Conservation tillage practices, like no-till, are

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1 Publshed March, 202 Effects of No-Tll on Yelds as Infl uenced by Crop and Envronmental Factors Sol Tllage, Conservaton & Management Dustn K. Tolver,* James A. Larson, Roland K. Roberts, Burton C. Englsh, Danel G. De La Torre Ugarte, and Trstram O. West ABSTRACT Ths research evaluated dfferences n yelds and assocated downsde rsk from usng no-tll and tllage practces. Yelds from 442 pared tllage experments across the Unted States were evaluated wth respect to sx crops and envronmental factors ncludng geographc locaton, annual precptaton, sol texture, and tme snce converson from tllage to no-tll. Results ndcated that mean yelds for sorghum [Sorghum bcolor (L.) Moench] and wheat (Trtcum aestvum L.) wth no-tll were greater than wth tllage. In addton, no-tll tended to produce smlar or greater mean yelds than tllage for crops grown on loamy sols n the Southern Seaboard and Msssspp Portal regons. A warmer and more humd clmate and warmer sols n these regons relatve to the Heartland, Basn and Range, and Frutful Rm regons appear to favor no-tll on loamy sols. Wth the excepton of corn (Zea mays L.) and cotton (Gossypum hrsutum L.) n the Southern Seaboard regon, no-tll performed poorly on sandy sols. Crops grown n the Southern Seaboard were less lkely to have lower no-tll yelds than tllage yelds on loamy sols and thus had lower downsde yeld rsk than other farm resource regons. Consstent wth mean yeld results, soybean [Glycne max (L.) Merr.] and wheat grown on sandy sols n the Southern Seaboard regon usng no-tll had larger downsde yeld rsks than when produced wth no-tll on loamy sols. The key fndngs of ths study support the hypothess that sol and clmate factors mpact no-tll yelds relatve to tllage yelds and may be an mportant factor nfluencng rsk and expected return and the adopton of the practce by farmers. Conservaton tllage practces, lke no-tll, are not new. The Incas and ancent Egyptans used a form of no-tll by usng a stck to make a hole n the sol for the seed and coverng the hole wth drt usng ther feet (Derpsch, 2004). No-tll as we know t today, however, dd not occur untl the late 940s followng the nventon of the herbcde 2,4-D (2-4 dchlorophenoxyacetc acd) and later the herbcdes atrazne (2-chloro-4-ethylamne-6-sopropylamno-S-trazne) and paraquat ( -dmethyl-4,4 -bpyrdunum) (Derpsch, 2004). Herbcdes made t possble to control weeds wth chemcals, whch substtuted for manual labor and mechancal tllage. In addton, mprovements n no-tll plantng technology and the ntroducton of herbcde-tolerant crops n the 990s that allowed over-the-top applcaton of herbcdes durng the growng season have postvely nfluenced the adopton of conservaton tllage practces such as no-tll (Fernandez- Cornejo, and McBrde. 2000; Derpsch, 2004; Roberts et al., 2006). Thus, the area under no-tll for major crops n the Unted States has grown to an estmated 24 mllon ha n 2007 (Larson et al., 200). In 984, then Secretary of Agrculture John Block predcted that 95% of all U.S. cropland would be under no-tll by 200 (McWhorter, 984) and others predcted D.K. Tolver, J.A. Larson, R.K. Roberts, B.C. Englsh, and D.G. De La Torre Ugarte, Agrcultural and Resource Economcs Dep., Unv. of Tennessee, Knoxvlle, TN 37996; and T.O. West, Pacfc Northwest Natonal Lab., Rchland, WA 99352, and Global Change Research Inst., Unv. of Maryland, College Park, MD Receved 8 Sept. 20 *Correspondng author (dtolver@utk.edu). Publshed n Agron. J. 04: (202) Posted onlne 7 Feb 202 do:0.234/agronj Copyrght 202 by the Amercan Socety of Agronomy, 5585 Gulford Road, Madson, WI 537. All rghts reserved. No part of ths perodcal may be reproduced or transmtted n any form or by any means, electronc or mechancal, ncludng photocopyng, recordng, or any nformaton storage and retreval system, wthout permsson n wrtng from the publsher. 65% by 2000 (Phllps et al., 980). Only 24% of cropland n the Unted States s currently under no-tll, however, and the percentage declnes for all cropland worldwde (Conservaton Technology Informaton Center, 2009). Wth a wde range of reported advantages of no-tll (Young, 982; Bremer et al., 200; Lankosk et al., 2004; DeFelce et al., 2006), why has the practce not been more unversally adopted? Proft s probably the most mportant factor nfluencng whether farmers adopt conservaton practces such as no-tll (Cary and Wlknson, 997). Ths study addressed the crop yeld component of proft as nfluenced by tllage and no-tll practces. Farmers are passonately nterested n crop yelds and the nfluence that alternatve producton practces have on yelds because of ther large mpacts on profts (Lowenberg- DeBoer, 999, p. 276). Wth farmers beng prce takers, tllage practces can be ranked n terms of revenue by consderng dfferences n yelds and the probablty that no-tll yelds wll fall below tllage yelds (downsde yeld rsk). Evdence about whether no-tll yelds are dfferent from tllage yelds s not clear. Many researchers have ndcated hgher yelds wth the use of no-tll compared wth tllage (Endale et al., 2008; Smley, and Wlkns, 993; Wagger and Denton, 989), whle others have reported the opposte (Graven and Carter, 99; Halvorson et al., 2006; Hammel, 995). Stll others found no sgnfcant dfference between tllage and no-tll yelds (Archer and Recosky, 2009; Barnett, 990; Kapusta et al., 996). The sols on whch a crop s grown usng no-tll or tllage practces and clmate may nfluence the dfferences n yelds observed wth the two practces (DeFelce et al., 2006). Prevous studes have also found that dfferent crops respond dfferently to no-tll (Shapro et al., 200; Wlhelm, and Wortmann, 2004). These yeld dfferences may affect the rsk and return from convertng to no-tll and thus farmers wllngness to adopt no-tll (Larson et al., 200; Rbera et al., 2004). 530 Agronomy Journal Volume 04, Issue 2 202

2 Fg.. Experment locatons used n analyss and mapped accordng to ther USDA Economc Research Servce farm resource regon. The objectve of ths research was to evaluate the mpacts on expected crop yelds and rsk after convertng from tllage (conventonal tllage, strp tllage, rdge tllage, or mulch tllage) to no-tll practces. The mpacts were explored n relaton to the year the converson took place, the crop produced, annual precptaton, sol texture, and geographc locaton n the Unted States. The only other regonal analyss of tllage ntensty and crop yelds (DeFelce et al., 2006) evaluated mean corn and soybean yelds usng data from experments located n the mdwestern and eastern Unted States and southern Canada. Our study dffered by evaluatng the yelds of sx crops n addton to evaluatng the dfferences n downsde yeld rsk from convertng from tllage to no-tll usng experments from across the Unted States. We measured downsde rsk as the probablty of havng lower yelds wth no-tll than tllage after converson. We also evaluated a wder range of potental growng envronments and locaton factors that may nfluence dfferences n yelds and downsde rsk. DATA Data from 686 pared tllage and no-tll experments publshed n Sol and Tllage Research were compled by Kunda and West (2006). Of these 686 pared experments, the 6 experments pertanng to the 48 contguous Unted States were analyzed. The data set was updated to nclude four pared Agronomy Journal Volume 04, Issue experments reported n that journal between July 2006 and December The data set was further augmented by addng 73 pared experments publshed n Agronomy Journal from 980 through 2009 to mantan temporal consstency wth the prevous data set. The data set was expanded a thrd tme wth 04 pared experments found n the Journal of Producton Agrculture from ts ncepton n 988 untl t was absorbed nto Agronomy Journal n 999. These addtons ncreased the data set to a total of 442 pared tllage experments across 92 locatons n the 48 contguous Unted States. Of these experments, 66% used a randomzed complete block expermental desgn, 25% a splt-plot desgn, 7% another desgn (.e., strp plot, strp-splt, or unque companon plots), and 2% dd not report the expermental desgn. When researchers used dfferent fertlzer rates or other treatments n the experments, yelds were averaged across these treatments for each tllage method. The crops analyzed ncluded sorghum, corn, soybean, oat (Avena satva L.), cotton, and wheat. It should be noted, however, that data for oat n ths data set are few and lmted to one experment n Oho n the eastern part of the Heartland farm resource regon as defned by the USDA Economc Research Servce (2000; Fg. ). Other data for all experments ncluded the year each experment began, each ndvdual year of the experment, sol texture, geographc locaton, and annual precptaton. The sol texture at each 53

3 Table. Varable names and defntons for the statstcal models comparng no-tll yelds wth tllage yelds. Varable name Varable defnton Mean Hypotheszed sgn Dependent varables ln(rr) natural logarthm of the rato of no-tll to tllage yelds NTPROB = f no-tll yelds < tllage yelds; 0 otherwse 0.63 Explanatory varables TECH natural logarthm of the year the experment began 2.8 LOGYR natural logarthm of each year of the experment.4 + RAIN actual annual ranfall at experment locaton, cm / Crop varables SORG = f sorghum; 0 otherwse / WHEAT = f wheat; 0 otherwse / CORN = f corn; 0 otherwse / SOY = f soybean; 0 otherwse 0.6 +/ OAT = f oat; 0 otherwse 0.0 +/ COTT = f cotton; 0 otherwse / Tllage varables TILL = f comparng conventonal tllage to no-tll; 0 for comparng reduced tllage to no-tll 0.6 +/ Sol texture varables SAND = f sandy sol; 0 otherwse SILT = f slty sol; 0 otherwse 0.0 CLAY = f clay sol; 0 otherwse 0.03 LOAM = f loamy sol; 0 otherwse 0.87 Farm resource regon HEART = f Heartland regon; 0 otherwse / NCRES = f Northern Crescent regon; 0 otherwse 0.3 NGP = f Northern Great Plans regon; 0 otherwse 0.07 PGATE = f Prare Gateway regon; 0 otherwse / EASTU = f Eastern Upland regon; 0 otherwse / SOSEA = f Southern Seaboard regon; 0 otherwse 0. + FRIM = f Frutful Rm regon; 0 otherwse / BANDR = f Basn and Range regon; 0 otherwse / MISS = f Msssspp Portal regon; 0 otherwse Reference dummy varables excluded from the regresson models. expermental locaton was usually provded. When the sol texture was provded as percentages, the USDA sol texture trangle was used to estmate the sol texture classfcaton (Sol Survey Dvson Staff, 993). As wth oat, observatons for slttextured sols were lmted to a wheat experment n the upper part of the Basn and Range farm resource regon (USDA Economc Research Servce, 2000; Fg. ). Annual precptaton n centmeters for each year of each experment was added to the data set (Natonal Clmatc Data Center, oa/clmate/statonlocator.html, accessed 2009). Each experment was located wthn one of the nne farm resource regons defned by the USDA Economc Research Servce (2000; Fg. ). These regons represent areas wth smlar types of farms as well as smlar clmatc, physographc, and sol characterstcs (Fernandez-Cornejo and McBrde, 2000). Geographc locatons for the experments are provded n Fg.. Four observatons were omtted because of human error, resultng n lttle to no yeld. Three addtonal observatons were omtted because of zero yeld readngs from a plot dgtzer. The total number of usable pared no-tll and tllage yeld observatons from the 442 experments at 92 locatons n the augmented data set was 546. Table contans the varable names, means, and ther defntons. METHODS AND PROCEDURES Conceptual Framework Proft from producng a crop usng alternatve tllage practces can be modeled usng the followng equaton (Ncholson, 2005): π = PY VC FC where π s proft, P s crop prce, Y s crop yeld, VC s the varable costs of producton, FC s the fxed costs of producton, and subscrpt denotes the partcular tllage practce (conventonal tllage, no-tll, strp tllage, rdge tllage, or mulch tllage). Crop yeld Y can be used to rank tllage practces by ther revenue outcomes (PY ) because the crop prce s fxed for the prce-takng farmer. The costs of producton also affect proft (VC + FC ) but were not nvestgated n ths study. Yelds for the th tllage practce are uncertan due the unpredctable mpacts of weather, sol, other producton envronment factors, and locaton (Graven, and Carter, 99; Harston et al., 990; Lueschen et al., 992; Smth et al., 992). The nfluence of alternatve tllage practces on crop yelds can be evaluated usng the moments of the yeld probablty dstrbuton (Anderson et al., 977; Chavas et al., 2009). The frst moment s the mean yeld: [ Y ] = F( v x ) [2] E E 532 Agronomy Journal Volume 04, Issue []

4 where E[ ] s the expectaton operator, v s a vector of the producton envronment factors affectng producton, and x s a vector of producton nputs used wth the th tllage method. Mean yeld, and thus mean revenue, vares wth the th tllage practce as nfluenced by the producton envronment and locaton factors. Many farmers are concerned about the rsk of yeld varablty or change assocated wth the th tllage practce. Rskaverse farmers are most often concerned about devatons n yelds below the mean or some other target value (Bnswanger, 98; Selley, 984; Antle, 987; Chavas, 2004). Downsde rsk below a target or comparson value can be modeled usng the lower partal moment (LPM) (Fshburn, 977). The equaton for the LPM n the context of the tllage decson problem s [ ] = { ( Y Z) } [3] E LPM E mn,0 n where Z s some yeld reference pont for the th tllage practce to be evaluated aganst and n s the degree of the moment. Thus, the LPM s a measure of the expected devaton below the comparson or target level. The common classfcatons of n are: n = 0 s the probablty of a loss, n = s the target shortfall, n = 2 s the target semvarance, and n = 3 s the target skewness. Meta-analyss s a quanttatve method for summarzng the results of ndependent studes to allow the testng of hypotheses that cannot be addressed n a sngle experment (Hedges and Olkn, 985; Cooper and Hedges, 994; Mguez and Bollero, 2005). For ths study, the hypotheses related to the effects of weather, sols, and other producton envronment factors on mean yelds and downsde yeld rsk for no-tll ( = NT) relatve to tllage (conventonal, strp, rdge, or mulch tllage; = TILL) were evaluated. A response rato (RR) was created to evaluate the relatve no-tll and tllage yelds (Hedges et al., 999): NT RR= Y [4] Y TILL Wth the natural logarthm of the response rato [ln(rr)] as the dependent varable (Mguez, and Bollero, 2005), a mxed lnear model was used to evaluate whch producton envronmental and locaton factors affect the relatve mean crop yelds: ln( RR ) = Wα+ Uβ+ ε [5] where α s a vector of unknown fxed effects, β s a vector of random effects, ε s a vector of random resduals, and W and U are gven known and ncdence matrces, respectvely (Harvlle and Mee, 984; McLean et al., 99). The factors nfluencng the dfferences n mean yelds for no-tll vs. tllage practces were determned by the sgn and sgnfcance of the parameter estmates. Settng n = 0 n Eq. [3] (probablty of a loss below a comparson level) and usng the response rato n Eq. [4], a mxed logt model was specfed to evaluate the probablty of no-tll yelds beng lower than tllage yelds as nfluenced by the producton envronment and locaton factors. The dependent varable NTPROB was defned as follows: f RR <, then NTPROB = ; otherwse, NTPROB = 0. Thus, downsde rsk n ths case s defned as the probablty of no-tll yelds beng lower than tllage yelds after converson from tllage to no-tll. The mxed logt model specfes the probablty of downsde producton rsk when convertng from tllage to no-tll: exp() θ NTPROB( a = ) = [6] + exp() θ where a = f no-tll yeld s less than tllage yeld, otherwse 0; θ = Wγ + Uη + τ, γ s a vector of fxed effects, η s a vector of random effects, and τ s a vector of random resduals. The probablty of no downsde rsk when convertng to no-tll s determned as NTPROB( a= 0) = P( a= ) = [7] + exp() θ Identfcaton of the factors nfluencng the probablty of lower no-tll yelds was determned by the sgn and sgnfcance of the parameter estmates. Emprcal Models The emprcal model used to evaluate mean yeld dfferences between no-tll and tllage was specfed as ^ ln RR = 5 8 α ˆ + α ˆ CROP + αˆ SOIL 0 j j = j= 6 6 ˆ ˆ ˆ k k 7 8 k= 9 + α ERS +α TILL+α TECH +α ˆ LOGYR +αˆ RAIN = j= = 5 ( )( ) + αˆ CROP SOIL + αˆ ( CROP )( TECH) + αˆ ( CROP )( LOGYR) = αˆ ( CROP )( RAIN) = ^ 20 j j where ln( RR ) s the estmate of ln(rr), ˆα s an estmated coeffcent, CROP represents one of fve crops ( = sorghum, wheat, soybean, cotton, or oat); SOIL represents one of three sol textures ( j = sand, slt, or clay), and ERS represents one of eght USDA-ERS farm resource regons (k = Northern Crescent, Northern Great Plans, Prare Gateway, Eastern Uplands, Southern Seaboard, Frutful Rm, Basn and Range, or Msssspp Portal). The reference categores for CROP, SOIL, and ERS n Eq. [8] are corn (CORN), loam sol (LOAM), and the Heartland (HEART) regon. The ERS varables were ncluded to capture dfferences across lattudes, longtudes, and other unque regonal and envronmental factors that affect crop yelds. The TILL varable s a bnary dummy varable wth a value of for a comparson of conventonal tllage wth no-tll or 0 for a comparson of reduced tllage (strp tllage, rdge tllage, or mulch tllage) wth no-tll. The varable TECH s a contnuous varable used to capture mprovements n technology wth tme. It represents the natural logarthm of the year n whch the experment was ntated, wth 964 = ln(), 965 = ln(2),, 2005 = ln(42). The varable LOGYR s a contnuous varable that represents the natural logarthm of the year of the experment; for example, for Exp. A conducted between 98 and 985, 98 = ln(), 982 = ln(2),, 985 = ln(5), and for [8] Agronomy Journal Volume 04, Issue

5 Exp. B conducted n 995 and 996, 995 = ln() and 996 = ln(2). The value of LOGYR was used to test whether a yeld lag exsted when convertng from tllage to no-tll, as much anecdotal evdence suggests, and to see f no-tll yelds ncreased wth tme relatve to tllage yelds through sol mprovement. The contnuous varable RAIN s the mean annual precptaton for the locaton and year of each experment. No nteractons were specfed between CROP and ERS because not all crops were present n each farm resource regon. For example, the Frutful Rm and Basn and Range regons had observatons for only two and one crops, respectvely. It should be noted that there were no observatons for sorghum, soybean, oat, or cotton on a slt-textured sol and no observatons for oat on sand- or clay-textured sols; therefore, no nteractons were specfed between those specfc crops and sol textures. The followng logt model was specfed to evaluate the probablty of no-tll yelds beng less than tllage yelds after converson to no-tll: ^ NTPROB= CROP jsoil j = j= 6 6 ^ ˆ ˆ k k 7 8 k= 9 γ ˆ + γ ˆ + γˆ + γ ERS + γ TILL+ γ TECH + γ ˆ LOGYR + γˆ RAIN ( )( ) + γˆ CROP SOIL j j = j= = 5 9 = 5 20 = + γˆ ( CROP )( TECH) + γˆ ( CROP )( LOGYR) + γˆ ( CROP )( RAIN) ^ where NTPROB s the estmate of NTPROB that s the downsde rsk-dependent varable equal to one f the no-tll crop yeld was less than the tllage yeld and zero otherwse, and ˆγ s an estmated coeffcent. Statstcal Analyss Equaton [8] was estmated usng the MIXED procedure n SAS (SAS Insttute, 2004) to test the null hypothess that the yeld means do not dffer wth tllage practce (Lttell et al., 996). Equaton [9] was estmated usng the GLIMMIX procedure n SAS (SAS Insttute, 2006) to test the null hypothess that the downsde yeld rsk does not dffer wth tllage practce (Schabenberger, 2005). The null hypothess of constant yeld varances across experment locatons was tested usng two general tests for heteroskedastcty: the Whte test and the Breusch Pagan test. These tests were performed usng the resduals from the models estmated wthout a random effects statement n SAS (Judge et al., 985). Heteroskedastcty was detected, so the locaton of the experment was specfed as a random effect and modeled usng an unstructured covarance matrx (SAS Insttute, 2006). Akake nformaton crtera were used to determne whether the models wth the random effects specfcaton provded a better ft for the data (Dayton, 2003). Varance nflaton factors were used for multcollnearty dagnoss (Chatterjee and Prce, 99). [9] RESULTS Heteroskedastcty and Multcollnearty The Whte test and Breusch Pagan test both ndcated that heteroskedastcty was present n the models. The models corrected for heteroskedastcty had Akake nformaton crtera closer to zero than the models estmated wthout random effects, suggestng a better ft for ths specfcaton (Dayton, 2003). Evdence of multcollnearty was not found. Mean Yeld Dfferences In the followng dscusson, results from Table 2 are presented as the effects of a varable on the natural logarthm of the rato of no-tll to tllage yelds relatve to the rato of no-tll to tllage yelds for corn produced on a loam sol n the Heartland regon. A larger rato can be nterpreted as a larger dfference between no-tll and tllage yelds. A postve (negatve) coeffcent for a varable ndcates that the rato of yelds s larger (smaller) than the rato of yelds for corn on a loam sol n the Heartland regon, ndcatng that no-tll yelds are hgher (lower) than tllage yelds relatve to corn on a loam sol n the Heartland regon. In the dscusson, one or more of the reference categores for the dummy varables (corn, loam, Heartland) may be mpled for ease of exposton. Crops reacted dfferently to no-tll and tllage methods. Dfferences between no-tll and tllage sorghum (SORG) and wheat (WHEAT) yelds were larger than the dfference between no-tll and tllage corn yelds wth all other factors beng equal. Increased sol mosture, or sol water content, from the resdues left n the feld was by far the most common explanaton for ncreased no-tll sorghum and wheat yelds from the studes used n ths analyss (Dao and Nguyen, 989; Douglas et al., 994; Norwood, 992; Tarkalson et al., 2006; Wese et al., 998; Wnter and Unger, 200). Even though the coeffcent for oat was negatve and sgnfcant, t s dffcult to draw any conclusons about dfferences n yelds for OAT wthout ncludng the OAT LOGYR and OAT RAIN coeffcents because the data were from a sngle experment. The dfference between no-tll and tllage yelds n a slt sol (SILT) was smaller than the dfference between no-tll and tllage yelds n a loam sol. Ths result concdes wth prevous research showng that no-tll performs better on well-draned sols but does not produce as well on fnely textured or poorly draned sols such as slty sols (DeFelce et al., 2006; Harston et al., 990). Ths result may not gve a full representaton of no-tll s effectveness on a slt-textured sol, however, due to the lmted number of slt observatons n the data set. Dfferences between no-tll and tllage yelds on clay and sandy sols were not statstcally dfferent from those on loam sols, all other factors beng equal. Several sgnfcant nteractons were found between sol texture and crop. The nteractons SORG SAND, WHEAT SAND and SOY SAND were sgnfcant and negatve, suggestng that no-tll yelds for sorghum, wheat, and soybean produced on a sandy-textured sol were less than the yelds for corn produced on a loam sol. A potental explanaton generally gven for lower yelds under sandy sols n the studes used n ths analyss was lower water holdng capacty or lower sol mosture under sandy sols, whch s exacerbated n dry years (Busscher et al., 2005; Hlfker and Lowery, 988; Lowery et al., 534 Agronomy Journal Volume 04, Issue 2 202

6 998). Other reasons gven were N nsuffcency due to leachng (Evanylo, 99) and reduced root length under no-tll (Hlfker and Lowery, 988; Karlen et al., 99). Sandy sols leach nutrents more readly, reducng the amount avalable to the plants (North Carolna Department of Agrculture, 200). Reduced root length on a plant causes more stress on the plant under low ranfall and hgh temperatures (Karlen et al., 99). Sandy sols tend to compact and some recommend deep or slt tllage to loosen the sol, whch has been shown to ncrease root length and yelds over no-tll (Busscher et al., 2006; Karlen et al., 99). The hypothess of no-tll performng better relatve to tllage n warmer clmates was confrmed. The Southern Seaboard (SOSEA) and Msssspp Portal (MISS) regons, whch represent a majorty of the southern and southeastern Unted States, had postve and sgnfcant coeffcents. These regons had, on average, hgher no-tll yelds relatve to tllage yelds compared wth the Heartland regon. These results concur wth prevous research (DeFelce et al., 2006), where no-tll was found to have hgher yelds n the southern Unted States and lower yelds n the northern Unted States than tllage. The Frutful Rm (FRIM) and Basn and Range (BANDR) regons, whch cover much of the western and northwestern parts of the country, had smaller ratos of no-tll to tllage yelds than the Heartland regon. Ths result could be explaned by all experments n the data set from these two regons orgnatng from the Upper Northwest Unted States. That area receves large amounts of precptaton from October to March and experences cold weather. It s common for the sol to freeze to a depth of 40 cm or greater (Papendck, 987). Wet years and cold clmates have been found to cause reduced yelds under no-tll compared wth tllage (Graven and Carter, 99; Eckert, 984; Herbek et al., 986). The sgnfcant and negatve nteractons between the varable for technology and sorghum and wheat, SORG TECH and WHEAT TECH, suggest that as the year n whch the experment was ntated ncreased, the dfferences between no-tll and tllage yelds decreased for sorghum and wheat compared wth dfferences n corn yelds. Seed and no-tll technology have advanced wth tme. Wth the Unted States beng the largest producer of corn n the world, technologcal advances related to corn have ncreased at a faster rate than for sorghum or wheat. Intal efforts to genetcally modfy crops were prmarly focused on corn, soybean, cotton, canola (Brassca napus L. var. napus) and potato (Solanum tuberosum L.) (Harlander, 2002). In fact, one of the frst genetcally modfed crops was nsect-resstant corn: Bacllus thurngenss (Bt) corn (Harlander, 2002). The Bt corn provdes protecton aganst the European corn borer, whch s noted as the most damagng nsect n corn producton n the Unted States and Canada (Wtkowsk et al., 2002). To get a better vew of how much more technology was focused on corn than wheat or sorghum from 987 to 20, 7267 applcatons were approved for feld testng of genetcally engneered corn by the USDA Anmal and Plant Health Inspecton Servce (Informaton Systems for Botechnology, 200). By comparson, for the same tme perod, only 43 were approved for wheat, whle sorghum was not mentoned (Informaton Systems for Botechnology, 200). Another beneft to no-tll was the breedng of herbcde-tolerant crops. These herbcde-tolerant crops have ncreased the trend of no-tll by allowng easer control of Table 2. Emprcal mean yeld regresson model comparng no-tll yelds wth tllage yelds. The dependent varable s the natural logarthm of the rato of no-tll to tllage yelds. Explanatory varable Coeffcent t value INTERCEPT SORG ** 5.33 WHEAT ** 2.62 SOY OAT * 2.47 COTT SAND SILT CLAY TILL TECH LOGYR ** 2.67 RAIN NCRES NGP PGATE EASTU SOSEA ** 2.72 FRIM ** 2.77 BANDR * 2.46 MISS SORG SAND * 2.57 SORG CLAY SORG TECH ** 3.60 SORG LOGYR SORG RAIN ** 4.62 WHEAT SAND ** 4.49 WHEAT CLAY WHEAT TECH * 2.2 WHEAT LOGYR WHEAT RAIN SOY SAND ** 4.6 SOY CLAY SOY TECH SOY LOGYR SOY RAIN OAT LOGYR OAT RAIN * 2.58 COTT SAND COTT CLAY COTT TECH COTT LOGYR ** 3.40 COTT RAIN n Resdual log lkelhood 8. Akake nformaton crteron 807. Sample-sze-corrected Akake nformaton 807. crteron Bayesan nformaton crteron 8. * Sgnf cant at the 0.05 conf dence level. ** Sgnf cant at the 0.0 conf dence level. INTERCEPT contans the reference categores of corn crop, loam texture, and Heartland regon; SORG, sorghum crop; WHEAT, wheat crop; SOY, soybean crop; OAT, oat crop; COTT, cotton crop; SAND, sand texture; SILT, slt texture; CLAY, clay texture; TILL, comparson of tllage; TECH, natural logarthm of year experment began; LOGYR, natural logarthm of each year of experment; RAIN, actual ranfall at locaton; NCRES, Northern Crescent regon; NGP, Northern Great Plans regon; PGATE, Prare Gateway regon; EASTU, Eastern Upland regon; SOSEA, Southern Seaboard regon; FRIM, Frutful Rm regon; BANDR, Basn and Range regon; MISS, Msssspp Portal regon. Sgnf cant at the 0.0 conf dence level. Agronomy Journal Volume 04, Issue

7 weeds wthout tllage by applyng a post-emergent herbcde over the crop (Fernandez-Cornejo and McBrde, 2002). The year of the experment (LOGYR) was hypotheszed to have a postve sgn n the hope of capturng any yeld lag n the frst few years after converson from tllage to no-tll. Contrary to expectatons, the varable was sgnfcant and negatve. Thus, wth each addtonal year after converson, no-tll corn yelds decreased slghtly compared wth tllage corn yelds. The negatve effects mght be explaned by most experments lastng between 3 and 5 yr, whch may not be suffcent tme for no-tll felds to reach ther full potental n buldng sol tlth, porosty, and organc matter. Gven that most of the experments lasted between 3 to 5 yr, other factors could also have caused ths result, such as dsease or pest pressure or, as n some cases of early no-tll experments, unfamlarty wth the practce. Interactons of LOGYR wth the crop dummy varables were not sgnfcant except for cotton. The sum of the coeffcents for LOGYR and the COTT LOGYR nteracton suggests that cotton no-tll yelds ncreased relatve to tllage yelds wth each year after converson from tllage to no-tll compared wth the dfference between no-tll and tllage corn yelds. Ranfall dd not sgnfcantly affect the rato of no-tll to tllage yelds for corn; however, RAIN was sgnfcant n two nteractons, SORG RAIN and OAT RAIN. Compared wth corn, ncreases n the amount of precptaton decreased the rato of sorghum no-tll to tllage yelds. Ths has been found n other work where no-tll performed better than tllage durng dry years but yelded less durng wet years (Anderson, 986; Blevns et al., 97). One reason for lower no-tll yelds wth ncreased ranfall s that wetter sols requre more tme for the sol temperatures to ncrease. Ths problem s further exacerbated under cold temperatures when crop resdues are present. The resdues act as nsulaton, keepng the sols cooler as well as reducng mosture evaporaton (Herbek et al., 986). The other sgnfcant nteracton, OAT RAIN, was postve, showng that wth an ncrease n ranfall, no-tll oat yelds ncreased relatve to tllage oat yelds compared wth the dfference between no-tll and tllage corn yelds. The coeffcents n Table 2 can be used to calculate the ratos of no-tll to tllage yelds for specfc crops, sols, and regons. Corn produced on a loam sol n the Southern Seaboard regon (SOSEA), wth TECH and LOGYR evaluated at ther data set means and RAIN evaluated at ts mean for the Southern Seaboard regon (TECH = 2.8, LOGYR =.4, RAIN = 0., TILL = ), SOSEA =, and all other dummy varables equal to zero resulted n ( RR) = + ( TECH) + ( LOGYR) + ( RAIN) + ( TILL) + ( SOSEA) ln = [0] Takng the antlog of Eq. [0] [exp( )] gves as the rato of no-tll to tllage yelds. Takng the antlog of the equaton gves a smple proporton of no-tll yelds over tllage yelds. Ths can then be nterpreted as: f the rato s less (greater) than one, the dfference s negatve (postve), meanng that no-tll yelds are less (greater) than tllage yelds. In ths case, the rato ndcates that mean no-tll yelds were 2.% greater than the mean tllage yelds for corn produced on a loam sol n the Southern Seaboard regon. Another example s that of corn produced on a loam sol n the Heartland regon. In ths case, all dummy varables, ncludng SOSEA, were set equal to zero and the mean ranfall for the Heartland regon (8.5 cm) was used: ln( RR ) = ( ) + ( ) + ( ) + ( ) = [] The antlog of Eq. [] [exp( )] s , ndcatng that no-tll yelds for corn grown on a loam sol n the Heartland regon are 4.4% lower than tllage yelds. The results n Eq. [0] and [] ndcate that no-tll produced hgher corn yelds n the Southern Seaboard regon, whereas tllage produced hgher corn yelds n the Heartland regon. The coeffcents from the model n Table 2 can also be used to show how relatve yelds are affected through tme after converson to no-tll. For the Southern Seaboard regon, the process would be the same as n Eq. [0], except LOGYR would take on values for the specfc years after converson. As an example, LOGYR would be evaluated at ln(), ln(3), ln(5), and ln(0) nstead of at ts mean as n Eq. [0]. The resultng no-tll to tllage ratos for Years, 3, 5, and 0 after converson to no-tll would be.055,.029,.07, and.00, respectvely. These results suggest that no-tll corn yelds would be greater than tllage corn yelds produced on a loam sol n the Southern Seaboard regon for at least 0 yr after converson to no-tll, but the advantage of no-tll would decrease from 5.5 to <% durng those 0 yr. When Eq. [] s used to estmate relatve yelds after converson to no-tll for corn grown on a loam sol n the Heartland regon, the results show that no-tll yelds are.3, 3.7, 4.8, and 6.3% lower than tllage yelds n Years, 3, 5, and 0 after converson, respectvely. The predcted no-tll yelds as a percentage of tllage yelds usng all of the avalable data for the Unted States are gven n Table 3. The predctons were calculated n the same manner as the examples usng the estmated coeffcents n Table 2 and sample means for TECH (2.8), LOGYR (.4), RAIN (usng the sample averages for each farm resource regon), and the dummy varable TILL =. Whle not all of the estmated coeffcents n Table 2 are sgnfcant, they are the best lnear unbased estmates of yeld dfferences due to tllage practce. No-tll tended to produce smlar or greater mean yelds than tllage for crops grown on loamy sols n the Southern Seaboard and Msssspp Portal regons. For the Southern Seaboard regon, no-tll yelds outperformed tllage yelds on average on loam sols when producng soybean (5.3% hgher), corn (2.% hgher), or wheat (2.7% hgher). A warmer and more humd clmate and warmer sols n these regons relatve to the Heartland, Basn and Range, and Frutful Rm regons appear to favor no-tll on loamy textured sols. By comparson, the results on sandy textured sols n the Southern Seaboard regon were mxed, wth hgher mean no-tll yelds for corn and cotton but 536 Agronomy Journal Volume 04, Issue 2 202

8 Table 3. Predcted no-tll yelds as a percentage of tllage yelds by USDA Economc Research Servce farm resource regon (Fg. ) and sol texture. USDA-ERS Annual Sol No-tll yeld ncrease regon precptaton texture Corn Sorghum Cotton Soybean Wheat Oat cm % Heartland 8.5 loam clay Northern Crescent 7.9 loam sand 95.4 Northern Great Plans 38.4 loam Prare Gateway 64.8 loam clay Eastern Upland 96.7 loam Southern Seaboard 0. loam sand Frutful Rm 34. loam sand Basn and Range 22.0 loam slt Msssspp Portal 32.8 loam clay Predctons were calculated usng the estmated regresson coeff cents n Table 2, sample means for TECH (2.8), LOGYR (.4), and RAIN for each farm resource regon, and TILL =. Crop yeld data were not avalable for the farm resource regon and sol texture classf caton. lower mean no-tll yelds for sorghum, soybean, and wheat. Lower mean no-tll yelds on sandy textured sols were also estmated for corn n the Frutful Rm and Northern Crescent regon. The estmates for oat on a loamy textured sol n the Heartland regon and wheat on a slt-textured sol n the Basn and Range regon should be vewed wth cauton due to the lmted number of observatons for both n the data set used for the meta-analyss. The results n Table 3 also ndcate the potental gaps n knowledge about the performance of no-tll relatve to tllage for alternatve crops and sols. For example, data based on the avalable refereed lterature about the performance of no-tll and tllage practces for corn and sorghum on loam-, clay-, slt-, and sand-textured sols were not avalable for the Msssspp Portal Regon. Downsde Rsk The downsde rsks for wheat (WHEAT), soybean (SOY), oat (OAT), and cotton (COTT) were not dfferent from corn, but sorghum (SORG) had a smaller probablty than corn of havng lower no-tll yelds than tllage yelds after converson to no-tll, all other factors beng equal (Table 4). Sol texture (SAND, SILT, or CLAY) dd not sgnfcantly affect the downsde rsk compared wth a loam (LOAM) sol texture. Alternatvely, wheat and soybean grown on sandy sols (WHEAT SAND and SOY SAND) ncreased the probablty of producng lower no-tll yelds than tllage yelds relatve to corn produced on a loam sol. Ths s probably due to sandy sols havng lower mosture holdng capacty, greater leachng potental, or reduced root length due to sol compacton (Busscher et al., 2005; Evanylo, 99; Hlfker and Lowery, 988; Karlen et al., 99; Lowery et al., 998). The lower mean yelds and hgher downsde rsk may ndcate that no-tll on sandy-textured sols for wheat and soybean may not be a rsk-effcent practce and may mpede the adopton of no-tll for these crops on these sols. The Southern Seaboard (SOSEA) was the only farm resource regon that had sgnfcantly reduced chances of havng lower no-tll yelds than tllage yelds. The rest of the regons were not sgnfcantly dfferent from the Heartland regon (HEART) n affectng downsde rsk. Because the Southern Seaboard covers much of the southeastern Unted States, ths result compares favorably to a study by DeFelce et al. (2006), who found that no-tll corn and soybean yelds were greater n the southern Unted States and lower n the northern Unted States. Ther results were manly attrbuted to sol mosture, dranage, and clmate (DeFelce et al., 2006). The results ndcate that the potental postve rsk management benefts of no-tll through hgher mean yelds and lower downsde rsk may be a postve factor n the adopton of the technology n the regon, all other factors beng equal. The varable LOGYR was sgnfcant, showng that the longer the amount of tme that no-tll s used, the hgher the probablty of havng lower no-tll corn yelds than tllage corn yelds. A possble explanaton could be ncreased weeds, nsects, and dsease wth the use of no-tll as a result of the ncreased resdues. Some prevous work has shown no-tll to have reduced yelds compared wth tllage due to weed nfestatons (Buhler and Mester, 99; Cardna et al., 995). The resdue could also be keepng the sol too cold and most, delayng crop emergence and dmnshng yelds. One study n Mnnesota dd report a gradual decrease n corn yelds wth tme wth the use of no-tll. Ths was attrbuted n part to wet and cold sol (Lnden et al., 2000). When LOGYR was nteracted wth soybean and cotton, just the opposte occurred. When nteracted wth soybean (SOY LOGYR) and cotton (COTT LOGYR), there s a lower probablty of havng lower yelds wth no-tll than wth no-tll corn. Thus the producton of soybean and cotton usng no-tll becomes less rsky relatve to tllage as the tme after converson ncreases. The year each experment was ntated (TECH) was not sgnfcant and was not sgnfcant n any nteractons. Ths result s a lttle surprsng. We hypotheszed that ncreases n technology Agronomy Journal Volume 04, Issue

9 Table 4. Estmated logt model predctng the probablty of no-tll yelds lower than tllage. The dependent varable s f no-tll yelds < tllage yelds, 0 otherwse. Explanatory varable Coeffcent t value INTERCEPT SORG * 2.40 WHEAT SOY OAT COTT SAND SILT CLAY TILL TECH LOGYR RAIN ** NCRES NGP PGATE EASTU SOSEA * 2.05 FRIM BANDR MISS SORG SAND SORG CLAY SORG TECH SORG LOGYR SORG RAIN ** WHEAT SAND ** 3.7 WHEAT CLAY WHEAT TECH WHEAT LOGYR WHEAT RAIN ** SOY SAND ** 4.42 SOY CLAY SOY TECH SOY LOGYR SOY RAIN ** OAT LOGYR OAT RAIN COTT SAND COTT CLAY COTT TECH COTT LOGYR COTT RAIN ** n log lkelhood Akake nformaton crteron Bayesan nformaton crteron Sample-sze-corrected Akake nformaton crteron * Sgnf cant at the 0.05 conf dence level. ** Sgnf cant at the 0.0 conf dence level. INTERCEPT contans the reference categores of corn crop, loam texture, and Heartland regon; SORG, sorghum crop; WHEAT, wheat crop; SOY, soybean crop; OAT, oat crop; COTT, cotton crop; SAND, sand texture; SILT, slt texture; CLAY, clay texture; TILL, comparson of tllage; TECH, natural logarthm of year experment began; LOGYR, natural logarthm of each year of experment; RAIN, actual ranfall at locaton; NCRES, Northern Crescent regon; NGP, Northern Great Plans regon; PGATE, Prare Gateway regon; EASTU, Eastern Upland regon; SOSEA, Southern Seaboard regon; FRIM, Frutful Rm regon; BANDR, Basn and Range regon; MISS, Msssspp Portal regon. Sgnf cant at the 0.0 conf dence level. wth tme would decrease the probablty of downsde rsk wth no-tll; however, the probablty of downsde rsk throughout the years dd not sgnfcantly ncrease for no-tll ether. The amount of precptaton (RAIN) was sgnfcant n affectng the probablty of corn havng dmnshed yelds wth no-tll relatve to tllage. Each centmeter ncrease n precptaton ncreased the probablty of lower no-tll corn yelds relatve to tllage corn yelds. Prevous research has shown that no-tll outperforms tllage durng dry years because no-tll conserves water, but no-tll yelds are less wth ncreased amounts of ranfall (Eckert, 984; Herbek et al., 986). Ths could be caused by the decayng wet resdue ncreasng weeds and dsease. The ncreased ranfall could also be keepng the sol too cool and most, delayng crop emergence and decreasng yelds (Herbek et al., 986). When ranfall was nteracted wth sorghum (SORG RAIN), a hgh-resdue crop, the probablty of havng lower no-tll yelds than tllage yelds ncreased compared wth corn; ths once agan concdes wth ranfall negatvely affectng no-tll yelds when hgh amounts of crop resdue are present on the sol. Increases n ranfall, however, decreased the lkelhood of lower relatve no-tll yelds when producng soybean (SOY RAIN), wheat (WHEAT RAIN), or cotton (COTT RAIN) compared wth corn. Because soybean, wheat, and cotton do not provde as much crop resdue as corn, they may not affect sol mosture and temperature as much as a dense-resdue crop such as corn. Therefore, crop emergence s not delayed and dmnshed yelds are less lkely to occur. The coeffcents n Table 4 can be used to calculate the probablty of no-tll yelds beng less than tllage yelds for specfc crops, sols, and regons. Corn produced on a loam sol n the Southern Seaboard regon (SOSEA), wth TECH and LOGYR evaluated at ther data set means and RAIN evaluated at ts mean for the Southern Seaboard regon (TECH = 2.8, LOGYR =.4, RAIN = 0., TILL = ), SOSEA =, and all other dummy varables equal to zero results n ( ) + ( LOGYR) + ( RAIN) + ( 0.05 TILL) ( SOSEA) ODDS= exp TECH + = [2] where ODDS s the odds rato. The rato ndcates that no-tll corn yelds were about.9 tmes as lkely to be lower than tllage corn yelds on a loam sol n the Heartland regon. The downsde yeld rsk probablty calculated usng the odds rato s ODDS NTPROB( a = ) = + ODDS = = [3] Ths ndcates that there was a 65% probablty that no-tll corn yelds would be lower than tllage corn yelds. Usng Eq. [2], the probablty of no-tll yelds beng lower than tllage yelds for corn on a loam sol n the Heartland regon can be calculated by settng SOSEA = 0 and RAIN = 8.5, producng 538 Agronomy Journal Volume 04, Issue 2 202

10 a hgher odds rato of and a hgher downsde yeld rsk probablty of 78%. Consstent wth the mean yeld results, the downsde yeld rsk under no-tll was less n the Southern Seaboard regon than n the Heartland regon. Evaluaton of the estmated coeffcents n the logt model also ndcated that downsde yeld rsks under no-tll for soybean producton were lower than for corn producton. The estmated soybean downsde yeld rsk probablty for the Heartland regon (SOY =, TECH = 2.8, LOGYR =.4, RAIN = 8.5, TILL = ) s ODDS= exp ( SOY ) + ( TECH) + ( LOGYR) + ( RAIN) [4] + ( 0.05 TILL) + ( SOY TECH) + ( 0.43 SOY LOGYR) + ( SOY RAIN) = and NTPROB( a = ) = = The estmated soybean downsde yeld rsk probablty for the Southern Seaboard (SOY =, TECH = 2.8, LOGYR =.4, RAIN = 0., TILL = ), SOSEA =, regon s and ( ) ) ( TECH) ( LOGYR) ( RAIN) ( SOSEA) ( 0.05 TILL) ( SOY TECH) ( 0.43 SOY LOGYR) ( SOY RAIN) ODDS= exp SOY = NTPROB( a = ) = = [5] The predcted downsde rsk probabltes for no-tll yelds compared wth tllage yelds usng all of the avalable data are presented n Table 5. The results ndcate that crops grown n the Southern Seaboard regon were less lkely to have lower notll yelds than tllage yelds on loamy-textured sols and thus had lower downsde yeld rsk than other farm resource regons. Consstent wth mean yeld results, soybean and wheat grown on sandy-textured sols n the Southern Seaboard regon usng no-tll had larger downsde yeld rsks than when produced wth no-tll on loamy-textured sols. CONCLUSION AND IMPLICATIONS The objectve of ths research was to evaluate the mpacts on the mean and rsk of crop yelds of swtchng from tllage practces to no-tll as explaned by factors such as the crop speces, the year the experment began, the tme snce converson from tllage to no-tll, annual precptaton, sol texture, and the locaton of producton. Ths objectve was accomplshed by collectng data from 30 yr of refereed journal artcles from 442 experments at 92 locatons comparng tllage wth no-tll. These data ncluded sx dfferent crops wth locatons across the Unted States. The earlest pared experment used n ths analyss was ntated n 964, when no-tll was n ts nfancy. Ths study was able to corroborate prevous work done wth no-tll. Prevous studes found that dfferent crops respond dfferently to no-tll (Shapro et al., 200; Wlhelm and Wortmann, 2004). Ths study found smlar results, wth sorghum and wheat prosperng under no-tll methods. Sorghum was also found to reduce the probablty of havng lower no-tll yelds than tllage yelds. Ths analyss ndcates that no-tll does not perform as well as tllage on a sandy-textured sol. For wheat and soybean on a sandy-textured sol, the lkelhood of lower no-tll yelds than tllage yelds was larger than on a loamytextured sol. Thus, no-tll may have lower mean yelds and greater downsde yeld rsk when wheat and soybean are grown on sandy sols. The length of tme that no-tll was used after converson from tllage had postve effects on the mean yelds for cotton. The tme after the converson from tllage to no-tll also mproved the probablty of havng hgher no-tll yelds when soybean and cotton were produced; therefore, the downsde yeld rsk was reduced wth tme for cotton and soybean produced wth no-tll. Annual ranfall ncreased the probablty of reduced no-tll yelds. Thus, there may be more downsde rsk assocated wth no-tll crop producton n regons where annual ranfall s hgher. Ths research showed that the dfferences between no-tll and tllage yelds n the southern regons of the Unted States were larger than n northern regons. The locaton of crop producton also affected the probablty of downsde yeld rsk. No-tll crop producton on a loamytextured sol n the Southern Seaboard regon was found to decrease the lkelhood of lower no-tll yelds compared wth the Heartland regon. Consequently, the favorable mean yelds and low downsde yeld rsk wth no-tll provdes rsk management benefts n the warmer and more humd clmates and warmer sols of the Southern Seaboard regon. The key fndngs of ths study support the hypothess that crop, sol, and clmate factors mpact no-tll yelds relatve to tllage yelds and may be an mportant factor nfluencng rsk and expected return and the adopton of the practce by farmers. The results of ths study also ndcate potental gaps n knowledge about the performance of no-tll relatve to tllage for alternatve crops and sols n dfferent farm resource regons. For example, there s a lack of data for corn and sorghum n the Msssspp Portal regon to corroborate the fndngs of postve advantages of no-tll that were observed n the Southern Seaboard regon. In addton, the results of ths study could be used to target ncentves to adopt no-tll to crops and regons where the mean yeld and downsde rsk tradeoff are not favorable. Gven the mportance of no-tll to the sustanablty of crop producton n the Unted States, future research Agronomy Journal Volume 04, Issue

11 Table 5. Predcted no-tll downsde yeld rsk probabltes relatve to tllage yelds by USDA Economc Research Servce farm resource regon (Fg. ) and sol texture. USDA-ERS regon Annual precptaton cm Sol texture Predcted no-tll downsde rsk probablty Corn Sorghum Cotton Soybean Wheat Oat Heartland 8.5 loam clay Northern Crescent 7.9 loam sand 0.50 Northern Great Plans 38.4 loam 0.38 Prare Gateway 64.8 loam clay Eastern Upland 96.7 loam Southern Seaboard 0. loam sand Frutful Rm 34. loam 0.78 sand 0.68 Basn and Range 22.0 loam 0.87 slt.00 Msssspp Portal 32.8 loam clay Predctons were calculated usng the estmated regresson coeff cents n Table 4, sample means for TECH (2.8) and LOGYR (.4) for each farm resource regon, and TILL =. Crop yeld data were not avalable for the farm resource regon and sol texture classf caton. should address those gaps n knowledge through strategc longterm experments and other research. REFERENCES Anderson, E.L No-tll effects on yeld and plant densty of maze hybrds. Agron. J. 78: do:0.234/agronj x Anderson, J.R., J.L. Dllon, and J.B. Hardaker Agrcultural decson analyss. Iowa State Unv. Press, Ames. Antle, J.M Econometrc estmaton of producers rsk atttudes. Am. J. Agrc. Econ. 69: do:0.2307/24687 Archer, D.W., and D.C. Recosky Economc performance of alternatve tllage systems n the northern Corn Belt. Agron. J. 0: do:0.234/agronj x Barnett, K.H No-tllage corn producton n an alfalfa grass sod. J. Prod. Agrc. 3:7 75. Blevns, R.L., D. Cook, S.H. Phllps, and R.E. Phllps. 97. Influence of notllage on sol mosture. Agron. J. 63: do:0.234/agronj x Bnswanger, H.P. 98. Atttudes toward rsk: Theoretcal mplcatons of an experment n rural Inda. Econ. J. 9: do:0.2307/ Bremer, J.E., S.D. Lvngston, R.D. Parker, and C.R. Stchler Conservaton tllage applcatons. Texas A&M Unv. Coop. Ext., College Staton. Buhler, D.D., and T.C. Mester. 99. Effect of tllage systems on the emergence depth of gant and green foxtal. Weed Sc. 39: Busscher, W.J., P.J. Bauer, and J.R. Frederck Deep tllage management for hgh strength southeastern USA Coastal Plan sols. Sol and Tllage Res. 85: do:0.06/j.stll Cardna, J., E. Regner, and D. Sparrow Velvetleaf (Abutlon theophrast) competton and economc threshold n conventonal and no-tllage corn (Zea mays). Weed Sc. 43:8 87. Cary, J.W., and R.L. Wlknson Perceved proftablty and farmers conservaton behavour. J. Agrc. Econ. 48: 3 2. do:0./j tb027.x Chatterjee, S., and P. Prce. 99. Regresson analyss by example. 2nd ed. Wley-Interscence, New York. Chavas, J.-P Rsk analyss n theory and practce. Elsever, New York. Chavas, J.-P., J.L. Posner, and J.L. Hedtcke Organc and conventonal producton systems n the Wsconsn Integrated Croppng Systems Tral: II. Economc and rsk analyss Agron. J. 0: do:0.234/agronj x Conservaton Technology Informaton Center Conservaton tllage facts. CTIC, W. Lafayette, IN. org/?acton=learnngcenter_core4_convotll (accessed 2 Apr. 2009). Cooper, H., and L.V. Hedges, edtors The handbook of research synthess. Russell Sage Foundaton, New York. Dao, T.H., and H.T. Nguyen Growth response of cultvars to conservaton tllage n a contnuous wheat croppng system. Agron. J. 8: do:0.234/agronj x. Dayton, C.M Model comparsons usng nformaton measures. J. Mod. Appl. Stat. Methods 2: DeFelce, M.S., P.R. Carter, and S.B. Mtchell Influence of tllage on corn and soybean yeld n the Unted States and Canada. Crop Manage. do:0.094/cm rs. Derpsch, R Hstory of crop producton, wth and wthout tllage. Leadng Edge 3(): Douglas, C.L., D.E. Wlkns, and D.B. Churchll Tllage, seed sze, and seed densty effects on performance of soft whte wnter wheat. Agron. J. 86: do:0.234/agronj x. Eckert, D.J Tllage system plantng date nteractons n corn producton. Agron. J. 76: do:0.234/agronj x Endale, D.M., H.H. Schomberg, D.S. Fsher, M.B. Jenkns, R.R. Sharpe, and M.L. Cabrera No-tll corn productvty n a southeastern Unted States Ultsol amended wth poultry ltter. Agron. J. 00: do:0.234/agronj Evanylo, G.K. 99. Potassum fertlzaton of doublecropped wheat and soybeans under two tllage systems. J. Prod. Agrc. 4: Fernandez-Cornejo, J., and W.D. McBrde Genetcally engneered crops for pest management n U.S. agrculture: Farm level effects. Agrc. Econ. Rep. AER-786. USDA Econ. Res. Serv., Washngton, DC. Fernandez-Cornejo, J., and W.D. McBrde Adopton of boengneered crops. Agrc. Econ. Rep. AER80. USDA Econ. Res. Serv., Washngton, DC. Fshburn, P.C Mean-rsk analyss wth rsk assocated wth below-target returns. Am. Econ. Rev. 67:6 26. Graven, L.M., and P.R. Carter. 99. Seed qualty effect on corn performance under conventonal and no-tllage systems. J. Prod. Agrc. 4: Harston, J.E., W.F. Jones, P.K. McConnaughey, L.K. Marshall, and K.B. Gll Tllage and fertlzer management effects on soybean growth and yeld on three Msssspp sols. J. Prod. Agrc. 3: Halvorson, A.D., A.R. Moser, C.A. Reule, and W.C. Bausch Ntrogen and tllage effects on rrgated contnuous corn yelds. Agron. J. 98:63 7. do:0.234/agronj Agronomy Journal Volume 04, Issue 2 202