- Email: [email protected]
Transferability issues of soybean research: a regional example
AGRICULTURAL SYSTEMS Agricultural Systems 81 (2004) 213–226 www.elsevier.com/locate/agsy
Transferability issues of soybean research: a regional example Michael P. Popp
a,*
, Carl R. Dillon b, Jennie S. Popp
a
a
b
Department of Agricultural Economics and Agribusiness, University of Arkansas, 217 Agriculture Building, Fayetteville, AR 72701, USA Department of Agricultural Economics, University of Kentucky, 403 C.E. Barnhart Building, Lexington, KY 40546, USA
Received 19 March 2003; received in revised form 24 October 2003; accepted 11 November 2003
Abstract Agricultural research monies are becoming increasingly scarce and therefore subject to additional scrutiny. To analyze the impact of transferring site-specific research recommendations to a larger region (roughly 2.4 million hectares of farm land in Eastern Arkansas), a set of soybean production parameters are varied in a biophysical simulation model using two representative soils and three climatic profiles. Mathematical programming was used to obtain site-specific, profit-maximizing production practices. Transfer of these optimal production practices to other environments resulted in operating losses up to $13.62 and $5.34 per hectare across soil and climatic region, respectively. This translated to annual estimated losses of $0.183–$3.877 million for the study region. Given the study specifications, guidelines for spatial replication of soybean research are (i) to provide recommendations using the poorer of the two soils and (ii) to give higher research priority to research replication across soils than climatic region. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Best management practices; Research transfer; Spatial research replication
*
Corresponding author. Tel.: +1-479-575-6838; fax: +1-479-575-5306. E-mail address: [email protected] (M.P. Popp).
0308-521X/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2003.11.003
214
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
1. Introduction In an effort to improve their profitability and/or reduce their risk exposure, agricultural producers are continually looking for new management techniques. To aid in this process, United States farmers often rely upon the research recommendations from, among others, their state agricultural experiment station and agricultural extension service to guide their decision making process. However, it is likely that there is a difference between research conditions that generate such recommendations and the production environment of the farmer. A review of recent studies on the economics of agricultural experiment station trials on soybean reveals several issues. First, spatial differences are not always reported across locations for various practices (Oriade et al., 1997; Dillon et al., 1997). More often than not, soil type and weather play a large role in the analyses, however, and thus potentially crucial information is missing. Second, study results on specific practices (i.e., seedbed preparation or pre-plant tillage) are not necessarily consistent across studies (Popp et al., 2000; Oriade et al., 1999; Manning et al., 2001). This may be a function of (i) the number of production practices controlled for in the study or its comprehensiveness from a whole farm decision making perspective and (ii) the number of years the study was conducted. Three, most controlled studies are not conducted at farm scale of production and thus research recommendations may need to be adjusted. Given these issues, advanced risk-return analysis utilizing generalized stochastic dominance (which assesses the risk versus return performance of alternative decisions relative to each other) for example, is often restricted to one location even when results are reported for another location(s) (Oriade et al., 1999; Popp et al., 2001b). Trials at the other locations may be terminated for lack of research funds, for one, and thus insufficient data exists to make data-intensive comparisons across production methods under different production conditions. Consequently, an intriguing question arises as to what kind of losses may be expected when transferring research recommendations to different settings. Further, it may be useful to identify the relative importance of factors that impact research transferability across climatic regions and soil type, so that research efforts may be targeted to be most productive. Some of the published research on transferability of agricultural research includes the transfer of input data such as solar radiation (Trent and Mckinion, 1987); durum wheat, bread wheat and barley variety research (Singh et al., 1996) and nitrogen and phosphorous fertilization of corn (Wood and Cady, 1981). The primary focus of these literature is on agronomic and statistical issues. Nonetheless, Henning and Eddleman (1986) studied the potential for soybean variety research transfer using cluster analysis. While Henning and Eddleman (1986) considered investment implications, their research also dealt with climatological input and yield issues. They reported a high potential for transfer of soybean variety research within a homogeneous subarea of the study region of Alabama, Arkansas, Louisiana, and Mississippi. This study looks at soybean research transfer issues within a sub-region of Arkansas (roughly 2.4 million hectares of suitable crop land commonly referred to as
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
215
Fig. 1. North, Central and Southern soybean production regions in Arkansas.
the Delta region of Arkansas) where soybean production is very prevalent (see Fig. 1). Results are expected to lead to guidelines on research replication at agricultural research experiment stations. Mathematical programming models were employed under constrained profit maximizing conditions to evaluate the portability of research recommendations under weather-dependent, simulated farm-scale conditions. The specific objectives of this region-specific study are (i) to provide insights into the potential opportunity costs associated with transferring research recommendations from one area or set of conditions to another under conditions of risk neutrality; (ii) to examine the relative importance of production factors that impact the portability of research recommendations across locations; and (iii) to provide some guidelines for experiment station directors to prioritize research expenditures towards soybean production. The research hypothesis is that location-specific soybean production recommendations do not affect yields/economic returns if applied at other similar locations.
2. Data and methods The discussion on data and methods starts with the delineation of resource conditions that face Arkansas Delta producers. The mathematical programming model
216
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
as well as required data inputs are discussed next. Part of these inputs are results of biophysical simulation. The section concludes with a description of calculation of losses from applying inappropriate recommendations both on a per hectare and regional basis. Given the background of economic studies discussed above, a case study of Arkansas soybean production at three different locations (agricultural experiment stations at Keiser, Rohwer, and Stuttgart) and two different prominent soil series (Sharkey Clay and Loring Silt Loam) is considered appropriate as the research framework needs to be manageable in terms of the number of resource conditions that are modeled. Climatic and soil differences were narrowed down to a broadly defined set albeit at the cost of potentially underestimating differences in resource conditions faced by producers. The locations are in three different climatic regions (mainly identified by differences in growing days with average temperatures above 50° F, Keisling et al., 1984) and two groups of soil series that are appropriate for dryland soybean production but differ in terms of texture (Keisling, T., University of Arkansas, personal communication, May 18, 2001). Soil types were aggregated using the Soil Survey Geographic (SSURGO) Data Base information provided by USDA, NRCS. In general, only loamy and clayey soils that had non-irrigated production classification codes less than or equal to 2 or 3, respectively, were included so as to remove soils that would not be suitable for dryland production. Further soils were classified into clayey soils as those with texture codes of clay (C), silty clay (SIC), and silty clay loam (SICL). Deep loamy soils are those with texture codes of loam (L), silty loam (SIL), sandy loam (SL), fine sandy loam (FSL), and very fine sandy loam (VFSL). Loamy sands (LS), loamy fine sands (LFS), fine sands (FS), and gravelly fine sands (GR-FSL) as well as land not in primary farm use were excluded from area totals. The Sharkey soil series are very common in all three regions but are most represented in the Northern region. Loring soils are very common in the central and northern regions and are most representative of the Central region. Table 1 shows a summary of land suitable for farming by weather region and soil texture to provide the starting point for making production recommendations. Arkansas Delta crop producers are thus faced with an array of these six alternative
Table 1 Arkansas Delta soil area composition by weather region Weather region
Climate station
North Central South
Keiser Stuttgart Rohwer
Clay
Total
Total (ha)
Soil (ha) Loam
455,206 366,396 260,050
394,726 657,149 233,215
849,932 1,023,545 493,265
1,081,652
1,285,090
2,366,742
Note. All land area numbers need to be adjusted to reflect that only approximately 30% of available land area is actually used for dryland soybean production in the region analyzed.
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
217
resource conditions for dryland soybean production according to weather and soil texture. As can be seen from the table, soil textures change from primarily clayey soils in the North and South to primarily loamy soils in the Central Region. The central region is also the largest, with the northern region in second place and the southern region last by a considerable margin. Arkansas Agricultural Statistics Service (AASS, 2003a) reports a state-wide, 10-year average of 781,000 ha in dryland production of which slightly over 90% (AASS, 2003b) were grown in the Arkansas Delta region. This amounts to approximately 30% of the land suitable for dryland soybean production listed in Table 1. The remaining 70% likely go to pasture/hay land, nonirrigated cotton as well as irrigated crop production. To simulate soybean production decisions that are within the realm of current Arkansas soybean production recommendations (Hill et al., 2003; Staff, 2000), a relatively large set of production parameters including planting date (varied on a weekly basis from May 10 through July 5), cultivar choice (maturity groups V and VI soybean associated with different planting date recommendations and crop time to maturity), and plant population (by changing row spacing and plants per foot in the row) are modeled using weather data over a forty year period (1960–1999) for each location/soil combination. While other parameters such as soil borne diseases, insects and weed pressure as well as tillage and weed control practices may impact the feasibility of research transfer, they could not be modeled using the biophysical simulator chosen for this study and are therefore left for additional consideration by the reader. Biophysical simulation was utilized to generate yield data and days suitable for field work given the various resource conditions. While use of models may show less variation in results than actual field trials, modeling is required to deal with the lack of available field trial data. Experimental field plots, for example, do not account for differential planting progress under different weather conditions. Typically a producer will require more time to perform operations compared to experimental plots as more land area is affected. In addition, experiment trials are not typically repeated for a sufficient number of years to truly estimate the impact of weather on yield results. Extensive validation of the biophysical simulation model for the Arkansas Delta was performed in the past with satisfactory results with the exception of overestimating yields which is typically corrected by applying an adjustment factor (Trice, 1986; Prickett, 1985). While soybean crop varieties continue to improve in terms of yield potential, the definitions of soil series are less variable. Calibration of the simulation model was not performed as the focus of modeling was not necessarily to arrive at realistic yields. Instead the focus of the research was on yield differences across production strategies with the assumption that yield prediction errors would be similar across production strategies and therefore errors in yield changes across strategies would be relatively insignificant. An economic decision-making model was employed to reflect the farmerÕs objective of maximizing net returns. A mixed integer programming model, an optimization technique using integer and continuous variables, was formulated to satisfy the following conditions:
218
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
MAX Y subject to: XXX M
P
S
M
P
S
M
P
S
M
P
S
XXX XXX XXX X
ð1Þ
XM;P ;S 6 243 8 WK
SOYLABP ;S;WK XM;P ;S 6 FLDDAYWK
EXPYLDM;P ;S;YR XM;P ;S SALESYR ¼ 0 REQI;P XM;P ;S PURCHI ¼ 0 8 I
IPI PURCHI P SALESYR þ YYR ¼ 0
8 YR
8 YR
ð2Þ ð3Þ ð4Þ ð5Þ
I
X 1 YYR Y ¼ 0 N YR X IR 6 1
ð6Þ ð7Þ
R
XX M
ROWTYPEP ;R XM;P ;S 100; 000IR 6 0
8R
ð8Þ
S
This model seeks to maximize Y , the expected net returns above variable cost (mean across net returns by year YYR ). Variable costs included seed, seed treatment, fertilizer, herbicides, fuel, oil, repairs, maintenance, custom operations, hired labor and interest on operating capital. The first constraint reflects the limit of land the farmer has available for production of soybeans with XM;P ;S representing production of maturity group M with a plant population P under sowing date S in hectares. Production also requires weekly labor SOYLABP;S;WK not to exceed FLDDAYWK or available field days per week (at the 75% level of certainty) under constraint 2. The third constraint reflects that SALESYR (kg of soybean sold per year) are equal to the amount produced as determined by hectares harvested and expected yield EXPYLDM;P;S . Constraint 4 models the need for various inputs I (REQI;P ) for each plant population and the fact that the correct amount must be purchased (PURCHI ). The fifth constraint calculates the net returns by year based on soybean price P (in dollars per kg less hauling costs) and input prices IPI . This allows constraint 6 to calculate the expected or average net returns across N years. Constraint 7 models the farmers choice to select only one row width to avoid changing equipment during planting by using IR , a binary ð0; 1Þ decision variable indicating choice of row spacing R. The final constraint (Eq. (8)) restricts production to the single row width selected based on ROWTYPEP;S , a row type indicator of row space R associated with row and plant spacing P . The maturity group is V or VI with the latter of the two more suited for later planting dates. There are eight row and plant spacing possibilities: rows spaced 23 cm apart with plants every 10 or 15 cm; rows spaced 48 cm apart with plants every 5 or 7.5 cm; rows spaced 76 cm apart with plants every 3.5 or 5 cm; and rows spaced
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
219
97 cm apart with plants every 2.5 or 4 cm. There are nine planting dates in weekly intervals beginning May 10 and ending July 5. The data required to specify the production decision model are: (i) available land; (ii) available field days; (iii) labor requirements; (iv) input requirements and prices; (v) soybean price; and (vi) yields. The hypothetical farm is assumed to be a commercial size operation with 243 hectares annually in dryland soybean production. A recent survey of county agents revealed average annual soybean production for typical farms in the study region to be approximately 324 ha (Hill et al., 2003). Average production did range from approximately 97 ha to nearly 526 ha. Note that only slightly more than half of this land will be non-irrigated. The number of suitable field days available per week was estimated using historical weather data and soil water simulation under a modified procedure discussed by Dillon et al. (1989). A 50% likelihood of a given number of field days occurring in any particular week was then specified as the labor constraint. Available field time was calculated by multiplying the average number of workable field days per week by 12 working hours per day for two people. The weekly number of days the tractor could work was calculated using a field days criteria function. The criteria used to identify a non-working day are (i) if it rained three consecutive days, the third day along with the following day was not considered a field day; (ii) if the soil moisture of the top 30 cm was 70% or greater of water storage capacity on a given day; and (iii) if it rained 0.4 cm or more on a given day. The soil moisture portion of the biophysical model was used to derive soil moisture. The vector of the available field days appeared as weekly right-hand side values in the mathematical programming model. The labor requirements per week and input costs and requirements per hectare were generated using budget data from Mississippi State Budget Generator (MSBG) (Spurlock, 1992). Sample budgets were constructed for each row and plant spacing using standard production practices (Keisling, T., University of Arkansas, personal communication, May 18, 2001; Windham and Marshall, 2003). The input prices used by MSBG were the 2001 estimates of prices from the Arkansas Cooperative Extension Service. The soybean price was the 1996–2000 Arkansas seasonal average of $0.219 kg1 (AASS, 2003a) less $0.006 kg1 hauling charge (Windham and Marshall, 2003). Hauling charges were deducted from the price because they are yield dependent. SOYGRO (Jones et al., 1989), a biophysical simulation model, was used to generate the yield data for the two maturity groups, eight plant populations, and nine planting dates. These production alternatives were chosen because they represent a reasonable span of production practices that are commonly encountered in the Arkansas Delta (Staff, 2000). Further, using this set of production practices in conjunction with planting progress dictated by weather and farm size, provides a more holistic set of production practices than a change in one production parameter as is often done in a controlled experiment. A summary of selected descriptive statistics for simulated crop yields under alternative planting dates for 1960 through 1999 weather conditions and constant technology is provided in Table 2. The data reflects the production strategies chosen in model solutions. Maturity group VI soybean was superior to maturity group V
220
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
Table 2 Simulated selected production statistics, resource and optimal production strategies by climate and soil texture Climate
Soil
SFD
Yields (kg ha1 )
Optimal planting date ranges Start
End
Mean
S.D.
Max
Min
Keiser
Clay Loam
5.22 5.33
Jun 28 Jun 21
Jul 5 Jun 28
1849 3067
1083 827
4190 4600
390 1009
Stuttgart
Clay Loam
5.50 5.50
Jun 28 Jun 28
Jul 5 Jul 5
1520 2724
1029 894
3759 3981
370 1217
Rohwer
Clay Loam
5.67 5.72
Jun 28 Jun 21
Jul 5 Jun 28
1533 2777
881 774
3363 4223
370 1029
Note. Average suitable field days per week (SFD) for the planting dates simulated (May 10–July 5).
soybean production and was the only maturity group selected in any of the model runs. High plant populations, resulting from narrow row production (23 cm apart) and plants every 15 cm, were also preferred across all resource conditions. The optimal planting date range represents the optimal starting and ending weeks for planting using the simulated historical weather data. Weather conditions did impact yields dramatically as yields ranged from less than 404 kg ha1 to about 4,573 kg ha1 depending on the production strategies and prevailing weather conditions. These yields are similar to results obtained at experiment stations according to expert opinion (Keisling, T., University of Arkansas, personal communication, May 18, 2001) but are higher than actual Arkansas Delta yields reported by the Arkansas Agricultural Statistics Service. This was as expected and may be related to overestimation of yields within the model and/or aforementioned factors not accounted for in the simulation model. Since only differences in yields across resource conditions are required for this analysis, however, these yield level errors are deemed inconsequential for this analysis. Suitable field days during planting were similar across all locations and lead to planting dates that started relatively late in the season. For loamy soils, optimal planting dates were one week earlier than for clayey soils at both Keiser and Rohwer. This was also the only difference in terms of production practice recommendations. Weather related impacts therefore appear somewhat minimal at least for planting considerations. Loamy soils were superior in terms of average yield as well as least absolute and relative risk at all locations. Given the biophysical simulation results, the mathematical programming model was used to determine average net returns for each location as well as optimal production practices. The model was then used to determine what net returns would be if each of the six climate and soil-specific production recommendations were applied at the other climate and soil combinations. Differences in net returns from the optimal and sub-optimal solutions would then be multiplied by the appropriate soil acreages in the North, South and Central regions. To do this, a necessary assumption was that the proportion of soybean production on suitable soils was the same across all climatic regions and soil types, i.e. 30% of the suitable acreage is used regardless of location. Also, the assumption is that fields need to be comprised of one primary
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
221
soil type in order for these calculations to be valid, i.e. a field might be 50% loam and 50% clay, in which case, the resultant yield impact or expected losses from applying the correct production practice may be biased. Since production recommendations only differ with respect to planting date (Table 2) this is not expected to be a major limitation.
3. Results and discussion Tables 3 and 4 present results of applying the six different planting recommendations across the six climate and soil combinations. Net returns above variable cost ranged from $66.91 to $395.98 ha1 . The diagonal in Table 3 describes the optimal returns achievable for each of the six climate and soil combinations. On clayey soils, returns ranged from $72.43 ha1 at Stuttgart to $140.53 ha1 at Keiser. Returns on Table 3 Net returns above variable costs in $ ha1 across climate and soil type Farmer condition
Recommendations from Keiser
Rohwer
Clay
Loam
Stuttgart
Clay
Loam
Clay
Loam
Keiser
Clay Loam
140.53 394.62
126.92 395.98
140.53 394.62
126.91 395.98
140.53 394.62
140.53 394.62
Rohwer
Clay Loam
75.14 335.39
66.91 335.71
75.14 335.39
66.91 335.71
75.14 335.39
75.14 335.39
Stuttgart
Clay Loam
72.43 324.07
69.48 318.73
72.43 324.07
69.48 318.73
72.43 324.07
72.43 324.07
Note. A typical farmer with Keiser climate and clayey soils employing recommendations generated for loamy soils at Keiser earns $126.92 ha1 (*) which amounts to $13.62 ha1 (** ) *) less than had he/she used recommendations for clayey soils. Table 4 Net returns above variable costs as a percent of optimal across climate and soil type Farmer condition
Recommendations from Keiser
Rohwer
Clay
Loam
Stuttgart
Clay
Loam
Clay
Loam
Keiser
Clay Loam
100.00 99.66
90.32 100.00
100.00 99.66
90.32 100.00
100.00 99.66
100.00 99.66
Rohwer
Clay Loam
100.00 99.90
89.04 100.00
100.00 99.90
89.04 100.00
100.00 99.90
100.00 99.90
Stuttgart
Clay Loam
100.00 100.00
95.93 98.35
100.00 100.00
95.93 98.35
100.00 100.00
100.00 100.00
Note. A typical farmer with Keiser climate and clayey soils employing recommendations generated for loamy soils at Keiser earns nearly 10% less (** ) *) than had he/she used recommendations for clayey soils.
222
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
Table 5 Net return loss implications of applying recommendations based on climate for each soil type Soil type ($/ha1 )
Recommendation condition combinations
Keiser @ Rohwer vs. Rohwer @ Rohwer Rohwer @ Keiser vs. Keiser @ Keiser Keiser @ Stuttgart vs. Stuttgart @ Stuttgart Stuttgart @ Keiser vs. Keiser @ Keiser Rohwer @ Stuttgart vs. Stuttgart @ Stuttgart Stuttgart @ Rohwer vs. Rohwer @ Rohwer
Clay
Loam
0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 5.34 1.36 5.34 0.32
Note. Differences in net returns observed by applying climate-specific recommendations at another location or climate but on the same soil type. An example for clay would be comparing Keiser recommendations under Rohwer conditions to optimal returns obtained using Rohwer recommendations under Rohwer conditions.
loamy soils were higher and ranged from a low of $324.07 ha1 at Stuttgart to a high of $395.98 ha1 at Keiser. To determine which of the two factors, climate or soil type, impacts returns more significantly, comparisons of returns across climate and soil type were isolated into two effects: (i) those generated by changes in climate – i.e. evaluating changes in returns observed by changing climate but not soil type and (ii) those generated by changes in soil type – i.e. evaluating changes in returns observed by changing soil type but not climate. Scenarios drawn from Table 3 can be used as examples for both effects. Suppose one wanted to obtain a climate effect. One could compare results of loam ðLÞ production recommendations from Stuttgart ðSÞ applied to Keiser ðKÞ. In this comparison one finds that returns for a farmer in Keiser who uses recommendations for Keiser (intersection of Keiser Loam row and Keiser Loam column) are $395.98; however, if this Keiser farmer uses recommendations from Stuttgart (intersection of Keiser Loam row and Stuttgart Loam column) returns fall to $394.62. Similarly, for a soil effect, if one wanted to compare returns in Keiser for recommendations for loam and clay soils one would find that when clay recommendations are used on clay soils (intersection of Keiser Clay row and Keiser Clay column) returns are $140.53; however when loam recommendations are used on clay soils (intersection of Keiser Clay row and Keiser Loam column) returns fall to $126.92. The effects of climate and soil type differences are summarized in Tables 5 and 6, respectively. Differences due to climate are irrelevant on clay soils since the production practice recommendations on clay soils are the same regardless of location (see Table 2). On loamy soils, climate differences are somewhat larger and also differ in size depending on which recommendations are adopted, i.e. applying Stuttgart recommendations to Keiser ($1.36 ha1 ) or Rohwer ($0.32 ha1 ) is less detrimental than applying Keiser or Rohwer recommendations to Stuttgart ($5.34 ha1 ) – see column entitled ÔLoamÕ for a summary of return differences across locations in Table 5. These differences are a result of changing planting date recommendations across climates on loamy soils. For an average size farm (243 ha) this translates to a change in returns of $77.76–$1297.62.
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
223
Table 6 Net return loss implications of applying recommendations based on soil type for each climate Soil differences ($/ha1 )
Climate
Keiser Rohwer Stuttgart
Clay on loam
Loam on clay
1.36 0.32 0.00
13.62 8.23 0.00
Note. Differences in net returns observed by applying soil-specific recommendations on another soil under same climatic conditions. An example at Keiser would be comparing clay recommendations on loam to optimal returns obtained using loam recommendations on loam. For each climate, clay on loam stands for applying clay recommendations on loamy soils instead of the appropriate loam recommendations and loam on clay stands for applying loam recommendations on clayey soils instead of the appropriate clay recommendations.
Soil type differences (Table 6) are climate-specific and appear more dramatic at Keiser than Rohwer. No soil type effect is apparent at Stuttgart as planting date and suitable field days are the same for both loam and clay soils (see Table 2). Table 6 also suggests that the direction of soil change is important (Loam on Clay vs. Clay on Loam). Application of recommendations from the poor performing clay soils to the better performing loam soils (see Table 2 and Clay on Loam column in Table 6) is less consequential than applying loam recommendations to clay soils ( Loam on Clay column in Table 6). A comparison with recent published experimental data revealed that statistically significant differences across space are difficult to determine in most empirical studies as study periods often do not match and include only a few years of data. Of the studies reviewed for this paper, statistically significant spatial differences were due to tillage practices as a result of soil characteristics and ranged up to $309 ha1 (Popp et al., 2001a). A study on maturity group selection reported statistically significant net return differences as large as $99 ha1 but included MG IV in the choice set (Popp et al., 2001b). No significant planting date differences were found. Overall, these results suggest that soil type differences are more significant than climatic differences. This becomes especially apparent when summarizing per hectare location and soil type impacts across production regions. Table 7 summarizes the impact of applying inappropriate recommendations across production regions with the Table 7 Total estimated annual losses associated with climate and soil-specific research Recommendations from Keiser
Rohwer
Stuttgart
Clay
Loam
Clay
Loam
Clay
Loam
Soil type losses ($) Climate losses ($)
183 –
2501 1375
183 –
2501 1375
183 –
– 183
Total loss ($)
183
3877
183
3877
183
183
224
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
assumption that research recommendations would be adopted by all producers in the region. Employing Keiser clay recommendations leads to losses in returns at Keiser on loam soils and also loamy soils at Rohwer. Since there is no climate effect on clay soils, both losses are due to restrictions of soil type (i.e., applying clay recommendations on loam soils). In this case there are approximately 30% of 394,726 hectares of loam used for dryland soybean production in the Northern region with a loss of $1.36 ha1 . In addition to those losses there are 233,215 hectares of loamy soils in the Southern region where transfer of recommendations across soil type leads to $0.32 ha1 losses. Employing Keiser loam recommendations leads to losses in returns on Keiser clay, Rohwer clay, Stuttgart clay and Stuttgart loam. The latter two, Stuttgart clay and loam are classified as climate losses. Applying Keiser loam conditions on Stuttgart loam lead to a loss of $5.34 ha1 . Applying Keiser loam conditions on Stuttgart clay leads to a $2.94 ha1 loss which is wholly attributable to climate differences as Stuttgart loam recommendations on Stuttgart clay lead to no losses. Again, these losses per acre are multiplied by the appropriate land area in dryland soybean production. While the per hectare impact is relatively small (Tables 5 and 6), mainly because of the relatively small change in production practices (Table 2), Table 7 indicates that the choice of research location and soil type could lead to large losses at the regional level. Compared to the losses listed in Table 7, spending an additional $5000–$20,000 for replication of research across soils and climatic region seems appropriate even if new recommendations based on the research are adopted by only a small fraction of regional producers. Impacts of applying loam recommendations from either Rohwer or Keiser across the Arkansas Delta lead to estimated potential annual losses of nearly $3.9 million. Losses from applying recommendations to the wrong soil type are again larger than losses resulting from differences in weather conditions across production regions. Further, the results suggest that the research ought to be done on the poorer of the two soil types (recall that clay had poorer performance statistics than loam soils) as clay recommendations (ÔKeiser ClayÕ and ÔRowher ClayÕ columns in Table 7) lead to lower overall losses compared to loam recommendations (ÔKeiser LoamÕ and ÔRohwer LoamÕ columns in Table 7). The central Stuttgart research location offers the least potential regional losses from research transfer as soil type and climate losses are minimized.
4. Conclusions This study attempted to delineate the potential loss from applying soybean research recommendations from a single location/climate and soil type to other locations/climates and soil types in the Arkansas Delta. Biophysical simulation was used in conjunction with three sets of weather data and two soil series argued to be representative of the production region. Optimal soybean planting strategies were developed by varying planting date, row spacing, seeding rate in the row, and seed maturity group. While the study shows some potentially large implications of apply-
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
225
ing research recommendations from one climate/soil type combination to the rest of the production region, overall results suggest that, given the focus of this study, the potential for loss is relatively small in four of six cases on an annual basis. Given even these relatively small findings, the lowest loss estimate of $183,000 would likely be sufficient to cover the marginal (additional) costs of at least one additional experimental site. Recommendations to experiment station directors, faced with prioritizing research on topics similar to the conditions outlined in this study, would be to conduct research on the poorer clayey soils regardless of location as potential losses from soil type restrictions were the greatest if loamy recommendations were applied on clay. Further, as additional resources are available, experimental research should first be expanded to different soil types than to different climatic regions in the state of Arkansas. However, these study results need to be regarded with caution as the set of ‘‘reasonable’’ production parameters chosen (i.e., representative soil series, weather data, production parameters) may not necessarily be the practices researchers are expected to evaluate. Perhaps planting date recommendations on maturity groups 0 through IV would lead to much different planting date and production recommendations across regions and thus larger potential research transfer losses. Finally, if a researcher was interested in making more generalizable recommendations across crops and production practices, further research would be required to analyze similar decisions for other crops or even livestock and a greater list of parameters associated with soybean production than modeled here. While not attempted here, further research in this area may be of interest to experiment station directors in need of allocation guidelines for future research.
Acknowledgements This research was funded through the support of the Arkansas Soybean Promotion Board and the Arkansas Agricultural Experiment Station. The authors wish to acknowledge the agronomic expertise of Dr. Terry Keisling and the help on managing the soil series definitions and soils data base from Amanda Aescoba. Finally the authors are also grateful for the help of Patrick Manning.
References Arkansas Agricultural Statistics Service, 2003a. Soybeans: acreage, yield, production, price and value. Available from. Arkansas Agricultural Statistiacs Service, 2003b. Soybeans: Non-Irrigated Acreage, Yield and Production by Counties, 1999–2000. Available from . Dillon, C.R., Mjelde, J.W., McCarl, B.A., 1989. Biophysical simulation in support of crop production decisions: a case study in the Blacklands region of Texas. South. J. Agric. Econ. 21, 73–86. Dillon, C.R., Keisling, T.C., Riggs, R.D., Oliver, L.R., 1997. Profit potential of soybean production rotation systems in Arkansas. Comm. Soil Sci. Plant Anal. 28, 1693–1709.
226
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
Henning, S.A., Eddleman, B.R., 1986. Intra- and inter-state transferability of soybean variety research. South. J. Agric. Econ. 18, 7–13. Hill, J., Popp, M., Manning, P., 2003. Focus group survey results: typical Arkansas crop producer production and marketing practices. Research Report 971. University of Arkansas Experiment Station, Fayetteville. Jones, J.W., Boote, K.J., Hoogenboom, G., Jagtap, S.S., Wilkerson, G.G., 1989. Soybean crop growth simulation Model v5.42. UserÕs Guide. Journal No. 8304. Agricultural Experiment Station, University of Florida, Gainesville. Keisling, T.C., Wells, B.R., Davis, G.L., 1984. Rice management decision aids based upon thermal time base 50° F. Computer Technical Bulletin 1. University of Arkansas, Cooperative Extension Service, Little Rock. Manning, P., Keisling, T., Popp, M., Oliver, L., 2001. Evaluation of row-spacing, seedbed preparation, and weed control options for dryland soybean. Comm. Soil Sci. Plant Anal. 32, 1899–1913. Oriade, C.A., Dillon, C.R., Keisling, T.C., 1999. Economics of wheat residue management in doublecrop soybean. J. Prod. Agric. 12, 42–48. Oriade, C., Dillon, C.R., Vories, E.D., Bohanan, M.E., 1997. An economic analysis of alternative cropping and row spacing systems for soybean production. J. Prod. Agric. 10, 619–624. Popp, M., Keisling, T., Dillon, C., Manning, P., 2001a. Economic and agronomic assessment of deep tillage in soybean production on Mississippi river valley soils. Agron. J. 93, 164–169. Popp, M., Keisling, T., Oliver, L., Dillon, C., Manning, P., 2001b. Analysis of seedbeds and maturity groups for dryland soybean on clayey soil. Agron. J. 93, 827–835. Popp, M., Oliver, L., Dillon, C., Keisling, T., Manning, P., 2000. Evaluation of seed bed preparations, planting method and herbicide alternatives for dryland soybean production. Agron. J. 92, 1149–1155. Prickett, M., 1985. Irrigation scheduling for Arkansas soybean production. M.Sc. Thesis. University of Arkansas, Fayetteville. Singh, M., Yau, S.K., Hamblin, J., Porceddu, E., 1996. Inter-site transferability of crop varieties: another approach for analyzing multi-locational variety trials. Euphytica 89, 305–311. Spurlock, R., 1992. Mississippi State University Budget Generator. Technical Bulletin No. 52. Mississippi State. University, Agricultural Experiment Station, Mississippi State. Staff, University of Arkansas Division of Agriculture., 2000. Arkansas Soybean Handbook. MP197. University of Arkansas, Cooperative Extension Service, Little Rock. Trent, A., McKinion, J.M., 1987. Determining transferability of solar radiation data. Agron. J. 79, 124– 129. Trice, K., 1986. The economics of double cropping wheat and soybeans: a simulation analysis using WHEATSOY. M.Sc. Thesis. University of Arkansas, Fayetteville. Windham, T., Marshall, J., 2003. Soybeans, stale seedbed, non-irrigated, roundup ready, clay and mixed soils. Enterprise Budget AG-739. University of Arkansas, Cooperative Extension Service, Little Rock. Wood, C.L., Cady, F.B., 1981. Intersite transfer of estimated response surfaces. Biometrics 37, 1–10.
Transferability issues of soybean research: a regional example Michael P. Popp
a,*
, Carl R. Dillon b, Jennie S. Popp
a
a
b
Department of Agricultural Economics and Agribusiness, University of Arkansas, 217 Agriculture Building, Fayetteville, AR 72701, USA Department of Agricultural Economics, University of Kentucky, 403 C.E. Barnhart Building, Lexington, KY 40546, USA
Received 19 March 2003; received in revised form 24 October 2003; accepted 11 November 2003
Abstract Agricultural research monies are becoming increasingly scarce and therefore subject to additional scrutiny. To analyze the impact of transferring site-specific research recommendations to a larger region (roughly 2.4 million hectares of farm land in Eastern Arkansas), a set of soybean production parameters are varied in a biophysical simulation model using two representative soils and three climatic profiles. Mathematical programming was used to obtain site-specific, profit-maximizing production practices. Transfer of these optimal production practices to other environments resulted in operating losses up to $13.62 and $5.34 per hectare across soil and climatic region, respectively. This translated to annual estimated losses of $0.183–$3.877 million for the study region. Given the study specifications, guidelines for spatial replication of soybean research are (i) to provide recommendations using the poorer of the two soils and (ii) to give higher research priority to research replication across soils than climatic region. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Best management practices; Research transfer; Spatial research replication
*
Corresponding author. Tel.: +1-479-575-6838; fax: +1-479-575-5306. E-mail address: [email protected] (M.P. Popp).
0308-521X/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2003.11.003
214
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
1. Introduction In an effort to improve their profitability and/or reduce their risk exposure, agricultural producers are continually looking for new management techniques. To aid in this process, United States farmers often rely upon the research recommendations from, among others, their state agricultural experiment station and agricultural extension service to guide their decision making process. However, it is likely that there is a difference between research conditions that generate such recommendations and the production environment of the farmer. A review of recent studies on the economics of agricultural experiment station trials on soybean reveals several issues. First, spatial differences are not always reported across locations for various practices (Oriade et al., 1997; Dillon et al., 1997). More often than not, soil type and weather play a large role in the analyses, however, and thus potentially crucial information is missing. Second, study results on specific practices (i.e., seedbed preparation or pre-plant tillage) are not necessarily consistent across studies (Popp et al., 2000; Oriade et al., 1999; Manning et al., 2001). This may be a function of (i) the number of production practices controlled for in the study or its comprehensiveness from a whole farm decision making perspective and (ii) the number of years the study was conducted. Three, most controlled studies are not conducted at farm scale of production and thus research recommendations may need to be adjusted. Given these issues, advanced risk-return analysis utilizing generalized stochastic dominance (which assesses the risk versus return performance of alternative decisions relative to each other) for example, is often restricted to one location even when results are reported for another location(s) (Oriade et al., 1999; Popp et al., 2001b). Trials at the other locations may be terminated for lack of research funds, for one, and thus insufficient data exists to make data-intensive comparisons across production methods under different production conditions. Consequently, an intriguing question arises as to what kind of losses may be expected when transferring research recommendations to different settings. Further, it may be useful to identify the relative importance of factors that impact research transferability across climatic regions and soil type, so that research efforts may be targeted to be most productive. Some of the published research on transferability of agricultural research includes the transfer of input data such as solar radiation (Trent and Mckinion, 1987); durum wheat, bread wheat and barley variety research (Singh et al., 1996) and nitrogen and phosphorous fertilization of corn (Wood and Cady, 1981). The primary focus of these literature is on agronomic and statistical issues. Nonetheless, Henning and Eddleman (1986) studied the potential for soybean variety research transfer using cluster analysis. While Henning and Eddleman (1986) considered investment implications, their research also dealt with climatological input and yield issues. They reported a high potential for transfer of soybean variety research within a homogeneous subarea of the study region of Alabama, Arkansas, Louisiana, and Mississippi. This study looks at soybean research transfer issues within a sub-region of Arkansas (roughly 2.4 million hectares of suitable crop land commonly referred to as
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
215
Fig. 1. North, Central and Southern soybean production regions in Arkansas.
the Delta region of Arkansas) where soybean production is very prevalent (see Fig. 1). Results are expected to lead to guidelines on research replication at agricultural research experiment stations. Mathematical programming models were employed under constrained profit maximizing conditions to evaluate the portability of research recommendations under weather-dependent, simulated farm-scale conditions. The specific objectives of this region-specific study are (i) to provide insights into the potential opportunity costs associated with transferring research recommendations from one area or set of conditions to another under conditions of risk neutrality; (ii) to examine the relative importance of production factors that impact the portability of research recommendations across locations; and (iii) to provide some guidelines for experiment station directors to prioritize research expenditures towards soybean production. The research hypothesis is that location-specific soybean production recommendations do not affect yields/economic returns if applied at other similar locations.
2. Data and methods The discussion on data and methods starts with the delineation of resource conditions that face Arkansas Delta producers. The mathematical programming model
216
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
as well as required data inputs are discussed next. Part of these inputs are results of biophysical simulation. The section concludes with a description of calculation of losses from applying inappropriate recommendations both on a per hectare and regional basis. Given the background of economic studies discussed above, a case study of Arkansas soybean production at three different locations (agricultural experiment stations at Keiser, Rohwer, and Stuttgart) and two different prominent soil series (Sharkey Clay and Loring Silt Loam) is considered appropriate as the research framework needs to be manageable in terms of the number of resource conditions that are modeled. Climatic and soil differences were narrowed down to a broadly defined set albeit at the cost of potentially underestimating differences in resource conditions faced by producers. The locations are in three different climatic regions (mainly identified by differences in growing days with average temperatures above 50° F, Keisling et al., 1984) and two groups of soil series that are appropriate for dryland soybean production but differ in terms of texture (Keisling, T., University of Arkansas, personal communication, May 18, 2001). Soil types were aggregated using the Soil Survey Geographic (SSURGO) Data Base information provided by USDA, NRCS. In general, only loamy and clayey soils that had non-irrigated production classification codes less than or equal to 2 or 3, respectively, were included so as to remove soils that would not be suitable for dryland production. Further soils were classified into clayey soils as those with texture codes of clay (C), silty clay (SIC), and silty clay loam (SICL). Deep loamy soils are those with texture codes of loam (L), silty loam (SIL), sandy loam (SL), fine sandy loam (FSL), and very fine sandy loam (VFSL). Loamy sands (LS), loamy fine sands (LFS), fine sands (FS), and gravelly fine sands (GR-FSL) as well as land not in primary farm use were excluded from area totals. The Sharkey soil series are very common in all three regions but are most represented in the Northern region. Loring soils are very common in the central and northern regions and are most representative of the Central region. Table 1 shows a summary of land suitable for farming by weather region and soil texture to provide the starting point for making production recommendations. Arkansas Delta crop producers are thus faced with an array of these six alternative
Table 1 Arkansas Delta soil area composition by weather region Weather region
Climate station
North Central South
Keiser Stuttgart Rohwer
Clay
Total
Total (ha)
Soil (ha) Loam
455,206 366,396 260,050
394,726 657,149 233,215
849,932 1,023,545 493,265
1,081,652
1,285,090
2,366,742
Note. All land area numbers need to be adjusted to reflect that only approximately 30% of available land area is actually used for dryland soybean production in the region analyzed.
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
217
resource conditions for dryland soybean production according to weather and soil texture. As can be seen from the table, soil textures change from primarily clayey soils in the North and South to primarily loamy soils in the Central Region. The central region is also the largest, with the northern region in second place and the southern region last by a considerable margin. Arkansas Agricultural Statistics Service (AASS, 2003a) reports a state-wide, 10-year average of 781,000 ha in dryland production of which slightly over 90% (AASS, 2003b) were grown in the Arkansas Delta region. This amounts to approximately 30% of the land suitable for dryland soybean production listed in Table 1. The remaining 70% likely go to pasture/hay land, nonirrigated cotton as well as irrigated crop production. To simulate soybean production decisions that are within the realm of current Arkansas soybean production recommendations (Hill et al., 2003; Staff, 2000), a relatively large set of production parameters including planting date (varied on a weekly basis from May 10 through July 5), cultivar choice (maturity groups V and VI soybean associated with different planting date recommendations and crop time to maturity), and plant population (by changing row spacing and plants per foot in the row) are modeled using weather data over a forty year period (1960–1999) for each location/soil combination. While other parameters such as soil borne diseases, insects and weed pressure as well as tillage and weed control practices may impact the feasibility of research transfer, they could not be modeled using the biophysical simulator chosen for this study and are therefore left for additional consideration by the reader. Biophysical simulation was utilized to generate yield data and days suitable for field work given the various resource conditions. While use of models may show less variation in results than actual field trials, modeling is required to deal with the lack of available field trial data. Experimental field plots, for example, do not account for differential planting progress under different weather conditions. Typically a producer will require more time to perform operations compared to experimental plots as more land area is affected. In addition, experiment trials are not typically repeated for a sufficient number of years to truly estimate the impact of weather on yield results. Extensive validation of the biophysical simulation model for the Arkansas Delta was performed in the past with satisfactory results with the exception of overestimating yields which is typically corrected by applying an adjustment factor (Trice, 1986; Prickett, 1985). While soybean crop varieties continue to improve in terms of yield potential, the definitions of soil series are less variable. Calibration of the simulation model was not performed as the focus of modeling was not necessarily to arrive at realistic yields. Instead the focus of the research was on yield differences across production strategies with the assumption that yield prediction errors would be similar across production strategies and therefore errors in yield changes across strategies would be relatively insignificant. An economic decision-making model was employed to reflect the farmerÕs objective of maximizing net returns. A mixed integer programming model, an optimization technique using integer and continuous variables, was formulated to satisfy the following conditions:
218
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
MAX Y subject to: XXX M
P
S
M
P
S
M
P
S
M
P
S
XXX XXX XXX X
ð1Þ
XM;P ;S 6 243 8 WK
SOYLABP ;S;WK XM;P ;S 6 FLDDAYWK
EXPYLDM;P ;S;YR XM;P ;S SALESYR ¼ 0 REQI;P XM;P ;S PURCHI ¼ 0 8 I
IPI PURCHI P SALESYR þ YYR ¼ 0
8 YR
8 YR
ð2Þ ð3Þ ð4Þ ð5Þ
I
X 1 YYR Y ¼ 0 N YR X IR 6 1
ð6Þ ð7Þ
R
XX M
ROWTYPEP ;R XM;P ;S 100; 000IR 6 0
8R
ð8Þ
S
This model seeks to maximize Y , the expected net returns above variable cost (mean across net returns by year YYR ). Variable costs included seed, seed treatment, fertilizer, herbicides, fuel, oil, repairs, maintenance, custom operations, hired labor and interest on operating capital. The first constraint reflects the limit of land the farmer has available for production of soybeans with XM;P ;S representing production of maturity group M with a plant population P under sowing date S in hectares. Production also requires weekly labor SOYLABP;S;WK not to exceed FLDDAYWK or available field days per week (at the 75% level of certainty) under constraint 2. The third constraint reflects that SALESYR (kg of soybean sold per year) are equal to the amount produced as determined by hectares harvested and expected yield EXPYLDM;P;S . Constraint 4 models the need for various inputs I (REQI;P ) for each plant population and the fact that the correct amount must be purchased (PURCHI ). The fifth constraint calculates the net returns by year based on soybean price P (in dollars per kg less hauling costs) and input prices IPI . This allows constraint 6 to calculate the expected or average net returns across N years. Constraint 7 models the farmers choice to select only one row width to avoid changing equipment during planting by using IR , a binary ð0; 1Þ decision variable indicating choice of row spacing R. The final constraint (Eq. (8)) restricts production to the single row width selected based on ROWTYPEP;S , a row type indicator of row space R associated with row and plant spacing P . The maturity group is V or VI with the latter of the two more suited for later planting dates. There are eight row and plant spacing possibilities: rows spaced 23 cm apart with plants every 10 or 15 cm; rows spaced 48 cm apart with plants every 5 or 7.5 cm; rows spaced 76 cm apart with plants every 3.5 or 5 cm; and rows spaced
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
219
97 cm apart with plants every 2.5 or 4 cm. There are nine planting dates in weekly intervals beginning May 10 and ending July 5. The data required to specify the production decision model are: (i) available land; (ii) available field days; (iii) labor requirements; (iv) input requirements and prices; (v) soybean price; and (vi) yields. The hypothetical farm is assumed to be a commercial size operation with 243 hectares annually in dryland soybean production. A recent survey of county agents revealed average annual soybean production for typical farms in the study region to be approximately 324 ha (Hill et al., 2003). Average production did range from approximately 97 ha to nearly 526 ha. Note that only slightly more than half of this land will be non-irrigated. The number of suitable field days available per week was estimated using historical weather data and soil water simulation under a modified procedure discussed by Dillon et al. (1989). A 50% likelihood of a given number of field days occurring in any particular week was then specified as the labor constraint. Available field time was calculated by multiplying the average number of workable field days per week by 12 working hours per day for two people. The weekly number of days the tractor could work was calculated using a field days criteria function. The criteria used to identify a non-working day are (i) if it rained three consecutive days, the third day along with the following day was not considered a field day; (ii) if the soil moisture of the top 30 cm was 70% or greater of water storage capacity on a given day; and (iii) if it rained 0.4 cm or more on a given day. The soil moisture portion of the biophysical model was used to derive soil moisture. The vector of the available field days appeared as weekly right-hand side values in the mathematical programming model. The labor requirements per week and input costs and requirements per hectare were generated using budget data from Mississippi State Budget Generator (MSBG) (Spurlock, 1992). Sample budgets were constructed for each row and plant spacing using standard production practices (Keisling, T., University of Arkansas, personal communication, May 18, 2001; Windham and Marshall, 2003). The input prices used by MSBG were the 2001 estimates of prices from the Arkansas Cooperative Extension Service. The soybean price was the 1996–2000 Arkansas seasonal average of $0.219 kg1 (AASS, 2003a) less $0.006 kg1 hauling charge (Windham and Marshall, 2003). Hauling charges were deducted from the price because they are yield dependent. SOYGRO (Jones et al., 1989), a biophysical simulation model, was used to generate the yield data for the two maturity groups, eight plant populations, and nine planting dates. These production alternatives were chosen because they represent a reasonable span of production practices that are commonly encountered in the Arkansas Delta (Staff, 2000). Further, using this set of production practices in conjunction with planting progress dictated by weather and farm size, provides a more holistic set of production practices than a change in one production parameter as is often done in a controlled experiment. A summary of selected descriptive statistics for simulated crop yields under alternative planting dates for 1960 through 1999 weather conditions and constant technology is provided in Table 2. The data reflects the production strategies chosen in model solutions. Maturity group VI soybean was superior to maturity group V
220
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
Table 2 Simulated selected production statistics, resource and optimal production strategies by climate and soil texture Climate
Soil
SFD
Yields (kg ha1 )
Optimal planting date ranges Start
End
Mean
S.D.
Max
Min
Keiser
Clay Loam
5.22 5.33
Jun 28 Jun 21
Jul 5 Jun 28
1849 3067
1083 827
4190 4600
390 1009
Stuttgart
Clay Loam
5.50 5.50
Jun 28 Jun 28
Jul 5 Jul 5
1520 2724
1029 894
3759 3981
370 1217
Rohwer
Clay Loam
5.67 5.72
Jun 28 Jun 21
Jul 5 Jun 28
1533 2777
881 774
3363 4223
370 1029
Note. Average suitable field days per week (SFD) for the planting dates simulated (May 10–July 5).
soybean production and was the only maturity group selected in any of the model runs. High plant populations, resulting from narrow row production (23 cm apart) and plants every 15 cm, were also preferred across all resource conditions. The optimal planting date range represents the optimal starting and ending weeks for planting using the simulated historical weather data. Weather conditions did impact yields dramatically as yields ranged from less than 404 kg ha1 to about 4,573 kg ha1 depending on the production strategies and prevailing weather conditions. These yields are similar to results obtained at experiment stations according to expert opinion (Keisling, T., University of Arkansas, personal communication, May 18, 2001) but are higher than actual Arkansas Delta yields reported by the Arkansas Agricultural Statistics Service. This was as expected and may be related to overestimation of yields within the model and/or aforementioned factors not accounted for in the simulation model. Since only differences in yields across resource conditions are required for this analysis, however, these yield level errors are deemed inconsequential for this analysis. Suitable field days during planting were similar across all locations and lead to planting dates that started relatively late in the season. For loamy soils, optimal planting dates were one week earlier than for clayey soils at both Keiser and Rohwer. This was also the only difference in terms of production practice recommendations. Weather related impacts therefore appear somewhat minimal at least for planting considerations. Loamy soils were superior in terms of average yield as well as least absolute and relative risk at all locations. Given the biophysical simulation results, the mathematical programming model was used to determine average net returns for each location as well as optimal production practices. The model was then used to determine what net returns would be if each of the six climate and soil-specific production recommendations were applied at the other climate and soil combinations. Differences in net returns from the optimal and sub-optimal solutions would then be multiplied by the appropriate soil acreages in the North, South and Central regions. To do this, a necessary assumption was that the proportion of soybean production on suitable soils was the same across all climatic regions and soil types, i.e. 30% of the suitable acreage is used regardless of location. Also, the assumption is that fields need to be comprised of one primary
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
221
soil type in order for these calculations to be valid, i.e. a field might be 50% loam and 50% clay, in which case, the resultant yield impact or expected losses from applying the correct production practice may be biased. Since production recommendations only differ with respect to planting date (Table 2) this is not expected to be a major limitation.
3. Results and discussion Tables 3 and 4 present results of applying the six different planting recommendations across the six climate and soil combinations. Net returns above variable cost ranged from $66.91 to $395.98 ha1 . The diagonal in Table 3 describes the optimal returns achievable for each of the six climate and soil combinations. On clayey soils, returns ranged from $72.43 ha1 at Stuttgart to $140.53 ha1 at Keiser. Returns on Table 3 Net returns above variable costs in $ ha1 across climate and soil type Farmer condition
Recommendations from Keiser
Rohwer
Clay
Loam
Stuttgart
Clay
Loam
Clay
Loam
Keiser
Clay Loam
140.53 394.62
126.92 395.98
140.53 394.62
126.91 395.98
140.53 394.62
140.53 394.62
Rohwer
Clay Loam
75.14 335.39
66.91 335.71
75.14 335.39
66.91 335.71
75.14 335.39
75.14 335.39
Stuttgart
Clay Loam
72.43 324.07
69.48 318.73
72.43 324.07
69.48 318.73
72.43 324.07
72.43 324.07
Note. A typical farmer with Keiser climate and clayey soils employing recommendations generated for loamy soils at Keiser earns $126.92 ha1 (*) which amounts to $13.62 ha1 (** ) *) less than had he/she used recommendations for clayey soils. Table 4 Net returns above variable costs as a percent of optimal across climate and soil type Farmer condition
Recommendations from Keiser
Rohwer
Clay
Loam
Stuttgart
Clay
Loam
Clay
Loam
Keiser
Clay Loam
100.00 99.66
90.32 100.00
100.00 99.66
90.32 100.00
100.00 99.66
100.00 99.66
Rohwer
Clay Loam
100.00 99.90
89.04 100.00
100.00 99.90
89.04 100.00
100.00 99.90
100.00 99.90
Stuttgart
Clay Loam
100.00 100.00
95.93 98.35
100.00 100.00
95.93 98.35
100.00 100.00
100.00 100.00
Note. A typical farmer with Keiser climate and clayey soils employing recommendations generated for loamy soils at Keiser earns nearly 10% less (** ) *) than had he/she used recommendations for clayey soils.
222
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
Table 5 Net return loss implications of applying recommendations based on climate for each soil type Soil type ($/ha1 )
Recommendation condition combinations
Keiser @ Rohwer vs. Rohwer @ Rohwer Rohwer @ Keiser vs. Keiser @ Keiser Keiser @ Stuttgart vs. Stuttgart @ Stuttgart Stuttgart @ Keiser vs. Keiser @ Keiser Rohwer @ Stuttgart vs. Stuttgart @ Stuttgart Stuttgart @ Rohwer vs. Rohwer @ Rohwer
Clay
Loam
0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 5.34 1.36 5.34 0.32
Note. Differences in net returns observed by applying climate-specific recommendations at another location or climate but on the same soil type. An example for clay would be comparing Keiser recommendations under Rohwer conditions to optimal returns obtained using Rohwer recommendations under Rohwer conditions.
loamy soils were higher and ranged from a low of $324.07 ha1 at Stuttgart to a high of $395.98 ha1 at Keiser. To determine which of the two factors, climate or soil type, impacts returns more significantly, comparisons of returns across climate and soil type were isolated into two effects: (i) those generated by changes in climate – i.e. evaluating changes in returns observed by changing climate but not soil type and (ii) those generated by changes in soil type – i.e. evaluating changes in returns observed by changing soil type but not climate. Scenarios drawn from Table 3 can be used as examples for both effects. Suppose one wanted to obtain a climate effect. One could compare results of loam ðLÞ production recommendations from Stuttgart ðSÞ applied to Keiser ðKÞ. In this comparison one finds that returns for a farmer in Keiser who uses recommendations for Keiser (intersection of Keiser Loam row and Keiser Loam column) are $395.98; however, if this Keiser farmer uses recommendations from Stuttgart (intersection of Keiser Loam row and Stuttgart Loam column) returns fall to $394.62. Similarly, for a soil effect, if one wanted to compare returns in Keiser for recommendations for loam and clay soils one would find that when clay recommendations are used on clay soils (intersection of Keiser Clay row and Keiser Clay column) returns are $140.53; however when loam recommendations are used on clay soils (intersection of Keiser Clay row and Keiser Loam column) returns fall to $126.92. The effects of climate and soil type differences are summarized in Tables 5 and 6, respectively. Differences due to climate are irrelevant on clay soils since the production practice recommendations on clay soils are the same regardless of location (see Table 2). On loamy soils, climate differences are somewhat larger and also differ in size depending on which recommendations are adopted, i.e. applying Stuttgart recommendations to Keiser ($1.36 ha1 ) or Rohwer ($0.32 ha1 ) is less detrimental than applying Keiser or Rohwer recommendations to Stuttgart ($5.34 ha1 ) – see column entitled ÔLoamÕ for a summary of return differences across locations in Table 5. These differences are a result of changing planting date recommendations across climates on loamy soils. For an average size farm (243 ha) this translates to a change in returns of $77.76–$1297.62.
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
223
Table 6 Net return loss implications of applying recommendations based on soil type for each climate Soil differences ($/ha1 )
Climate
Keiser Rohwer Stuttgart
Clay on loam
Loam on clay
1.36 0.32 0.00
13.62 8.23 0.00
Note. Differences in net returns observed by applying soil-specific recommendations on another soil under same climatic conditions. An example at Keiser would be comparing clay recommendations on loam to optimal returns obtained using loam recommendations on loam. For each climate, clay on loam stands for applying clay recommendations on loamy soils instead of the appropriate loam recommendations and loam on clay stands for applying loam recommendations on clayey soils instead of the appropriate clay recommendations.
Soil type differences (Table 6) are climate-specific and appear more dramatic at Keiser than Rohwer. No soil type effect is apparent at Stuttgart as planting date and suitable field days are the same for both loam and clay soils (see Table 2). Table 6 also suggests that the direction of soil change is important (Loam on Clay vs. Clay on Loam). Application of recommendations from the poor performing clay soils to the better performing loam soils (see Table 2 and Clay on Loam column in Table 6) is less consequential than applying loam recommendations to clay soils ( Loam on Clay column in Table 6). A comparison with recent published experimental data revealed that statistically significant differences across space are difficult to determine in most empirical studies as study periods often do not match and include only a few years of data. Of the studies reviewed for this paper, statistically significant spatial differences were due to tillage practices as a result of soil characteristics and ranged up to $309 ha1 (Popp et al., 2001a). A study on maturity group selection reported statistically significant net return differences as large as $99 ha1 but included MG IV in the choice set (Popp et al., 2001b). No significant planting date differences were found. Overall, these results suggest that soil type differences are more significant than climatic differences. This becomes especially apparent when summarizing per hectare location and soil type impacts across production regions. Table 7 summarizes the impact of applying inappropriate recommendations across production regions with the Table 7 Total estimated annual losses associated with climate and soil-specific research Recommendations from Keiser
Rohwer
Stuttgart
Clay
Loam
Clay
Loam
Clay
Loam
Soil type losses ($) Climate losses ($)
183 –
2501 1375
183 –
2501 1375
183 –
– 183
Total loss ($)
183
3877
183
3877
183
183
224
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
assumption that research recommendations would be adopted by all producers in the region. Employing Keiser clay recommendations leads to losses in returns at Keiser on loam soils and also loamy soils at Rohwer. Since there is no climate effect on clay soils, both losses are due to restrictions of soil type (i.e., applying clay recommendations on loam soils). In this case there are approximately 30% of 394,726 hectares of loam used for dryland soybean production in the Northern region with a loss of $1.36 ha1 . In addition to those losses there are 233,215 hectares of loamy soils in the Southern region where transfer of recommendations across soil type leads to $0.32 ha1 losses. Employing Keiser loam recommendations leads to losses in returns on Keiser clay, Rohwer clay, Stuttgart clay and Stuttgart loam. The latter two, Stuttgart clay and loam are classified as climate losses. Applying Keiser loam conditions on Stuttgart loam lead to a loss of $5.34 ha1 . Applying Keiser loam conditions on Stuttgart clay leads to a $2.94 ha1 loss which is wholly attributable to climate differences as Stuttgart loam recommendations on Stuttgart clay lead to no losses. Again, these losses per acre are multiplied by the appropriate land area in dryland soybean production. While the per hectare impact is relatively small (Tables 5 and 6), mainly because of the relatively small change in production practices (Table 2), Table 7 indicates that the choice of research location and soil type could lead to large losses at the regional level. Compared to the losses listed in Table 7, spending an additional $5000–$20,000 for replication of research across soils and climatic region seems appropriate even if new recommendations based on the research are adopted by only a small fraction of regional producers. Impacts of applying loam recommendations from either Rohwer or Keiser across the Arkansas Delta lead to estimated potential annual losses of nearly $3.9 million. Losses from applying recommendations to the wrong soil type are again larger than losses resulting from differences in weather conditions across production regions. Further, the results suggest that the research ought to be done on the poorer of the two soil types (recall that clay had poorer performance statistics than loam soils) as clay recommendations (ÔKeiser ClayÕ and ÔRowher ClayÕ columns in Table 7) lead to lower overall losses compared to loam recommendations (ÔKeiser LoamÕ and ÔRohwer LoamÕ columns in Table 7). The central Stuttgart research location offers the least potential regional losses from research transfer as soil type and climate losses are minimized.
4. Conclusions This study attempted to delineate the potential loss from applying soybean research recommendations from a single location/climate and soil type to other locations/climates and soil types in the Arkansas Delta. Biophysical simulation was used in conjunction with three sets of weather data and two soil series argued to be representative of the production region. Optimal soybean planting strategies were developed by varying planting date, row spacing, seeding rate in the row, and seed maturity group. While the study shows some potentially large implications of apply-
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
225
ing research recommendations from one climate/soil type combination to the rest of the production region, overall results suggest that, given the focus of this study, the potential for loss is relatively small in four of six cases on an annual basis. Given even these relatively small findings, the lowest loss estimate of $183,000 would likely be sufficient to cover the marginal (additional) costs of at least one additional experimental site. Recommendations to experiment station directors, faced with prioritizing research on topics similar to the conditions outlined in this study, would be to conduct research on the poorer clayey soils regardless of location as potential losses from soil type restrictions were the greatest if loamy recommendations were applied on clay. Further, as additional resources are available, experimental research should first be expanded to different soil types than to different climatic regions in the state of Arkansas. However, these study results need to be regarded with caution as the set of ‘‘reasonable’’ production parameters chosen (i.e., representative soil series, weather data, production parameters) may not necessarily be the practices researchers are expected to evaluate. Perhaps planting date recommendations on maturity groups 0 through IV would lead to much different planting date and production recommendations across regions and thus larger potential research transfer losses. Finally, if a researcher was interested in making more generalizable recommendations across crops and production practices, further research would be required to analyze similar decisions for other crops or even livestock and a greater list of parameters associated with soybean production than modeled here. While not attempted here, further research in this area may be of interest to experiment station directors in need of allocation guidelines for future research.
Acknowledgements This research was funded through the support of the Arkansas Soybean Promotion Board and the Arkansas Agricultural Experiment Station. The authors wish to acknowledge the agronomic expertise of Dr. Terry Keisling and the help on managing the soil series definitions and soils data base from Amanda Aescoba. Finally the authors are also grateful for the help of Patrick Manning.
References Arkansas Agricultural Statistics Service, 2003a. Soybeans: acreage, yield, production, price and value. Available from
226
M.P. Popp et al. = Agricultural Systems 81 (2004) 213–226
Henning, S.A., Eddleman, B.R., 1986. Intra- and inter-state transferability of soybean variety research. South. J. Agric. Econ. 18, 7–13. Hill, J., Popp, M., Manning, P., 2003. Focus group survey results: typical Arkansas crop producer production and marketing practices. Research Report 971. University of Arkansas Experiment Station, Fayetteville. Jones, J.W., Boote, K.J., Hoogenboom, G., Jagtap, S.S., Wilkerson, G.G., 1989. Soybean crop growth simulation Model v5.42. UserÕs Guide. Journal No. 8304. Agricultural Experiment Station, University of Florida, Gainesville. Keisling, T.C., Wells, B.R., Davis, G.L., 1984. Rice management decision aids based upon thermal time base 50° F. Computer Technical Bulletin 1. University of Arkansas, Cooperative Extension Service, Little Rock. Manning, P., Keisling, T., Popp, M., Oliver, L., 2001. Evaluation of row-spacing, seedbed preparation, and weed control options for dryland soybean. Comm. Soil Sci. Plant Anal. 32, 1899–1913. Oriade, C.A., Dillon, C.R., Keisling, T.C., 1999. Economics of wheat residue management in doublecrop soybean. J. Prod. Agric. 12, 42–48. Oriade, C., Dillon, C.R., Vories, E.D., Bohanan, M.E., 1997. An economic analysis of alternative cropping and row spacing systems for soybean production. J. Prod. Agric. 10, 619–624. Popp, M., Keisling, T., Dillon, C., Manning, P., 2001a. Economic and agronomic assessment of deep tillage in soybean production on Mississippi river valley soils. Agron. J. 93, 164–169. Popp, M., Keisling, T., Oliver, L., Dillon, C., Manning, P., 2001b. Analysis of seedbeds and maturity groups for dryland soybean on clayey soil. Agron. J. 93, 827–835. Popp, M., Oliver, L., Dillon, C., Keisling, T., Manning, P., 2000. Evaluation of seed bed preparations, planting method and herbicide alternatives for dryland soybean production. Agron. J. 92, 1149–1155. Prickett, M., 1985. Irrigation scheduling for Arkansas soybean production. M.Sc. Thesis. University of Arkansas, Fayetteville. Singh, M., Yau, S.K., Hamblin, J., Porceddu, E., 1996. Inter-site transferability of crop varieties: another approach for analyzing multi-locational variety trials. Euphytica 89, 305–311. Spurlock, R., 1992. Mississippi State University Budget Generator. Technical Bulletin No. 52. Mississippi State. University, Agricultural Experiment Station, Mississippi State. Staff, University of Arkansas Division of Agriculture., 2000. Arkansas Soybean Handbook. MP197. University of Arkansas, Cooperative Extension Service, Little Rock. Trent, A., McKinion, J.M., 1987. Determining transferability of solar radiation data. Agron. J. 79, 124– 129. Trice, K., 1986. The economics of double cropping wheat and soybeans: a simulation analysis using WHEATSOY. M.Sc. Thesis. University of Arkansas, Fayetteville. Windham, T., Marshall, J., 2003. Soybeans, stale seedbed, non-irrigated, roundup ready, clay and mixed soils. Enterprise Budget AG-739. University of Arkansas, Cooperative Extension Service, Little Rock. Wood, C.L., Cady, F.B., 1981. Intersite transfer of estimated response surfaces. Biometrics 37, 1–10.