demand of the sector directly affected by drought. Agricultural droughts generally affect the agricultural industry. Therefore, direct impacts include changes in output of agriculture, as well as corresponding input levels. The net result of these direct impacts is loss of farm family income. This means that less cash is available for meeting family consumption needs and for new investment. The induced impacts of this reduced farm family income are included in the secondary impacts of the drought. These secondary impacts are obtained by multiplying the change in various input purchases by a multiplier matrix; the latter matrix is obtained from the appropriate regional input-output model. The above secondary impacts are calculated in terms of level of output. By using appropriate multiplier matrices, these are translated into gross domestic product, personal income, and imports.
EMPLOYMENT MODEL Employment levels associated with a given drought impact were estimated by using the concept of an employment production function, similar to that shown in eqn (7). K
EMit : a + bGit + ) ' , CkAki t "['-eit
(7)
k=l
where:
EMi, = the level of employment (full-time equivalent workers basis) in industry i during the year t, Git = the level of output for the industry i, during year t, Ak, = exogenous influences (k = 1.... , K) affecting the relationship between output and employment (such as technological change, and location of industry) for industry i, during year t,
a, b and e = parameters to be estimated, and ei, = random error term. The parameters a, b and c are estimated using ordinary least squares
Agricultural drought impact evaluation model
135
TABLE 6 Estimated Employment Production Functions for All Employees, by Sectors, Saskatchewan, 1980
No.
Sector
Intercept
Slope coefficient with respect to output in 1000 $
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Agriculture Non-fuel mines Fuel mines Forestry and fisheries Agricultural processing Other manufacturing Construction Utilities Trade Transportation, communication and storage Finance, insurance and real estate Services
62 071 2 281 398 0 3 330 7 395 9 389 2 863 50 652
0.004 42 0.007 14 0-001 39 0.062 23 0.002 62 0'017 98 0'012 71 0.004 29 0"016 07
29321
0.00403
11 859 67 327
0.003 26 0.009 50
11. 12.
procedures. Data for the 1961-80 period were used for the estimation. All values were converted into 1980 dollars using indexes of change in industrial selling price, published by Statistics Canada. Estimated coefficients are shown in Table 6. For example, to produce an extra million dollars' worth of output for the agricultural processing sector would require an additional 2"62 workers, over and above the 3330 workers required for base level of production. A similar interpretation can be made to the other numbers in the table. The employment model predicts marginal change in the employment of various production sectors once their levels of output are determined using the input-output model.
SUMMARY The economic evaluation of drought impacts on agriculture requires a conceptually sound foundation. All direct effects of the drought occur right on the farms. If anything can be done to offset the most serious consequences of a drought, it is individual farmers who must make the adjustments. It was necessary, therefore, to develop an analytical procedure that focused attention on the managers of individual farms in Saskatchewan. Computerized models of individual farms were developed or modified for this study. These models view the entire farm as an operating unit and do not
136
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
make arbitrary allocations of certain resources to specific enterprises. The farm level models are a 'laboratory' for evaluation of drought impacts. The models can be used to simulate the effects of droughts on particular farms in the province and assess the consequences of adjusting resource use in response to a drought. Farm level drought impacts are aggregated into changes at the regional level. These are called direct impacts of the agricultural drought. These direct impacts result in changes in final demand for various industries. Using the input-output model multipliers and estimated employment changes, direct impacts are translated into changes in economic aggregates such as gross domestic product, household incomes, and employment levels.
REFERENCES Agriculture Canada, Livestock Market Review, various issues. Anderson, M. and Associates (1982). Drought Adjustment Patterns, Study Element 4, Saskatchewan Drought Studies, Prairie Farm Rehabilitation Administration and Saskatchewan Environment, Regina, Saskatchewan. Farm Credit Corporation Canada (1981). 'Farm Survey', Research Division, Ottawa. Klein, K. K. & Klein, S. A. (1983). 'Framework for Regional Farm Analysis', Study Element 7, Saskatchewan Drought Studies, Prairie Farm Rehabilitation Administration and Saskatchewan Environment, Regina, Saskatchewan. Klein, Kurt K. & Sonntag, Bernard H. (1982). Bioeconomic farm-level model of beef, forage and grain farms in Western Canada: Structure and operation, Agricultural Systems, 8, 41-53. Kraft, D. (1982). Crop Yield Model, Study Element No. 4, Saskatchewan Drought Studies, Prairie Farm Rehabilitation Administration, Regina, Saskatchewan. Kulshreshtha, S. N. & Klein, K. K. (1988). Agricultural drought impact evaluation model: A systems approach. Agricultural Systems., 30, 81-96. Miller, R. E. & Blair P. D. (1985). Input-Output Analysis: Foundations and Extensions. Englewood Cliffs, Prentice-Hall Inc. Saskatchewan Agriculture (1981). Farm Business Analysis, 1980 Grain Enterprise, Marketing and Economics Division, Regina. Saskatchewan Agriculture (1979). Farm Business Review for the Year 1978, Statistics Branch. Sonntag, B. H. (1971). Simulated Near-Optimal Growth Paths for Hog-Corn Farms Under Alternative Resource Prices and Efficiency Situations, Unpublished PhD Thesis, Purdue University, West Lafayette, Indiana. Stalling, J. L. (1958). Indexes of the Influence of Weather on Agriculture Output, Unpublished PhD Thesis, Michigan State University. Statistics Canada (1976, 1981). Census of Canada-Agriculture-Saskatchewan, Ottawa. Zentner, R. P., Sonntag, B. H. & Lee, G. E. (1978). A simulation model for dryland crop production in the Canadian Prairies, Agricultural Systems, 3, 241-51.
Agricultural drought impact evaluation model
137
APPENDIX A: REGIONAL DISTRIBUTION OF REPRESENTATIVE FARMS, SASKATCHEWAN, 1980 Number of farms Region I
Region H
Region III
Region IV
Total
----
2 834 4 704 1 901
----
----
2 834 4 704 1 901
Total
--
9 439
--
--
9 439
Dark B r o w n - - S m a l l --Medium --Large
2 409 3 303 843
----
1 079 1 479 376
1 621 2 224 566
5 109 7 006 1 785
6555
--
2934
4411
13900
4 176 3 494 486
----
5 059 4 232 586
1 672 1 399 194
10 907 9 125 1 266
8 156
--
9 877
3 265
21 298
Cereal Brown--Small --Medium --Large
Total Black--Small --Medium --Large Total
Mixed Brown, Dark Brown, L G - H C " Brown, Dark Brown, H G - L C b Black, L G - H C Black, H G - L C
122 153 589 271
254 318 ---
61 76 546 251
71 89 301 139
508 636 1 436 661
1 135
572
934
600
3 241
248 252 1 216 502
486 494 ---
124 126 1 123 462
176 178 780 322
1 034 1050 3 119 1 286
2 218
980
1 835
1456
6 489
742 221 804 239
556 166 603 180
441 132 477 142
580 173 628 187
2 319 692 2 512 748
2006
1 505
1 192
1 568
6271
Cow-calf Farm Brown, Dark B r o w n - - S m a l l Brown, D a r k B r o w n - - L a r g e Black--Small Black--Large
Feeder Farm Unfinished~--Small Unfinished--Large Finishedd--Small Finished--Large
(continued)
138
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
APPENDIX
A--contd. Number of farms
Region I Hog Small Medium Large
Dairy Farm Small Large
Region H
Region III
Region IV
Total
326 189 52
307 178 49
892 517 142
363 210 57
1 888 1094 300
567
534
1551
630
3 282
109 123
33 37
115 129
38 43
295 332
232
70
244
81
627
° Low grain acres, high cattle numbers. b High grain acres, low cattle numbers. c Farms that raise calves, maintain them through winter, pasture feeders the following summer, then sell long yearling feeders. d Same type of farm as unfinished feeder except long yearling feeders are placed in the feedlot in the fall instead of being sold.
Agricultural Drought Impact Evaluation Model: Description of Components*
K. K. Klein Department of Economics, University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
S. N. Kulshreshtha Department of Agricultural Economics, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 0W0
& S. A. Klein Medicine Hat College, Brooks, Alberta, Canada T0J 0J0 (Received 8 January 1988; revised version received 8 August 1988; accepted 26 September 1988)
ABSTRACT Various components of the Agricultural Drought Impact Evaluation Model are described in this paper, which is a secondpaper in a series of three. Various components describe methodology to estimate yields of various cereal grain and forage crops under drought conditions; to simulate the effects of the drought on economic andfinancial performance of thefarm firm; to aggregate micro level results into regional (or provincial) level entities; to estimate secondary impacts of the drought both on economic activity and employment levels. * Financial support received from the Prairie Farm Rehabilitation Administration is
gratefully acknowledged. Authors are thankful to Mr George Pearson, Murray Jones, Ray Fautley, and Ted O'Brian for assistance received during the course of the study. 117 Agricultural Systems 0308-521X/89/$03.50 © 1989 Elsevier Science Publishers Ltd, England. Printed in Great Britain
118
K. K. Kle&, S. N. Kulshreshtha, S. A. Klein
INTRODUCTION In the first paper in this series (Kulshreshtha & Klein, 1988), the methodology for developing an agricultural drought impact evaluation m o d e l - - A D I E M - - w a s described. This model has four components--yieldhydrology simulator, a set of farm business simulation models, an inputoutput model, and an employment model. Each of these components is described in detail in this paper.
Y I E L D - H Y D R O L O G Y SIMULATOR Previous studies on estimating a relationship between yield and amount of moisture available to plants have varied from simple weather index formulation (Stalling, 1958), to more sophisticated measurement of climatic variables, soil properties, and management practices. In this study the relationship between yields of a given crop at a specific location and moisture stress at that location was estimated. The level of soil moisture stress was measured at five key stages of plant growth: seeding to emergence, emergence to jointing, jointing to heading, heading to soft kernel development, and hardening of the kernels. The soil moisture at each of these stages was derived using meteorological data, date of seeding, and the Versatile Soil Moisture Budget (VSMB) model. (For more details of the Versatile Soil Moisture Budget Model, see Kraft, 1982.) The VSMB model is a computerized mathematical model which estimates changes in soil moisture resulting from net evapotranspiration. The level of soil moisture is calculated each day by subtracting the moisture withdrawn and adding the daily precipitation to that available initially. Since the water retaining capacity of different types of soils is different, the VSMB model was run individually for three soil textures: coarse, medium, and fine. (Coarse soils have a holding capacity of 125-150 mm of moisture; medium soils have between 200 and 225 mm holding capacity; and the fine soils have a holding capacity of over 280 mm.) Two types of yield prediction models were developed: one, for cereal crops, and the other for forage crops. The latter type of model was also used for predicting the impact of the drought on quality of pastures. The cereal crop yield model was estimated for five major crops: wheat, oats, barley, canola, and flaxseed. In general, the relationship was estimated as: k __ YDrt - f ( T ~ k, FRT~tk , SMut)
(1)
Agricultural drought impact evaluation model
119
where: k YDrtyield per
acre during period t, in location r, for the k th crop. = index of technological change for crop k, during year t. FR~, = level of fertilizer for crop k, in location r, during year t. SMrt = level of soil moisture stress during year t in region r. Since data used in the estimation of the yield were of a time series nature, they needed adjustments for variations other than weather in order to determine the magnitude of the weather influence. The non-weather related variables were: technological change as approximated by an index of varietal yield improvement, and rate of application of fertilizer. The first variable was calculated by weighting the relative yield of different varieties of that crop by their share of total acreage. (The yield of one dominating variety was taken as one, and the others were normalized against that base. These normalized yields were subsequently multiplied by the share of each variety's total acreage under that crop.) Fertilizer application rates were those that were recommended to farmers in different regions during that year. Although the model has the capability of estimating the crop yields given available moisture at various stages of plant growth, this procedure can be by-passed for any ex post drought impact evaluation. The reason for this is the fact that yields are already known and therefore need not be estimated in a manner described above.
FARM SIMULATION MODELS The key to estimating the cost of drought is to measure its financial effects on the individual farm operator. It must be determined how seriously the drought affects the farmer's level of well being, as measured by such indicators as net income, net worth and cash flow. The financial incentives for adopting specific production and management strategies that reduce the impact of drought on the individual farm must be evaluated and understood. The farm simulation models were developed quantitatively to measure the consequences of drought and drought-mitigating strategies at the farm level. These models are comprehensive enough to deal with production of all major agricultural commodities in the province. They also consider the time effects of production and the lingering effects of drought on production in the ensuing years. They have the capacity to provide a full financial analysis of a given farm situation, including calculations of net worth, net farm income, receipts and costs by enterprise, cash flow analysis, changes in debts,
120
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
value of different assets, family living expenditures, and income tax payments. Four simulation models were developed or modified in this study: crops, beef-forage-grain, hog-grain, and dairy-forage-grain. The first three are modified versions of farm-level models described in detail in Zentner et al. ( 1978), Klein & Sonntag (1982) and Sonntag ( 1971), respectively. The fourth model is described in Klein & Klein (1983). Though very complex in detail, the simulation models proceed through a series of steps not unlike the decision process used by any farm operator-decision maker. Every farm operator must choose among many alternative methods of production, e.g. rotation, crops to plant, number of pre-seed tillage operations, machines to use, planting time, fertilization rate, herbicide use, and other jobs that require different combinations of resources. The manager must ensure that adequate resources (machines, buildings, labor, cash) are available to carry out the production plan. And it is hoped that wise choices are made so that suitable rewards are obtained from the farming operation. The simulation models mirror this decision process. They develop complete budgets for a specific production plan for periods up to ten years in length. The models are computerized. This permits rapid calculations and comparisons of dozens of production plans, if desired. The simulation models can compare budgets for a wide array of production and management strategies for a farm. They incorporate literally millions of combinations of production and management strategies for a representative farm and have the facility to substitute or add newly developed production technologies. The farm level models have the capacity to differentiate among good and bad growing years and incorporate probabilistic estimates of their frequencies. They reflect closely the actual production and management decisions on the farm, e.g. integration of enterprises, multi-year planning horizons, 'lumpy' investments, and institutional constraints like income tax. Of major importance in this study, the simulation models generate an economic analysis consisting of: a. b. c. d.
projected net income for each of the production plans considered, projected net worth for each of the production plans, riskiness (variability in net income, net worth, cash flow) of selected production plans over time, additional resources required to accomplish selected production plans.
The economic analysis generated by the simulation models permits discovery of the best time path available for changes in the organization of the representative farm's resources.
Agricultural drought impact evaluation model
121
Major steps in the operation of each of the farm business simulation models are described in the flowchart of Fig. 1. Two important cycles are prominent in each model's operation. An outside cycle permits the model to evaluate a number of production plans for the representative farm and determine the 'best' one. Best refers to highest terminal net worth among the production plans evaluated. There is no guarantee in the simulation models that a better production plan could not be found. A probabilistic statement about how close the best plan may be to the true optimum is all that can be made. The second or inside cycle revolves over the number of years specified by the user of the model in the input form. Each of the simulation models can be run for a period of one to ten years. Each of the steps in Fig. 1 is done for each year of the simulation (inside cycle) and for each of the production plans to be evaluated (outside cycle). Thus, a single run of the model could evaluate and compare dozens of production plans for a single representative farm over a time period up to 10years in length.
Selection of representative farms Twenty-six representative farms were developed to model production of the major agricultural commodities in the major soil zones of Saskatchewan: nine crop farms, twelve beef cattle farms, two dairy farms, and three hog farms. Representative farms in all categories were assumed to have the same characteristics of tenure as reported in the Agricultural Census 1981 by Statistics Canada: 1. 2. 3.
brown soil zone--63% cropland owned, 27% rented, dark brown soil zone--70% cropland owned, 30% rented. black soil zone--73% cropland owned, 27% rented.
Machines and machine sizes were selected for these farms on the basis of discussion with extension workers in Saskatchewan and from previous studies (unpublished) conducted by Agriculture Canada. Farm assets and debts for each representative farm were based on survey results of Farm Credit Corporation. The following sections summarize the major characteristics of representative farms used in this study. Complete details on each farm are available in the Technical Appendix to Klein & Klein (1983).
Cropfarms Three representative farms were chosen to represent crop farms in each of the major soil zones that characterize the main grain growing area of
122
K. K. Klein, S. N. Kulshreshtha, S. A. Klein T
Buy or rent additional inputs
( START ) Read input form --set key variables --set parameters on run
Prepare one-year L budget for production plan ["
Modify base data
.I-]
Print annual tables, if desired
Begin cycle over number of production plans
I
Store identification of plan and value of objective funcUon
Select production plan from data provided m input form
Print summary tables, if desired
Begin cycle over number of years No Compute job matrices
Ves
~ Print output tables for 'best' or only production plan
Account for joint use of inputs
( Compute input requirements for production plan
END )
h Fig. 1. Flowchart for simulation model.
Determine production plan that is 'best' and regenerate it
Agricultural drought impact evaluation model
123
Saskatchewan (Fig. 2). The representative farms included a small, medium and large farm in each of the three soil zones. The resource bases represented by each of the representative farms are shown in Table 1. (The size categories of each of the representative crop farms were determined from data obtained from Statistics Canada and Saskatchewan Agriculture (1979).) Farms in the dark brown soil zone had higher assets and debts than did farms in the other two soil zones. The proportion of summerfallow on actual farms in Saskatchewan has varied between 0.40 and 0"45 in recent years. However, actual farms typically
UNCULTIVATABLE REGION
BLACK SOIL DARK BROWN SOIL
BROWN SOIL
Fig. 2.
Soil zones of Saskatchewan.
124
K. K. Klein, S. N. Kulshreshtha, S. A. Klein TABLE 1 Representative Cereal Farms, in Saskatchewan, Selected Characteristics, 1980 Farm size Small
Medium
Large
Brown soil zone Average Acres Farmland Cropland Assets ('000 $) Debts ('000 $)
477 415 161.6 27.8
1 029 896 288"3 49.9
2 465 2 146 679"8 118-1
Dark brown soil zone Average Acres Farmland Cropland Assets ('000 $) Debts ('000 $)
457 407 207.1 35.1
1 139 1 015 459"4 79.2
1 855 1 654 812'6 127.7
958 795 316"5 54.6
2 204 1 829 625.6 108.5
Black soil zone Average Acres Farmland Cropland Assets ('000 $) Debts ('000 $)
425 353 152'4 26.1
use rotations that include one-half, one-third, or one-quarter summerfallow. Individual representative farms therefore had to be given conventional rotations that in the aggregate would produce the required proportion of summerfallow. All representative crop farms in the brown soil zone were given a one-half summerfallow, one-half crop rotation. The small and large representative farms in the dark brown soil zone were also given a one-half crop, one-half summerfallow rotation. The medium-sized representative farm in the dark brown soil zone was given a two-thirds crop, one-third summerfallow rotation. All representative farms in the black soil zone used a one-third summerfallow, two-thirds crop rotation. Since the livestock models (beef-forage-grain, hog-grain, and dairyforage-grain) contained land area for feed grain production, only wheat, canola, and flax were grown on the representative crop farms. In aggregate, this accounted for the entire area of major crop production in Saskatchewan in the base year of 1980. Field operations were begun in early May for farms in the brown and dark brown soil zones; they were started two weeks later for farms in the black soil
Agricultural drought impact evaluation model
125
zone. Two pre-seed tillage operations were performed on farms in the brown and dark brown zones; an extra operation was performed on farms in the black zone. Pre-emergent weed control with chemical spray was used in the spring. Both wild oats and broadleaf seeds were controlled with herbicides. Fertilizer was applied at a rate of 5 lbs N and 20 lbs P205 per acre on summerfallow and 201bs N with 201bs P205 per acre on stubble crops. These correspond to average application rates used by farmers in Saskatchewan in 1980 (Saskatchewan Agriculture, 1979, 1981). Four tillage operations that used a cultivator and rod weeder were simulated for summerfallow area of representative farms in the brown soil zone. One extra tillage operation was used on farms in the dark brown zone; two extra operations were performed on farms in the black zone. No postharvest tillage operations were done. Seed was purchased from off-farm sources. No crop insurance was carried on either cereal or oilseed crops but premiums for the Western Grain Stabilization Program were paid. Machines were replaced when they reached 66% of their maximum useful life. Planting and harvesting operations were assumed to differ by size of farm and by soil zone. In the brown soil zone, small and medium-sized farms planted crops with a discer; the large farm used a hoe drill for planting operations. In the dark brown soil zone, the small farm planted with a discer, the two larger sized farms used a hoe drill. In the black soil zone, all farms had a press drill. Discer seeding was assumed to be used for all stubble crops. Most farms had a power take-offswather; however, medium and large farms in the black zone had a self-propelled swather. All farms in the black zone plus the large farm in the brown zone had self-propelled combines; the other farms had power take-off combines.
Beef cattle farms Twelve farms were chosen to represent different types of beef-grain farms in the province: four 'mixed', four cow-calf, and four feeder farms. The four mixed farms were identified by different ratios of cattle to grain enterprise in two zones (brown-dark brown and black soil zones): 1. 2. 3. 4.
brown~tark brown soil--low grain, high cattle (LG-HC); brown-dark brown soil--high grain, low cattle (HG-LC); black soil-low grain, high cattle (LG-HC); and black soil-high grain, low cattle (HG-LC).
The type of beef enterprise used on these mixed farms was cow-calf, where weaned calves are maintained over the winter, and are sold in the spring as year-old stockers.
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
126
Four farms Saskatchewan: 1. 2. 3. 4.
were constructed
to
represent
cow-calf farms
in
brown-dark brown soil--small (69 cows, 369 crop acres); brown-dark brown soil--large (137 cows, 732 crop acres); black soil-small (40 cows, 213 crop acres); and black soil-large (59 cows, 315 crop acres).
The type of beef enterprise on the cow-calf farms was raising and selling of weaned calves. Only the breeding herd and replacements were kept over the winter season. Four farms were selected to represent feeder farms in Saskatchewan: 1. 2. 3. 4.
small unfinished feeder--70 cow herd and 353 acres of cropland; large unfinished feeder--144 cows and 440 acres of cropland; small finished feeder--70 cows and 343 acres of cropland; and large finished feeder--144 cows, 40 purchased feeders, and 446 acres of cropland.
The unfinished feeder farms produced and sold long yearling feeders, i.e. offspring are maintained over the winter, placed on pasture for the summer and sold as unfinished feeders off-pasture. The other two representative farms produced long yearling feeders as well but finished them to slaughter weight on their own farms. The overall resource base of these representative farms is documented in Table 2. No community pasture was made available to mixed farms but it was available for 34% of the cow-calf units on cow-calf farms. (This is consistent with available level of cattle on Prairie Farm Rehabilitation Administration pastures, provincial pastures, private leases and grazing association pastures.) Therefore, only 66% of the cattle had to be pastured on land resources available to the representative cow-calf farms. To account for available community pasture, 30 cow-calf units for each of the small feeder farms and 61 cow-calf units for each of the large feeder farms was made available. The number of cows per feeder farm placed on community pastures was proportionately greater than for the representative cow--calf farms. This was to allow for the estimated 62 000 yearlings on community pastures in Saskatchewan in 1980. The beef-forage-grain simulation model (Klein & Sonntag, 1982) permits only cow-calf units to be placed on outside grazing sources. Therefore, the total number of feeders on community pastures was converted to cow-calf units at the rate of two feeders per cow-calf unit. This meant that 34% of the cow herd on these farms could graze on off-farm pasture.
310 85 500 50 335-0 49.9
1 000
a Low-grain acres, high cattle numbers. b High-grain acres, low cattle numbers.
Average Acres Farmland Improved Cropland Tame Hay and Pasture Unimproved Average Cows Assets ('000 $) Debts ('000 $) 739 215 319 27 446-6 66.5
1 280
LG-HC ~ H G - - L C
Brown, dark brown
Black
176 44 265 28 237.9 35.5
488 723 194 169 18 479.2 71.4
1 099
2 728
369 732 156 311 828 1 644 69 137 384-3 770-0 57-3 114-7
1 374
Large
213 92 480 40 265.8 39.6
796
315 133 708 59 355.2 52-9
1 174
Large
Black
Small
Cow-calf Brown, dark brown
L G - H C ~ H G - L C ~ Small
Mixed
TABLE 2 Representative Cattle Farms in Saskatchewan, Selected Characteristics, 1980
2054
Large
353 440 108 139 714 1 468 70 144 374.6 631.8 55-8 94-1
1 184
Small
Unfinished
2061
Large
343 446 105 136 714 1468 70 144 415-2 684.3 61.9 101.9
1 170
Small
Finished
Feeder farms
7_,
e~
e5
128
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
Cropping rotations were one-third fallow, two-thirds crop on each of these representative beef-producing farms. It was assumed that feed grain was grown on all land not used for cereal forage. Fertilizer and chemical herbicides were applied on all crops.
Dairy farms Representative dairy farms were developed for fluid milk producers only. Cream and manufactured milk production were not included in these representative farms. The two farms chosen to represent fluid milk production were: 1. 2.
small--30 cows with 205 acres of cropland, and large 48 cows with 324 acres of cropland.
The resource base for each of the representative farms is in Table 3. A typical dairy herd has 83% of the cows lactating and 17% of cows dry at any point in time (based on a 305 day lactation period). An average farm contains 4% high producing cows (31.8kg milk per day), 30% medium producing cows (23.6 kg milk per day), and 66% low producing cows (16.8 kg milk per day). These data were incorporated into each of the representative farms.
Hog farms Three farms were chosen to represent hog farms in the province (Table 4): (1) Small--10 sows producing one litter per year (80 hogs) with 328 acres of cropland,
TABLE 3 Representative Dairy Farms in Saskatchewan, Selected Characteristics, 1980 Farm
Average Acres Farmland Improved Cropland Tame Hay and Pasture Unimproved Average Cows Assets ('000 $) Debts C000 $)
Small
Large
397 205 72 111 30 459.5 60.6
629 324 115 178 48 556.2 73.4
Agricultural drought impact evaluation model
129
TABLE 4 Hog Farms in Saskatchewan, Selected Characteristics, 1980
Farm size
Average Acres Farmland Improved Cropland Average Hogs Assets ('000 $) Debts ('000 $)
Small
Medium
Large
422 328 80 154.6 59.6
557 435 192 205.5 79.3
960 748 900 421-3 162.5
(2) Medium--16 sows producing an average of 1.5 litters per year (191 hogs) with 435 acres of cropland, and (3) Large--60 sows producing two litters per year (900 hogs) with 748 acres of cropland. All representative farms were modelled as farrow-to-finish types of hog enterprise. The farrowing schedules were altered among representative farms to provide a somewhat even flow of hogs to market. Hogs were marketed at 190 lbs liveweight. The one-third fallow, two-thirds crop rotation was used on these representative farms. The only crop grown on these representative farms was barley.
Drought adjustments The farm business simulation models were programmed to permit analysis of farm-level adjustments during a drought period. Most of these adjustments were related to agronomic and cultural practices, or management decisions typical during a drought year. They included advancing the starting date of spring field operations (to gain a yield advantage), reducing summerfallow tillage operations (to conserve moisture), chemical summerfallow (to conserve moisture and prevent soil erosion), reducing purchases of depreciable assets (to assist cash flow), early weaning of calves (to conserve fodder), and heavier culling of cows (to save forage). In total, over 50 possible farm production adjustments were included in the farm business simulation models. The drought adjustment strategies are under the control of the user of the simulation models. When invoked by the user, they provide for a comparative financial and economic analysis of the benefits of such actions.
130
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
They do not predict what farmers will do; they provide for an evaluation of the consequences of particular strategies. Aggregation
o f f a r m results
To permit regional assessments of the impacts of a drought and droughtmitigating strategies, coefficients were developed to aggregate the output from farm level models. The procedure consisted of estimating the numbers of each type of representative farm (Distribution of farms by type of farm and regions within Saskatchewan is shown in Appendix A.) in each of four economic zones in the province (Fig. 3). If a drought situation was to affect yields on cereal farms in Region I (southeast), the effects on six representative farms would be evaluated. The changes in financial and physical variables on the small representative farm in the dark brown soil zone would be multiplied by the estimated 2409 actual farms that were represented. These results would be added to changes that occurred on the 3303 medium-sized representative farms, and so on.
18
17 16
14
Is IIII 13 12 11
.
(
"
I
,
\
3
10
I
.
I
j
2
5
I
'
Fig. 3. Regionalboundaries for sub-provincial input-output tables.
Agricultural drought impact evaluation model
131
This procedure allows for differential drought adjustment strategies on different sizes of farms within and among soil zones in the same region in the province. It also greatly increases the effective number of representative farms. The small representative farm in the dark brown soil zone represents farms in three different regions in the province. Drought conditions of different levels of severity could be simulated for each of the regions. That particular representative farm then effectively becomes three representative farms. The aggregation of farm level economic indicators into regional or provincial level indicators assumes that farmers are facing a perfectly elastic demand for their product. Thus, change in product prices as a result of a local drought would be minimal, if at all. (This assumption is tenable in the light of the fact that most grain markets are international in scope; market prices are established through an interplay of international supply and demand.) Similarly, it is also stipulated that, due to relatively small changes in feedgrains prices, changes in the supply of livestock would also be minimal.
I N P U T - O U T P U T MODEL An input-output (I-O) model portrays the interdependence of one economic sector on other economic sectors, or on forces outside the region. The economy is broken into several industries (also called sectors) which both produce and purchase several products (also called commodities). Besides the industries, the economy contains consumers and various levels of government, each one of which purchases goods and services (or commodities) produced by industries. Since no region is totally selfsufficient, imports are allowed into the region to cover any shortfall, and similarly excess production (over and above local demand) is disposed as exports. The I-O models adapted for the ADIEM model contained 12 major sectors and 72 commodities. This commodity-by-industry framework, known as a rectangular input-output model, is an improvement over the traditional square I-O models. An I-O model is based on two assumptions: one, constant and fixed proportions of inputs are used in the production of various commodities by an industry, and two, if output of an industry changes, level of production of all commodities increases proportionately. The output of industries and commodities are related in the following manner: Q = U+ F
(2)
132
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
where, Q is a vector of commodity output U is a 12 × 12 matrix of intermediate demand, and F is a vector of final demand. The two assumptions of rectangular I-O models are written as: U = BG
(3)
where, B is the technology matrix of 72 × 12 dimension, and G is a vector of commodity output. Intermediate demands for various inputs are derived from level ofindustry's output (G), and the production function, as represented by the B matrix. The second assumption dictates that: G = DQ
(4)
where, D is a 12 x 72 matrix of market share coefficients. Substituting eqn (2) into (4) and solving for G: G = D [ U + F]
(5)
Substituting eqn (3) into (5): G = DEDG + F]
(5.1)
G = (DB)G + (DG)
(5.2)
or
Equation (5.2) states that total output of an industry is split into the portion demanded by other industries as intermediate inputs (i.e. I-DB]G), and the portion destined for final use (i.e. DF). This equation represents the transactions table. An example of a transactions table is shown in Table 5. The equation can be solved for G, as shown in eqn (6). G = ( I - D B ) - 1(OF)
(6)
In other words, industry output levels can be estimated by a multiplier m a t r i x - - ( I - D B ) -~ and industry's final demand--DF. Thus, if final
demand for a sector changes, by a process of'ripple effects', all other sectors would be affected. The model can be used for estimating impacts of a drought in the following manner. The model is converted into a regional input-output model by using the method of location quotient. (For details of this method, see Miller & Blair (1985) pp. 296-9. The method is viewed as a measure of the ability of regional industry i to supply the demands placed upon it by other industries in the region and by regional final demand. If the location quotient (LQ) is less than one, the industry is less capable of satisfying
232.56 475-93
562.64
998-00 913.00 496.79 -6-95 -7.48
2956-00
Sub-total
G D P households G D P other Imports Comm. tax subsidy Residual
Total inputs
1318.70 951.60
523.00 226.01 479.00 207.01 67.69 26.36 18.77 20.97 -2.32 -4.68
0-06 0.18 26-18 42.87 3.09 0-11 0.00 0.00 0-40 0.02 12.53 1.72 18.82 26-03 26.77 27.29 6.89 0'68 19.86 3.91 57.98 351.70 59.99 21.42
48.40 872.90
13-00 90-00 309-00 11.00 82.00 283-00 2.26 118-33 484.16 1.85 4.66 - 5 4 . 2 4 -0.16 -5.90 -5.82
20.45 583.82 501.70
0.10 393.85 1-58 0.02 0-30 68-99 0.01 0-03 25.88 2.56 0.00 22.31 0.00 62.90 5-58 2.80 7.27 132.02 0.11 2.36 18-65 0.14 7.33 25.53 0.66 13-67 14.03 6.29 45.84 75.18 1.93 2.00 8-96 5.84 48.28 103.00
0-06 35-45 1.15 0-71 1-06 1.47 28.84 1.20 2-53 0.00 0.01 0"03 0.01 0-32 0.24 2.46 20-79 44-95 81.82 9-89 90-92 14-66 25.29 13.98 1.31 9.27 20.06 23.59 95.29 166-38 5.82 36-05 15.04 21.59 186-33 114.20
593.00 492.00 543.00 451-00 109.23 78.31 60-90 23.39 -3-97 -3'85
16.63 11.15 0-60 0.75 120.38 129.58 5.66 15-67 150.17 268.59 34.24 480.97
71.39 6.30 3.20 4-80 75-19 184.68 345.56 156.48 860.50 372.56 111.79 777.21
2385-39 811-02 872.57 17.22 564.26 549.77 1318.13 113-60 581.77 288.63 1 673-01 1023.62
739.00 600.02 471.95 579-00 676.00 549.02 0.00 530-00 105-16 566.802582.61 270.35 421.52 197.47 241.77 344-94 -7-73 -12-88 0.00 - 9 2 . 1 7
582.85 1 234-372969-6710 108.91
2.19 3.39 0.99 0.01 0.03 5-99 275-89 15.78 2.94 56.41 62-66 156.57
2213-00447.30 1723.10 1511.80 2516.803134.806266.00 11931.03
500.93 130.00 458.94119.00 456.09 25.03 158.40 - 6 . 0 2 -7-96 -1.58
Final demand
2956.00 1318-70 951.63 48.40 872-90 1517-90 2213.00 447-30 1723.10 1511.80 2516.80 3134-80
Total output
156.50 37465-73
0-00 6264-91 0.00 5301-97 17.91 5407-07 8.65 1436.08 0.00 --156-50
129.94 19212-20
24.46 10-29 8"32 0.31 6.48 9.87 15.22 2.25 6-84 8.17 20-12 17-92
Trans. Finance Service House- Other Residual comm. insur, hoM final storage r. estate demand
646.60 180-87 420-95 470.94
0-66 130-30 2.69 0-18 0-88 249.54 3.94 2.14 53-02 80.54 10.72 112.00
Const. Utility Trade
Purchasing sectors
TABLE 5 T r a n s a c t i o n s M a t r i x f o r S a s k a t c h e w a n , 1980 (All v a l u e s in '000,000 $)
Agric. Non-fuel Fuel Forestry Agrie. Other mines mines fish process manuf
22-94 204-66 1.84 0"21 36-21 163-84 0.00 0-40 1-29 0.59 124.77 5-88
Agriculture Non-fuel mines Fuel mines Forestry/fish Agric. process Other manuf. Construction Utility Trade Trans./comm./store Finan./insur./r. est. Service
Production sectors and primary inputs
An Input-Output
~~"
~" ~".
_~" ~5
~'~
:x 0~
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
134
regional demand, and its local supply coefficient is reduced by the amount LQ. If LQ is greater than one, it is viewed as satisfying entire regional demand, and the regional input-output coefficient in (DB)matrix is set equal to the provincial coefficient.) For a given region in the regional input-output framework, each industry's output is determined by its and other industries' final demand. The second step in determining drought impacts is to estimate change in the final demand of the sector directly affected by drought. Agricultural droughts generally affect the agricultural industry. Therefore, direct impacts include changes in output of agriculture, as well as corresponding input levels. The net result of these direct impacts is loss of farm family income. This means that less cash is available for meeting family consumption needs and for new investment. The induced impacts of this reduced farm family income are included in the secondary impacts of the drought. These secondary impacts are obtained by multiplying the change in various input purchases by a multiplier matrix; the latter matrix is obtained from the appropriate regional input-output model. The above secondary impacts are calculated in terms of level of output. By using appropriate multiplier matrices, these are translated into gross domestic product, personal income, and imports.
EMPLOYMENT MODEL Employment levels associated with a given drought impact were estimated by using the concept of an employment production function, similar to that shown in eqn (7). K
EMit : a + bGit + ) ' , CkAki t "['-eit
(7)
k=l
where:
EMi, = the level of employment (full-time equivalent workers basis) in industry i during the year t, Git = the level of output for the industry i, during year t, Ak, = exogenous influences (k = 1.... , K) affecting the relationship between output and employment (such as technological change, and location of industry) for industry i, during year t,
a, b and e = parameters to be estimated, and ei, = random error term. The parameters a, b and c are estimated using ordinary least squares
Agricultural drought impact evaluation model
135
TABLE 6 Estimated Employment Production Functions for All Employees, by Sectors, Saskatchewan, 1980
No.
Sector
Intercept
Slope coefficient with respect to output in 1000 $
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Agriculture Non-fuel mines Fuel mines Forestry and fisheries Agricultural processing Other manufacturing Construction Utilities Trade Transportation, communication and storage Finance, insurance and real estate Services
62 071 2 281 398 0 3 330 7 395 9 389 2 863 50 652
0.004 42 0.007 14 0-001 39 0.062 23 0.002 62 0'017 98 0'012 71 0.004 29 0"016 07
29321
0.00403
11 859 67 327
0.003 26 0.009 50
11. 12.
procedures. Data for the 1961-80 period were used for the estimation. All values were converted into 1980 dollars using indexes of change in industrial selling price, published by Statistics Canada. Estimated coefficients are shown in Table 6. For example, to produce an extra million dollars' worth of output for the agricultural processing sector would require an additional 2"62 workers, over and above the 3330 workers required for base level of production. A similar interpretation can be made to the other numbers in the table. The employment model predicts marginal change in the employment of various production sectors once their levels of output are determined using the input-output model.
SUMMARY The economic evaluation of drought impacts on agriculture requires a conceptually sound foundation. All direct effects of the drought occur right on the farms. If anything can be done to offset the most serious consequences of a drought, it is individual farmers who must make the adjustments. It was necessary, therefore, to develop an analytical procedure that focused attention on the managers of individual farms in Saskatchewan. Computerized models of individual farms were developed or modified for this study. These models view the entire farm as an operating unit and do not
136
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
make arbitrary allocations of certain resources to specific enterprises. The farm level models are a 'laboratory' for evaluation of drought impacts. The models can be used to simulate the effects of droughts on particular farms in the province and assess the consequences of adjusting resource use in response to a drought. Farm level drought impacts are aggregated into changes at the regional level. These are called direct impacts of the agricultural drought. These direct impacts result in changes in final demand for various industries. Using the input-output model multipliers and estimated employment changes, direct impacts are translated into changes in economic aggregates such as gross domestic product, household incomes, and employment levels.
REFERENCES Agriculture Canada, Livestock Market Review, various issues. Anderson, M. and Associates (1982). Drought Adjustment Patterns, Study Element 4, Saskatchewan Drought Studies, Prairie Farm Rehabilitation Administration and Saskatchewan Environment, Regina, Saskatchewan. Farm Credit Corporation Canada (1981). 'Farm Survey', Research Division, Ottawa. Klein, K. K. & Klein, S. A. (1983). 'Framework for Regional Farm Analysis', Study Element 7, Saskatchewan Drought Studies, Prairie Farm Rehabilitation Administration and Saskatchewan Environment, Regina, Saskatchewan. Klein, Kurt K. & Sonntag, Bernard H. (1982). Bioeconomic farm-level model of beef, forage and grain farms in Western Canada: Structure and operation, Agricultural Systems, 8, 41-53. Kraft, D. (1982). Crop Yield Model, Study Element No. 4, Saskatchewan Drought Studies, Prairie Farm Rehabilitation Administration, Regina, Saskatchewan. Kulshreshtha, S. N. & Klein, K. K. (1988). Agricultural drought impact evaluation model: A systems approach. Agricultural Systems., 30, 81-96. Miller, R. E. & Blair P. D. (1985). Input-Output Analysis: Foundations and Extensions. Englewood Cliffs, Prentice-Hall Inc. Saskatchewan Agriculture (1981). Farm Business Analysis, 1980 Grain Enterprise, Marketing and Economics Division, Regina. Saskatchewan Agriculture (1979). Farm Business Review for the Year 1978, Statistics Branch. Sonntag, B. H. (1971). Simulated Near-Optimal Growth Paths for Hog-Corn Farms Under Alternative Resource Prices and Efficiency Situations, Unpublished PhD Thesis, Purdue University, West Lafayette, Indiana. Stalling, J. L. (1958). Indexes of the Influence of Weather on Agriculture Output, Unpublished PhD Thesis, Michigan State University. Statistics Canada (1976, 1981). Census of Canada-Agriculture-Saskatchewan, Ottawa. Zentner, R. P., Sonntag, B. H. & Lee, G. E. (1978). A simulation model for dryland crop production in the Canadian Prairies, Agricultural Systems, 3, 241-51.
Agricultural drought impact evaluation model
137
APPENDIX A: REGIONAL DISTRIBUTION OF REPRESENTATIVE FARMS, SASKATCHEWAN, 1980 Number of farms Region I
Region H
Region III
Region IV
Total
----
2 834 4 704 1 901
----
----
2 834 4 704 1 901
Total
--
9 439
--
--
9 439
Dark B r o w n - - S m a l l --Medium --Large
2 409 3 303 843
----
1 079 1 479 376
1 621 2 224 566
5 109 7 006 1 785
6555
--
2934
4411
13900
4 176 3 494 486
----
5 059 4 232 586
1 672 1 399 194
10 907 9 125 1 266
8 156
--
9 877
3 265
21 298
Cereal Brown--Small --Medium --Large
Total Black--Small --Medium --Large Total
Mixed Brown, Dark Brown, L G - H C " Brown, Dark Brown, H G - L C b Black, L G - H C Black, H G - L C
122 153 589 271
254 318 ---
61 76 546 251
71 89 301 139
508 636 1 436 661
1 135
572
934
600
3 241
248 252 1 216 502
486 494 ---
124 126 1 123 462
176 178 780 322
1 034 1050 3 119 1 286
2 218
980
1 835
1456
6 489
742 221 804 239
556 166 603 180
441 132 477 142
580 173 628 187
2 319 692 2 512 748
2006
1 505
1 192
1 568
6271
Cow-calf Farm Brown, Dark B r o w n - - S m a l l Brown, D a r k B r o w n - - L a r g e Black--Small Black--Large
Feeder Farm Unfinished~--Small Unfinished--Large Finishedd--Small Finished--Large
(continued)
138
K. K. Klein, S. N. Kulshreshtha, S. A. Klein
APPENDIX
A--contd. Number of farms
Region I Hog Small Medium Large
Dairy Farm Small Large
Region H
Region III
Region IV
Total
326 189 52
307 178 49
892 517 142
363 210 57
1 888 1094 300
567
534
1551
630
3 282
109 123
33 37
115 129
38 43
295 332
232
70
244
81
627
° Low grain acres, high cattle numbers. b High grain acres, low cattle numbers. c Farms that raise calves, maintain them through winter, pasture feeders the following summer, then sell long yearling feeders. d Same type of farm as unfinished feeder except long yearling feeders are placed in the feedlot in the fall instead of being sold.