Multi scale analysis of the factors influencing wheat quality as determined by Gluten Index

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Field Crops Research 123 (2011) 1–9

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Multi scale analysis of the factors influencing wheat quality as determined byGluten Index

D. Har Gil a, D.J. Bonfilb,∗, T. Svoraya,1

a Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israelb Field Crops and Natural Resources, The Institute of Plant Sciences, Agricultural Research Organization, Gilat Research Center, M.P. Negev, 85280, Israel

a r t i c l e i n f o

Article history:Received 23 November 2010Received in revised form 31 March 2011Accepted 2 April 2011

Keywords:Wheat qualityGluten IndexGenotypeEnvironmentNitrogen managementMoisture regimeGeostatistics

a b s t r a c t

In recent years, the Israeli wheat industry has suffered from quality problems that can endanger thepossibility of using the wheat to produce bread. The aim of this study is to understand how climatic andmanagement factors, such as N fertilization, emergence date and wheat cultivar, affect the Gluten Index(GI) – a main wheat quality parameter in the Israeli commercial market. The study was undertaken atindividual producer fields across southern Israel on three different scales: regional, farm, and the field.Overall, 1800 samples of 15 genotypes were collected, over three consecutive seasons, 2006–2008, fromcommercial fields throughout the area, from experiments conducted in the fields of the Gilat ResearchCenter, and in the fields of Kibbutz Sa’ad. Climatic variables were mapped exclusively and on the basisof maximum temperature and rainfall data, and were collected in the course of the entire season andduring certain periods. Relationships between climatic variables and GI were determined by multivariateregression analyses. On regional scale, the ability of explaining the differences was low, while on thefarm and field scale, variation explanation was generally higher. The factors that were found to be mostinfluential on wheat quality in southern Israel are: (1) type of variety; (2) negative influence of cumulativetemperature stress; (3) negative influence of excess water; and (4) negative influence of excess nitrogenfertilization. It was found that the genotype is the most important factor influencing GI, but environmentaland crop management factors also have a strong effect on the frequency and severity of GI depreciation.The probability of GI reduction increases in “too good” (excess fertilization, redundant irrigation) andin “too bad” conditions (water and temperature stress). Hence, when farmers make decisions regardingsowing, applying nitrogen and/or supplemental irrigation, they must also consider quality parameters,such as GI and not only yield quantity.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

In many countries, the economic importance of wheat (Triticumaestivum L. emend. Thell.) is great, as it constitutes the mostwidely grown field crop. The semiarid areas in the Northern Negevregion provide the major portion of Israel’s wheat grain crop area.This area experiences a wide range of climatic conditions withinand between growing seasons, spatially and temporally. Theseconditions cause substantial variability in grain production andquality. During recent years, both growers and bakers have neededto consider this quality fluctuation as a major issue, as in more and

Abbreviations: GI, Gluten Index; SI, stress index; WSI, water stress index.∗ Corresponding author. Tel.: +972 8 9928654; fax: +972 8 9926485.

E-mail addresses: [email protected] (D.H. Gil), [email protected] (D.J. Bon-fil), [email protected] (T. Svoray).

1 Tel.: +972 8 6472018; fax: +972 8 6472821.

more cases; wheat grains have not met the requirements of thebaking industry.

Several variables may determine wheat quality, including: phys-ical grain properties, protein percentage and composition, andstarch content and composition. Among the quality characteristics,seed storage protein content was found to be the most importantbaking quality feature for wheat (Dowell et al., 2008). Neverthe-less, variation in total protein content alone does not adequatelyexplain variation in bread making quality, as protein quality is animportant factor as well (Peterson et al., 1986). The Gluten Index(GI) is a method of analyzing wheat protein that provides simulta-neous determination of gluten quality and quantity (AACC, 2000).The GI value expresses a weight percentage of the wet glutenremaining on a sieve after automatic washing in salt solution andcentrifugation. GI allows a reliable prediction of bread making qual-ity (Curic et al., 2001; Dowell et al., 2008). At present, the Israeliwheat grain commercial market is based on three quality param-eters: grain protein, test weight, and GI. According to Curic et al.(2001), 75 < GI < 90 provides the optimal bread making quality for

0378-4290/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.fcr.2011.04.001


2 D. Har Gil et al. / Field Crops Research 123 (2011) 1–9

Central European cultivars. In Israel, grains with GI < 40 arerestricted to animal feed, hence their price is lower than for breadmaking grains. In addition, there are penalties for the 40–55 GI class,while the 55–100 GI class is considered suitable for bread. Measure-ments show that during the research period, wheat quality in about16% of the tested fields produced GI values below 40, while in somegrowing areas, the GI problem could rise to 30% of the wheat grainyield.

Comprehensive research on the impact of genotype, manage-ment factors, environmental factors and their interactions, onwheat protein quality has indicated that all four are significant.Depending on the climatic nature of the growing season, on wheatgenotypes and management application, the importance of eachone of these factors can exceed that of the others (Borghi et al., 1997;Panozzo and Eagles, 2000; López-Bellido et al., 2001; Garrido-Lestache et al., 2004). The mechanisms of stress processes thatinfluence wheat protein quality are very complex and depend onboth timing of stress relative to anthesis, as well as nitrogen avail-ability before and during the stress.

Temperature and moisture stress can directly affect the stor-age of protein through the process of protein polymerization, sincethey affect the mechanism by which intermolecular disulphidebonds are formed, or through changing the duration and timingof growth stages. Temperature and moisture stress before anthe-sis can influence wheat protein composition through sink/sourceeffects on nitrogen during early growth stages that affect yieldpotential and protein content (Triboï et al., 2006). High temper-atures and low relative humidity after anthesis exert an influencethrough changing glutenin:gliadin ratio (Blumenthal et al., 1993).This ratio changes because, at high temperatures, there is lowerreduction of gliadin accumulation than that of glutenin (Stone andSavin, 1999). Moisture stress may also affect the glutenin:gliadinratio by modifying the timing of the rapid protein polymeriza-tion, as it begins earlier than normal (Daniel and Triboï, 2002).Moreover, different wheat cultivars respond differently to tem-perature changes, heat shock and moisture stress (Lawrenceet al., 1988), since they have different alleles coded for differentgenes.

Wheat protein quality, as measured by GI, was found to be highlyinfluenced by environmental factors (Garrido-Lestache et al., 2004)and by the cultivar (Curic et al., 2001). Curic et al. (2001) alsoreported that substantial variation could be recorded within thesame cultivar under different climatic conditions. The geographi-cal latitude influences spatial variation of climate conditions andhas a significant effect on durum wheat quality (Rharrabti et al.,2003). The processes affecting agro-ecosystems operate at charac-teristic periodicities and spatial scales. There is a scale dependencyon the factors that characterize agro-ecosystems. At small scales,the effect of nutrients, water and soil type is dominant. On largerscales, the climate is more influential (O’Neill et al., 1991). The pos-sible scale effect on wheat yield and its components is imposedmainly by climatic conditions. Climatic processes have a strong top-down influence. On the other hand, spatial aggregation can havebottom-up effects (Viglizzo et al., 2005).

The climatic conditions of the Northern Negev region, typical ofMediterranean areas, are highly variable. Therefore, the resultingwheat is highly variable in end-use quality at the producer level, amatter of great concern for both producers and bakers. Even thoughthere is an increase in the importance of GI parameter in Israeligrain marketing, there is still a severe lack of knowledge on thatparticular parameter. Moreover, there is only sparse informationavailable on the influence of management factors and environmenton GI. The objectives of this study are therefore: (i) to understandwhich factors influence GI in the Northern Negev region; (ii) tograde the influences; and (iii) to evaluate the scale effect on thefactors influencing GI.

Fig. 1. Agricultural area of Northern Negev showing locations of the fields and thefarms analyzed at farm scale analysis.

2. Materials and methods

2.1. Study area

The study was carried out during the growing seasons of2006–2008 in the Northern Negev region in Israel (Fig. 1). The entireEastern Mediterranean region is characterized by a short rainy sea-son (November–April), increasing water deficit and thermal stressduring grain filling. The wheat production region of southern Israelextends from the Negev desert, a semiarid region in the south, tothe southern Israeli coastal plain and the “Shfela,” a mild Mediter-ranean climate region in the north. The mean annual precipitationranges from 478 mm in the north to 206 mm in the south andfrom 474 mm in the west to 240 mm in the east. For this study,wheat samples were collected from commercial fields across theentire wheat-growing region of Southern Israel. Approximately 40growers were solicited for cooperation on this project. During har-vest, growers collected and provided samples of approximately1 kg of the grain harvested from each study field. A few sampleswith wheat bug infestation were removed from the regional andfarm scale databases. Additional experiments were conducted dur-ing the seasons 2007 and 2008 at the Gilat Research Center at31◦20′N latitude and 34◦40′E longitude (average annual precipita-tion 230 mm) and at Sa’ad farm at 31◦28′N latitude and 34◦32′Elongitude (average annual precipitation 385 mm) for field scaleanalysis.

2.2. Experiments design

Regional scale: the regional scale analysis included 896 recordsfrom the three growing seasons. Wheat (hard spring) samples wereground into flour and GI was determined using a Perten Glutomatic,according to official standards (AACC, 2000) and under supervisedconditions, with a minimum of two repeats for each sample in theGilat field crop laboratory. Each sample was georeferenced and GISlayers were created to represent the wheat that each field hadproduced. The study focuses on 15 genotypes, some of them rel-atively new cultivars (Table 1). Each producer followed his ownnormal crop production practices and provided data on seeding andemergence date, irrigation/rain amount, fertilization and pesticideuse for each field included in the study. The emergence date wasclassified to three categories, early (before 2/December), normal(3/December–23/December) and late (after 23/December). Since,


D. Har Gil et al. / Field Crops Research 123 (2011) 1–9 3

Table 1Wheat genotypes (included in the study) phenology classification and their GI values – mean, standard deviation and number of records.

Cultivar Seed company Phenologya GI Fields

Mean STD n

Bar Nir Negev seeds Early ripening 92.8 8.0 35AG-15 Agridera Medium ripening 90.4 3.6 3Rotem Agridera Early ripening 84.6 17.8 77Ruta Negev seeds Late ripening 83.3 21.2 14Bet Hashita Hazera Early ripening 82.7 15.9 48Dariel Hazera Late ripening 78.3 21.9 29Negev Hazera Medium ripening 77.3 17.9 29Zahir Hazera Very early ripening 76.7 23.2 169Nirit Agridera Medium ripening 71.7 33.6 7Gedera Agridera Medium ripening 70.2 23.8 180Afik Agridera Very early ripening 67.7 22.0 6Ram Hazera Early ripening 67.5 20.4 18Yuval Agridera Very early ripening 62.6 29.8 151Omer Agridera Early ripening 60.9 31.4 13Galil Hazera Late ripening 44.2 27.0 117

a Phenology: very early, early, medium and late ripening groups, <70, 70–80, 80–90, >90 days, respectively, to reach heading under normal growth conditions.

in addition to annual climatic conditions, residual N present in thesoil from the preceding crop governs the effect of N fertilization,fertilization and preceding crop data couldn’t be included in thelarge scales analysis. All the commercial study fields had been suf-ficiently fertilized to the maximum yield level, therefore analysisaccording to nitrogen level was not required.

Farm scale: the farm scale analysis included 281 records from5 farms that represent different sub regions (Fig. 1) with relativelyuniform growing practices and fertilizer regimes for each grower.Each grower usually sows only 3–5 leading cultivars and appliessimilar crop management in his fields.

Field scale: field scale analysis included experiments at 10 fieldson loess soil at two sites and two growing seasons (2007–2008) toanalyze the within-field variation in GI. At Sa’ad site (Bonfil et al.,2008), during the 2007 growing season, two N (nitrogen) exper-iments were conducted following wheat crop at fields 18E and46E under irrigated conditions (513 mm). Base N treatment rangedfrom 20 to 220 kg ha−1. During the 2008 season, the base N ratewas tested following the wheat crop in three nearby fields (‘13E’,‘43’, ‘37’) under irrigated conditions (344 mm). Base N treatmentsranged from 20 to 70 kg ha−1 at ‘43’, from 50 to 130 kg ha−1 at ‘13E’and from 70 to 170 kg ha−1 at ‘37’.

The Gilat site is a Research Center that has been conduct-ing long-term multifactorial experiments since 1974. The currentexperiment took place at two fields with a layout of strip-splitblocks with four replications. One field was under “dryland” con-ditions (Dry) and the other under irrigated conditions (Irr), fortwo seasons (2007 and 2008). In each field, crop managementtreatments included the following factors: rotation (continuous vs.rotation), tillage (CT, MT and NT), base nitrogen application (0, 50,100 and 150 kg ha−1), and phosphorus (0 vs. 100 kg ha−1).

At Gilat ‘Gate’ field, an experiment was established during the2008 season that included the following factors: 7 cultivars, irri-gation (to a total of 350, 375, and 400 mm), and late top-dressfertilization (0 or 50 kg N ha−1 at heading). This field had high levelsof wheat bug (E. integriceps) infestation, therefore all samples wereexamined for infestation too (damage up to 16%).

2.3. Meteorological data

Daily meteorological data from 23 meteorological stations, pro-vided by The Israeli Meteorological Service and Israeli Ministry ofAgriculture, were used. Rain data were gathered from another 43stations for the whole season (Tot Rain) and for winter (Win Rain)(1/October–1/March). This information was used to calculate heatand moisture stress variables. The heat stress index (SI) was calcu-

lated by:

Stress index (SI) =∑

[Tmax − 30]

where Tmax is the maximum temperature for each day with max-imum temperature above 30 ◦C, for the period 1/Jan–20/May. Thisvariable was used by Panozzo and Eagles (2000) at the grain fillingperiod to explain the environmental variation for quality charac-ters. Since moisture stress is important too, the water stress index(WSI) was calculated by:

Water stress index (WSI) = SIrain + irrigation

Since WSI depends on water amount (rain plus additional irri-gation when given) it decreases dramatically in wetter areas andwhere the field received supplemental irrigation. Other indicesthat were calculated are: number of days with maximum tempera-ture above 30 ◦C during spring (1/March–31/May) and during April(the main grain-filling month); the average minimum daily relativehumidity for spring; and rain for April and spring periods.

2.3.1. Growing season conditionsA considerably high variation in climatic conditions in the stud-

ied areas is reflected in mean values for rainfall and stress variableswithin, as well as between, years (Table 2). Within years, pre-cipitation showed high variability, both spatially and temporally,with a high coefficient of variation (CV), up to 41. Between years,rainfall variability was also high. In 2008, precipitation was approx-imately 40% lower than that in 2007 (Table 2). In all three seasons,wheat crops were exposed to heat stress before and during ripen-ing. In general, the 2006 growing season had a little under the meanannual rainfall. April, which corresponds to the grain filling periodfor most of the fields, was rainy and hot. There were an unusualnumber of heat spells and rain amounts that were high above theannual mean, 40 mm in comparison to 15 mm annual mean. The2007 growing season was close to average annual precipitation,but hot and dry during the late spring.

The growing season of 2008 was hot and dry and experiencedone of the severest droughts in the last 50 years in the region. Theprecipitation only reached the level of 50–70% of the annual averageand from the beginning of February, there was almost no rain. TheSI average during the entire growing season was much higher in2008 than in 2006 and 2007 (Table 2). The SI spatial variation (Fig. 2)represents the common situation in the study area, which usuallyincreases Eastward as well as Southward (Kurtzman and Kadmon,1999).



Table 2Averages and coefficients of variation of GI and the growing season environmental variables.

Variablea 2006 2007 2008

Mean CV Mean CV Mean CV

Number of records 183 441 272GI 71 33 74 34 63 49Total Rain 320 19 354 26 204 33Win Rain 276 20 291 24 203 41SI 29 23 45 12 87 12NormWSI 11 46 16 54 40 47

a Variable abbreviations: GI – Gluten Index, Total Rain – whole season rain + irrigation, Win Rain – winter rain, SI – stress index, NormWSI – transformed water stressindex (using logarithmic transformation to achieve normal distribution).

2.4. Geostatistical analysis

The location of meteorological stations was georeferenced to theIsrael New Grid and GIS layers of the meteorological variables werecreated for each season. To provide prediction maps, the meteo-rological variables were interpolated using ordinary kriging (OK),a univariate interpolation method based on a weighting schemewidely used to estimate the unknown primary variable at unsam-pled locations, as a linear combination of neighboring locations(Burrough and McDonnell, 1998). Visual and statistical approacheswere used for semivariogram modeling and all semivariogramswere calculated isotropically, as suggested by Holdaway (1996).Exponential, Gaussian and Spherical models with nugget effectwere fitted and inspected by analyzing plots of residuals, the plot ofthe fitted model and the sample variogram. The best-fit model waschosen, based on the root mean square error (RMSE) that was cal-culated in a cross validation process. All final semivariograms had

Fig. 2. Stress index (SI) and the meteorological stations’ locations.

a nugget/sill ratio under 31%, with most of them being under 10%,which indicates a strong spatial structure (Kravchenko, 2003). Themeteorological grid maps were produced at a 100 meter grid size.The next step was to spatially join wheat field layers with the gridlayers of meteorological parameters and to analyze the relationshipbetween GI and meteorological variables.

2.5. Statistical analyses

All the meteorological variables were transformed to uniformvalues between 0 and 100 to bring them to a uniform scale forthe multivariate regressions. They were tested for normality usingthe Shapiro Wilkins test. WSI was transformed using logarithmictransformation to achieve normal distribution. Also, a correlationmatrix was calculated between all variables.

To determine factors influencing wheat quality, a quantitativerelationship test between GI and the different factors (cultivar,environment, and the management variables), was carried out bya series of multivariate regression analyses (MRA) for each dataset at regional, farm and field scales. All the statistical evaluationswere carried out using JMP (v7.0, SAS Institute Inc.). The MRA wereperformed using a stepwise approach with a mixed procedure atsignificant level of ˛ = 0.05. The regression analysis was separatedinto two steps: first, significant parameters were identified by thebest-performed model (highest r2). Subsequently, only the mostsignificant three independent variables in the model were includedin a new linear model, to reduce information and complexity arisingfrom the results. The same procedure was executed on the regionaland farm scales. Field scale analysis included a series of experimentswith the same methodology, but with different factors, focusingmainly in fertilization, cultivars, elevation, tillage, and rotation.

3. Results

3.1. Factors influencing wheat quality at the regional scale

It was found that of the 10 factors (8 meteorological indices,cultivar and emergence date type), only cultivar and SI affected GIsignificantly at the regional scale, with the cultivar being the mostimportant (Table 3). There was a significant difference betweengenotype GI characteristics (Table 1) and cultivar GI means rangedfrom 93 to 44. The phenology classification, in terms of durationuntil heading, seems to be related to GI values. The mean GI ofearly ripening cultivars was 82 (SD 19) higher than the mean oflate ripening cultivars, which was 54 (SD 30).

The large differences in meteorological conditions between sea-sons resulted in measurable differences in GI. Apparently, therelatively warm, dry weather of the 2008 season resulted in sig-nificantly lower GI values (Table 2). This effect was reflected inthe model results, as the second factor SI had a negative effect ofaccumulated heat stress on GI.



Table 3GI and transformed WSI mean values on regional and farm scales and regression analyses results (correlation and significant factors).

Data set Fields GI NormWSIa R2 Factorsb

n Mean Mean Cultivar Emergence date SI WSI Win Rain

Regional 896 70 24 0.30 1 2

Farm scaleEtzion 48 72 17 0.62 2 1Sa’ad 37 77 19 0.37 2 1Be’eri 73 71 23 0.56 1 3 2Shahrur 51 77 35 0.67 2 1Shikma 72 50 46 0.59 2 1

a NormWSI: transformed WSI, using logarithmic transformation to achieve normal distribution.b Factors: the number represents the grade of influence; the influence direction is shown by the symbol: negative – italic, positive – bold. Abbreviations: SI – stress index,

WSI – water stress index, Win Rain – winter rain.

3.2. Factors influencing wheat quality at the farm scale

As expected, at the farm scale, decreasing the number of recordsand increasing data source uniformity increases correlation whencomparing the data with the regional scale analysis (Table 3). Fac-tors influencing wheat quality at each farm are summarized below(listed by WSI).

The Etzion farm field experienced relatively high precipitation,353 mm mean total rain for the fields included in this study. Thisfarm sows 5 cultivars, yet the cultivar was only the second mostimportant factor, next to the negative effect of surplus water,as reflected in the WSI variable. A strong correlation was foundbetween WSI and GI values, as shown in Fig. 3. It can be noticedthat the negative effect of surplus water influenced all cultivars;for the “strong” cultivars (Beit Hashita and Rotem), the effect wasrelatively small, while for the others, the effect was highly signifi-cant. It is shown that no quality problem exists where NormWSI is>20, however it should be noted that this is not the case in otherareas (Table 3), possibly due to the combination of much highertemperatures and lower rainfall.

The Sa’ad farm represents a farm that adds supplemental irriga-tion in many fields, mainly to ensure good germination. Similarly tothe E’tzion farm, the cultivar at Sa’ad was only the second-rankedfactor, while the WSI was found to be the most important. The Sa’adfarm received a mean annual water amount (rainfall + irrigation) of

Fig. 3. GI relation to water stress index (normalized values) until GI saturated values(NormWSI = 17.7), at Etzion farm (n = 34).

372 mm in the fields included in this study. The negative effect ofwater surplus at the Sa’ad farm is an outcome of relatively highlevels of irrigation. From this analysis, it can be concluded that GIappears to be predominantly reduced under an abundant moistureregime.

At the Be’eri farm, with mean total precipitation of 258 mm, thecultivar was found to be the most important factor, while the pos-itive effect of winter rainfall came next. It was also found that lateemergence caused reduction in quality, in comparison to normal orearly periods.

The Shahrur farm is the most southern farm and has many irri-gated fields. The mean total water available was 247 mm, and thisfarm produced high quality grains (GI mean 77). Here, the mostimportant factor was the emergence date and the cultivar cameonly second. Like the Be’eri farm, it was found that late emergencewas less suitable and led to reduction in GI values.

The Shikma farm experienced 242 mm mean total precipita-tion in the fields included in this study. This farm had the severestWSI value and reached the highest SI, as it is located in the south-eastern part of the region, remote from the sea (and therefore haslower atmospheric humidity). The mean GI values of this farm werevery low (50). The winter rainfall amount was the primary factorinfluencing GI and the cultivar was second.

3.3. Factors influencing wheat quality at the field scale

At two N experiments (Sa’ad site, 2007 season, fields 18E and46E), GI values were significantly lower than the commerciallygrown cultivar mean in the same area and season, 50 vs. 67. How-ever, no correlation was found between GI and N rates for eitherfield (Table 4). Other factors that contributed to the variation inGI values at Sa’ad (2008 season) were soil texture and topographicelevation. At field ‘43’, there was an area with sandy soil that pro-duced grains with lower GI values. In field ‘37’, there is a variationin the topography that was evaluated by high precision DEM. Here,there was a negative relation of GI values to topographic elevation(Fig. 4). Vegetative growth and water depletion could also explainwhy there was a reduction in GI values in soil with a sandy tex-ture at field ‘43’ or surplus water in lower elevated area at ‘37’. Inthese fields, correlation was still very low, as the factor affectedGI only within a relatively small area. The results show significantdifferences between GI values at these different N rates. For twoof these three fields, a significant relationship was found in whichGI decreased as N fertilizer rates increased, from 94 GI mean at20 kg N ha−1 to 81 at 150 kg N ha−1.

At Gilat site during the 2007 season, in ‘Dry’ field that receivedonly 216 mm rainfall, wheat following clean fallow conditionsachieved better GI values than in the continuous wheat crop sys-tem (86 and 65, respectively). Increasing N and P rates were alsofound to reduce GI values from an average GI of 82 at no P to 69at 10 kg P ha−1 and from a GI average of 90 at 0 N rate to 69 at



Fig. 4. Field ‘37’: Contours lines, GI samples locations and a representative grid, and N fertilizer representative grid.

150 kg N ha−1. During the 2008 season the management treatmentsof the continuous system were divided into two groups with pref-erence for minimal tillage (average GI of 25 vs. 14) and a weaknegative effect of N rates. These results can be explained by thebetter water regime that existed under these management systems(clean fallow, low fertilization), thus enabling higher grain qualityproduction.

For the ‘Irr’ and ‘Dry’ plots, there is a mild slope of about 4.2◦,and a precise DEM was produced by photogrammetric process toevaluate the topographic effect. During the 2008 season, ‘Irr’ plothad 348 mm and GI mean was 84. A strong positive correlation wasfound to topographic elevation. Under the assumption that wateraccumulated in the lower areas of the field, this result reinforcesthe conclusion regarding the negative effect of surplus water men-tioned in the farm scale and field ‘37’ analysis. It was also foundthat tilled continuous wheat treatment had significantly lower GIvalues (69 as opposed to the 90 of other treatments).

At the Gilat ‘Gate’ field, wheat bug data show some varia-tion between cultivar infestation levels. However, the experimentdesign prevented a comprehensive conclusion on this topic. Sincebug damage data were available, infected samples from this datasetwere kept and the bug damage was added as a factor that couldaffect GI level. Despite the bug damage, the cultivar was found tobe more important than the factor of infestation rate, which camein second, while the negative effect of irrigation level was third. Ourfinding may explain why, approximately, only up to 30% of Israeliwheat quality problems can be explained by wheat bug infestation,which more often than not is restricted to only a few percentagepoints (Kostyukovsky and Zohar, 2004 and unpublished data).

4. Discussion

The regional regression model shows that the variation of GIcould be partly explained using observed environmental param-eters, the cultivar, and the category of emergence date (Table 3).Although only a relatively weak relationship was found (R2 = 0.30),

the model is highly significant (P < 0.0001). Growth conditions atany time and site are influenced by meteorological and manage-ment factors that, in turn, influence crop growth, soil N availability,soil water availability, weed infestation and others. Hence, the samemeteorological variable values do not consistently exert the sameeffect on grain quality. Moreover, the preceding crop, the tillagesystem and N fertilizer were all found to be key factors influenc-ing wheat quality (López-Bellido et al., 2001). On the regional scale,these factors were not included in the regression analysis, due toabsence of consistent management between growers.

The difference between late ripening cultivars to early ripen-ing cultivars can be explained by the earlier anthesis that allowsgrain filling to escape heat stress, which, in combination with waterstress, was found to reduce grain quality (Wardlaw et al., 2002).The difference between seasons is also related to heat stress and issimilar to the results that were reported by Garrido-Lestache et al.(2004), who found a negative correlation between GI and meantemperatures during May. Previously, Blumenthal et al. (1995) hadfound that dough breakdown was affected by high temperatures,with high temperature stress 30 days after anthesis being the mostimportant factor in controlled environments.

Approximately 60% of the variation in GI was explained in thefarm scale regression models, except for the Sa’ad farm. (Table 3).This is an interesting outcome, implying that the only meteorolog-ical variables that need to be measured during the growing seasonare daily rainfall and daily maximum temperature.

The emergence date effect that was found to be important at twofarms (Shahrur and Be’eri) shows similar results to those found byMotzo et al. (2007) in durum wheat. The emergence date affectswheat quality mostly due to the climatic conditions during thegrain-filling period. Late sowing leads to late anthesis, which forcesthe grain-filling period to extend into a high temperature regimewith low or no precipitation.

The farm scale results display the high influence of moistureregime on wheat quality in this region. At the lowest WSI farms(Etzion and Sa’ad), the negative effect of surplus water is marked



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with a positive effect of WSI as first factor (Table 3). A possi-ble explanation for the reduction in GI with the increase of soilmoisture can be a change in the ratio between gluten proteinsaccumulation at the late stage of the grain filling period. This canbe caused either by prolongation of the grain-filling period, whichleads to greater exposure to a stress regime, or by accumulation ofmore storage proteins in an unbalanced ratio.

An opposite finding regarding the effect of water was found atthe highest WSI farms (Shikma and Be’eri). There, a positive effectof winter rain was found as the first and the second factor, respec-tively. Based on these results, we can assume that under a severetemperature stress regime, water deficiency is the most importantlimiting factor for wheat quality. Gooding et al. (2003) found thatbread-making quality, determined by an SDS-volumes test, wasmore severely affected by drought stress before the end of grain-filling than by temperature stress or by later drought. It seems thatat the Shikma fields, this was the situation.

This result shows that the water effect is location-dependent: formoderate moisture regimes, the effect of surplus water is negativeand under conditions of severe water deficiency, the effect of wateris, as might be expected, positive. This suggests that the relativelylow correlation on the regional scale can occur due to oppositeeffects of environmental factors at different locations, where dif-ferent growth conditions prevail.

A similar finding of the water effect was found at field ‘37’ inSa’ad N fertilizer experiment. This field has a topographic diver-gence with mild slopes of maximum 3.7◦. It was found that at thehigher areas in the field, with less water, the GI values were higher(Fig. 4). It is possible that the combination of high fertilizer levels,surplus water and temperature stress result in damage at lowerareas because N fertilizer can lead to an extension of the grainfilling phase (Sinclair and Jamieson, 2006). Another finding thatsupports this hypothesis was found at field 43. Where it was foundthat the part of the field with sandy soil, and therefore a lowerholding capacity, provided lower GI values.

At field scale plots, other similar evidence regarding the moisterregime was found. During the 2008 season, the ‘Dry’ plot receivedonly 128 mm and had a significantly lower GI mean than for 2007(25 and 76, respectively). In this field, under severe moister regimeduring both seasons, it was found that there is preponderance ofminimal soil tillage management. López-Bellido et al. (1998), whoexamined the effect of tillage and crop rotation systems treat-ments on rheological properties of the dough by alveogram index,found a similar result. They found the NT system to be preferential.Such crop management increases the amount of available water,enabling wheat quality improvement.

Nitrogen application rates had a consistent effect. When sur-plus nitrogen is added relative to production, it leads to reductionin GI. This agrees with the findings of Garrido-Lestache et al. (2004).They also found that the higher the N rate, the sensitivity to meantemperature during May increased and the decline in GI valueswere steeper. High N fertilizer rates can affect many yield com-ponents that also influence grain-filling. One example is the factthat vegetative growth of the stalks is promoted, which can resultin greater soil water depletion, which then enhances the WSI dur-ing late grain-filling. This effect can explain why at Sa’ad, underthe mild conditions of the 2007 season (513 mm water and mildheat stress), the negative influence of N was insignificant, but underthe severe conditions of 2008 (344 mm and severe heat stress) theinfluence became significant.

We found here that the cultivar is the most important factorinfluencing GI, but environmental and management factors exert astrong influence on the frequency and the severity of GI depre-ciation. The probability of GI reduction increases in “too good”(excess fertilization, redundant irrigation) and in “too bad” condi-tions (water and temperature stress). GI reduction under abundant



moisture, in arid environments, can be explained by dilution ofearly nitrogen reserves by vegetative growth due to rainfall priorto grain filling (Gooding and Davies, 1997). This can enhance theeffect of heat stress at later stages of grain filling and can also causereduction in GI values.

The regional scale analysis demonstrates the negative correla-tion with temperature stress during the season, in agreement withthe findings of Garrido-Lestache et al. (2004). A closer examina-tion, at the farm scale, reveals that, for different areas, there arevarious factors with different influential results. Under conditionswhere water is not the limiting factor to yield, the possibility for GIreduction increases as water amounts increase. But when water isa limiting factor, the correlation becomes positive. The field scaleanalysis included reliable and accurate data that supported thesefindings. In addition, field scale analysis exhibited other factorsaffecting GI.

A multi-scale approach combining data from field, farm andregion, permits a more educated systematic analysis. This approachallows the investigation of several factors and unravels possiblescale effects in actual spatial variability (Veldkamp et al., 2001).In this study, implementation of such a multi-scale approach hastwo main advantages. First, this approach is preferable to a rep-resentative sample from all regions, because some environmentalfactors undergo a gradual change (e.g., temperature) and oth-ers have a spatial discontinuity that can vary even for a smalllocal area (precipitation). Second, the multi-scale test implemen-tation enabled an examination of the overall results and thuscontributed to the conclusions reached in this study, by strengthen-ing the overall results, due to reversion of similar results at severalscales.

Our results also show that the genetic factor has more influenceon wheat grain quality than bug damage, even in rare cases wheninfestation rates are high. Wheat bug infestation causes damagesand reduction in GI by inserting proteases into the grain and dis-rupting protein (Aja et al., 2004), with variation between wheatcultivars sensitivity. In the literature, there is evidence first, on thedifference in the ability of the proteases to disrupt different stor-age proteins for different cultivars (Rosell et al., 2002) and second,on the level of tolerance to wheat bug damage for various cultivars(Sivri et al., 2002). Our data also show some variation between theIsraeli cultivars infestation levels.

5. Conclusions

In conclusion, the results of this study indicate that genotype,environmental conditions and management factors have a highinfluence on wheat quality in the Northern Negev, Israel. It wasfound that the cultivar is the most important factor influencingGI, but that environmental and management factors also have asubstantial influence on the frequency and the severity of GI depre-ciation. The probability of GI reduction increases under “too good”(excess fertilization, redundant irrigation) and “too bad” conditions(water and temperature stress). Quality characteristics of the culti-vars could not be discussed in isolation, since the growers considerboth quality and yield when choosing a suitable cultivar. However,the relationship between GI, cultivar and yield is beyond the scopeof this study. It is suggested that in future work, the effect of theseenvironmental factors on each cultivar be examined.

Although, clearly, the growers cannot influence climatic condi-tions, they can achieve better wheat quality by choosing a suitablecultivar and sowing date and by executing a better managementsystem. In some cases, an early-normal planting date can be usedas a strategy to reduce the impact of heat stress. We suggest thatwhen cultivars are chosen, consideration of the GI parameter mustbe the first step. Decisions on applying nitrogen and/or supplemen-tal irrigation, quantity and timing, must consider the GI quality and

not only yield quantity. Hence, many sophisticated decisions mustbe taken for which a decision support system is required.

The results of this study show that weather-based regressionmodels, based on the creation of digital grid maps, from justdaily maximum temperature and precipitation, can be efficient inexplaining variation in wheat quality and can therefore be used fordecision-making, as well as for building a decision support systemin the future.

Acknowledgements

This research was partially supported by grants from the ChiefScientist of the Israeli Ministry of Agriculture and by the Israeli FieldCrops Board. The authors thank Mrs. D. Cohen and Mrs. S. Asido fortheir skillful assistance in the laboratory, Dr. M. Kostyukovsky forhis assistance in bug damage analysis, and Mr. U. Naftalyahu for hisassistance in collecting samples.

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Multi scale analysis of the factors influencing wheat quality as determined by Gluten Index

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