To provide additional insight to the agricultural policy debate, FAPRI has introduced weather volatility into its analysis or three alternative farm program designs

Meyers, William H.

Smith, Darnell B.

English

To provide additional insight to the agricultural policy debate, FAPRI has introduced weather volatility into its analysis or three alternative farm program designs.</p

Meyers, William

Smith, Darnell

Digital Repository @ Iowa State University (ISU)

Iowa Ag Review lose acres in the final three years. Total corn and soybean acres would increase from 2001 to 2004 as farmers try ro capture more market derived net farm income. The acres t.hatthey put into production come from the land leaving the CRP. Average Annual Effects ofFann Bill Options on Selected Variables in Iowa Com and Soybean Planted Area Crop Ywrs 96197-0010 1 2003104 l3ascline Value 25 percent Flex · Revenue Assurance • No Program Corn Planted Area Crop \'ears Baseline Value 25 percent Flex Revenue Assurance No Program Soybean Planted Area CmJJ Years Baseline Value . 25 percent Flex - Revenue Assurance • No Program Crop Receipts Calcrular Years Baseline Value · 25 percent Flex Re1·enuc Assurance o Program Government Payments Calendar Years Baseline Value · 25 percent Flex · Revenue Assurance No Program Net farm Income Calendar Years Baseline Value 25 percent Flex . Revenue Assurance* · No ProgTam (Thousand acres) 21 .841 23,064 Up 221 Up 335 Up 600 • Up 429 Up323 Up 176 96197-0010 I 2003104 (Thousand acres) 13,085 13,533 Down IS 7 Down 180 Up 166 Up 3 Down 57 Down l38 96197-0010 I 2003104 (TI10usarul acres) 8.756 9,530 Up 378 Up 514 Up 434 Up 426 Up 379 Up 313 1996-2000 2003 (Millio11) $4,908 $5,689 Up 528 Up S75 Up $ 13-+ Up S244 UpS 155 Up 5231 I 996-2000 200.3 (Million) $869 $728 Down $142 Down $384 Down $608 1996-2000 Down $116 Down S554 Down S554 2003 (Million) $1,349 $1,171 Down $1-+2 Down 583 Down $320 Down $136 Down $526 Dowu $334 •Net Farm lrrcomc· cmdcr· Revenue Assumncc Ctlllttlins Revenue AsS/fi'IIIIC~ /rrrreJk~ prrid /.0 tile fm'mfr /Jy 1/rr rrdcml Glrvennnent. Planted area is higher under Revenue Assurance and the risk component of agriculture is reduced, relative to the other options, so banking ins titutions would be willing to lend more money for operating eA-penses. 1 o Program and 25 percent Flex are in the middle range of planted area for all the years. The difference occurs when flex is increased and farmers leave the program because the perceived bendits are less than the perceived costs. With the 25 percent Flex option comes a reduction in payments, and thus the program may not prove to be wonh the costs of compliance. The lowest plamed area shows up in the baseline, where net returns are the highest and A RPs are in effect. The corn and soybean plantings rise across aU scenarios in the final three years of the baseline projection period because of the expiration of the CRP. Budget impact on Programs In evaluating the previous resu lts, it is imponantto note that the baseline scenario is under fu ll CCC funding over the Lime period. The current budget resolution calls [or a nationwide reduction in CCC funding of $8.4 billion over five years and $13.4 billion over seven years. This would be the contribution of agriculture programs to achieve a balanced budget. Farmers would derive numerous indirect benefits from deficit elimination that are not included in the above analysis. Interest rates should be lower because the government \viii not demand money in the fonn of loans from commercial banks. The regulatory envi-ronment may not be as stringent on agriculmre. U such benefits Crom a balanced budget are realized, farm production costs would decline and net farm income would be higher. Each of these policy options will have a different impact on the future of Iowa agriculture. The underly-ing question of what irnpact the Farm BiJI policy wiJI have on the financial picture of the Iowa agriculntral · economy will not be determined for years. This analysis provides a look at some of the variations thal may occur if certain policy options are enacted int.o law. Weather Volatility and Farm Bill Options ( William H. Meyers, 5151294-1 184) (Dame// B. Smith, 5151294- 1184) To provide additional insight to the agricultural policy debate, FAPRI has introduced weather volatility into its analysis or three alternative farm program designs. The t.hree scenarios were previously swdied and presented to Congressional staff under the assumption of "normal" weather. The extended analysis, discussed in this article, incorporated weather volatility that was experienced in the 1980s into the 1995 FAPRI baseline Page 6 CENTER FOR AGRICULTURAL AND RURAL DEVELOPMENT September 1995 and the three options presented in the last issue of this publication. The three alternatives evaluated are: • No Program (NP)- elimination of all budget driven agricultuml polic:ies • Marketing Loan (ML) - conversion of current programs to marketing loans • Revenue Assurance (RA) - provides producers with safety net on revenue Among other differences, the expon enhancement program was eliminated under the NP and ML sce-narios and was retained under the RA option. More complete details of the three options are discussed in the june issue of Iowa Ag Review. Considerable volatility e.Aists in U.S. agricultural crop production. The vagaries of weather, the export market s ituation, and shifts in government policy contribute to large year-over-year shi fts in production, utilization. and prices of agricul tural commodities. ln reality, weather induced supply shocks carry forward into future behavior impacting acreage planted, livestock production, and end-of-year stock holdings. Because assuming nonual weatl1er precludes evalua-tion of how volatility impacts these variables, an analysis highlighting recent weather shocks was deemed advisable. T he analysis assumed that the weather of t he 1980s repeats, begi nning with the 1996 crop year projections. Weather for 1996 is assumed to be identical to the weather of 1982. Thus, the drought of 1983 affects 1997 yield projections and the 1988 drought decreases projections of yields for 2002. Lndividual crop tables are not shown, but corn yields, for example, fall below expected yields by 33.6 and 34.4 bushels per acre for the years 1996 and 2002, respectively. Over the fu ll analysis, yield changes vary considerably by crop and region. While com yields were. largely down in 1997, wheat yields were above trend !'or that crop year. Cotton had yield decreases in 1997 and 2000, but was above trend for 2002. Among other things, this study was undertaken to answer several fundamental questions: l. How does weather uncertainty affect FA PRJ baseline values, especially key aggregates? 2. How do alternative programs compare in 1erms of volatility of prices to consumers, volati li ty of net farm income to producers, and volatility o f program costs to the government? Iowa. Ag Review baseline crop receipts are, on average, at higher levels with weather variability increasing from $97.61 billion to $98.95 billion. Crop receipt volatility is also slightly higher with variable weather as standard deviation increases to 6.88 from 5.-+8. Table 1. Impa cts of 1980s Weather on Baseline (Current Program) Projections tha t Assumed Average Weather. Average Weather 1980s Weather Mean Standard Mean Standard Dcvt:llion Deviation (Billion Dollars) Crop Receipts 97.61 5.48 98.95 6.88 Livestock Receipl5 1)6.35 6.52 97.22 -) 1 '·-J=ccd Expense 24.07 1.09 25.2-t 2.85 Tota l Produc tion Expenses tn.so 7.!5 174.38 9.13 Net Fann Income 47.-+6 4.93 46.57 7.23 Net CCC Outlays 7.63 0.95 6.68 2.08 Toto l Food Expenditure 541.82 47.66 544.12 48.99 Mc:u Bundle Price 1.78 O.oJ 1.80 0.04 ln general, for baseline comparison purposes (question one above), the results iudicate that inclusion of weather variability tends to result in slightly higher prices on average and increased volatility. The largest percentage effect was to CCC expenditures as they were reduced by 12.5 percent on average and volatUlty nearly doubled. Weather variation provided sHghtly lower estimates of farm income with volatility; again measured by standard deviation, increasing by nearly 50 percent from 4.93 to 7.23. Interes tingly, consumer food expenditures showed less than a 0.5 percent increase from weather variability with only a slight change in volatility as measured by standard deviation. Table 2. Impacts of 1980s Weather on Key Aggregates Under Alternative Policies. • Current Programs Mnrkcting Loan Revenue Assurance t ransitlon pa)•ments fC\'enuc :ISSUr:tiiCC export programs No Program Net Farm CCC Outlays Income High Low llil(h L(>w (Billion Dollars) 57.52 33.10 9.32 3.39 55.20 31.+1 l0.52 2.82 56.25 3-1.-10 7.48 1.80 5.06 0.00 1.50 1.28 0.92 0.52 54.40 27.-10 3.49 0.08 Cons11mcr El<-pcndirurc High Low 624 622 613 622 472 472 4n 470 In comparing the volati lity of key aggregate variables across alternative farm programs, Table 2 shows that the high and low values for consumer e:11:penditures Weather impacts under the baseline arc compared in were quite similar over the ten-year projection period. terms of average levels and variability of key aggregates As expected, continuation of current programs showed (Table L). Here standard deviation over the period is the highest farm income with the NP scenario having used as the measure of volatility. To illustrate results. the lowest levels of income. Extremes in farm income September 1995 CENTER FOR AGRICULTURAL AND RURAL DEVELOPMENT Page 7 Iowa Ag Review (the range between the high and low values) were lowest under RA and largest under NP. The range of CCC expenditures was largest under ML, implying that if cenainty of federal budget exposure is important, this ML option docs not perform well. CCC outlays under RA are separated into these categories, since transition payments and export subsidies are on a fixed and decl ining schedule. The cost of the revenue assurance indemnities, however, do vary from year to year; but the variation over this period is less than $250 million. A general conclusion of 1 his s tudy is that if consumer price s tability is one of the objectives o f farm pro-grams, then the current program s tructure is not contributing s ignLficantly to this goal. The inclusion of weather variabili ty s howed little dHference between the current programs and that of other scenarios in consumer expenditure variation analyzed. The results also indicate only a s light reduction in net farn1 income volatility due tO changes in policy structure. Differences between high and low values varied only slightly across current programs, ML, and RA, al-though it was significanLly larger under NP. While this study compares only a few options, it indicates that there are other program designs that perform as well or better in stabilizing net farm income and consumer expenditures. How Revenue Assurance and Yield Insurance Stack Up: A Cost Comparison (Chad E. Hart, 515/294-6307) (Darnell B. Smith, 5151294-1184) What percentage of expected revenue would be assured to agricultural producers if govermnent yield insurance was transformed into a revenue assurance type of safety net program? Recent interest in a potential dual federa l crop insurance program that would offer producers the option of yield or revenue insurance at the same level of U.S. government subsidy prompts this question. 1-le re, we illustrate what level of revenue insurance coverage might be obtained given a fixed amount of government expenditure. We have assumed that average U.S. Federal Crop lnsurance totals $0.5 billion per year- the forn1ulation of this dollar amount is described later. We then estimate the level of revenue insurance obtainable with the given expenditure level. The reader is forewarned that several assumptions are crucial to the esrimates provided below. Estimating Revenue Assurance Costs For the FA PRJ Weather scenario presented in the previous article, the shocks arc induced imo the weather variables which in turn affect yields and other explanatory variables used in the FAPRI system. Because detailed weather information is not readily available at the state level, translation of the shocks from the USDA cost of production regional level to the state level is performed using yield deviations. Rev-enue per acre is computed as the product of [arm price and yield for each state. In the Average Weather Revenue Assurance scenario, gross reven ue is taken to be normally distributed and non-negative. Cost of revenue assurance per acre for each state and crop combination is esti mated by evaluating the probability of realized revenue falling below a threshold proportion of expected revenue. This fixed cost per acre is then multiplied by acres planted by state to derive an aggregate U.S. cost. For the Variable Weather Revenue Assurance scenario, the cost is estimated in each simulation year using the average of the previous five years of revenues as the mean revenue. Thus, revenue assurance costs per acre for each state and crop combinatio n are updated in each simulation year in the Weather scenario. Comparing Revenue Assurance/Yield Insurance Costs Average historical crop insurance costs for each crop, and in total for L989-L994, are computed as the total of the average government total premium subsidy, average excess loss, and a 30 percent reimbursement of average total premiums to private insurers over the • Lime period. The sums of these cost estimates are used as the benchmark government funding amounts assumed for yield insurance. As a result, they can then be used for the revenue assurance comparison. Given these figures, the percemage of revenue that could be ensured is varied to equate the average payout with Revenue Assurance under the Weather scenario [or 1996-2003 to the average crop insurance costs for 1989-1994 by crop and in total. Preliminary results are given in Table l. With the L994 Federal Crop Insurance reform that replaced disaster payments with low catastrophic coverage, this is likely 10 be a conser-vative estimate of future yield insurance cost. The following notes lis t estimation caveats: • FCIC overhead and administration costs are not included in the average crop insurance cost esti-mates. Page 8 CENTER FOR AGRICULTURAL AND RURAL DEVELOPMENT September L995 

Weather Volatility and Farm Bill Options

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1328&amp;context=iowaagreview

Weather Volatility and Farm Bill Options

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