CALIFORNIA AGRICULTURE, JANUARY-FEBRUARY 2001 25

Peach trees perform similarlydespite different irrigationscheduling methodsDavid A. Goldhamer ❏ Mario Salinas ❏ Merce Soler AnayaAlfonso Moriana Elvira

There are numerous techniquesfor scientifically scheduling irriga-tions in tree fruit orchards. Theseapproaches involve measuringsoil, plant or atmospheric param-eters, then using this informationto determine when to irrigate andhow much water to apply. Westudied the effects of the differentirrigation scheduling methods onpeach trees in Tulare County. Oneof the key aspects of irrigationscheduling is being able to inter-pret the measurements so that theresulting water management deci-sions produce maximum growerprofit with the minimum amount ofwater. Thus the measurementsmust not only be accurately taken,but protocols for their interpreta-tion must be reliable in terms ofachieving optimal tree perfor-mance without wasting water.This requires a marriage of thetechnology used to take the mea-surement and the science usedto develop the interpretationguidelines. When this is success-fully done, we found that themethod of scheduling irrigationshad no effect on the peach trees’performance.

Soil-based irrigation schedulingtechniques involve measuring ei-

ther soil water content or a soil prop-erty related to soil water content. Ingeneral, the objective is to irrigate insuch a way that soil water status in theroot zone of the tree remains withinthe ideal range for root extraction,

thus ensuring maximum soil wateruptake. Soil measurements are takenusing instruments placed at variousdepths in the root zone, such as tensi-ometers, electrical resistance blocks,neutron probes, and time and fre-quency domain reflectometry probes.Major issues with soil water measure-ments are that they may not indicateactual conditions at the soil-root inter-face and may only indirectly reflecttree water status.

Plant-based irrigation schedulingbecame more feasible in the early1960s with the development of pres-sure chambers that allowed leaf waterpotential to be measured. This in-volves placing an excised leaf in ametal vessel with the cut end of thepetiole protruding through an air-tightseal. Compressed gas is injected intothe vessel until xylem fluid appears atthe end of the petiole. The gas pres-sure at this point is considered to beequivalent to the absolute value of thexylem pressure potential, which isnearly equivalent to leaf water poten-tial. If irrigation is not adequate tomeet the potential water use of thetree, the leaf water potential becomesmore negative. Another tree water sta-tus indicator is stem water potential(SWP), which is measured by placing afoil-covered plastic bag over the leaf afew hours prior to taking the pressurechamber measurement.

Another plant-based approach to ir-rigation scheduling involves assessingthe small changes in trunk diameterthat occur over the day. Trunk-mounted linear variable displacementtransducers (LVDTs) can measure

unfortunate from the industry’s per-spective that expenditures were cur-tailed from 1994/95 through 1996/97due to litigation. Our analysis suggeststhat suspension of the advertising pro-gram during this period cost the in-dustry accumulated profits in therange of $90 million to $234 million.

Although our focus is on promo-tion, it is worth noting that this studyhas also provided some new evidenceabout the price elasticity of demandfor almonds in the United States. Theestimates suggest that the elasticity isin the range of –0.35 (shorter time se-ries) to –0.70 (full time series). There-fore the industry is operating in the in-elastic portion of its demand curve (atleast in the U.S. market), and the largeharvests anticipated now and in thefuture will cause major decreases inproducer prices, unless the industry isable to stimulate demand through pro-motions or other means.

J. Crespi recently received his Ph.D. fromthe Department of Agricultural and Re-source Economics, UC Davis, and is nowAssistant Professor in the Department ofAgricultural Economics, Kansas StateUniversity. R. Sexton is Professor, De-partment of Agricultural and ResourceEconomics, UC Davis, and a member ofthe Giannini Foundation of AgriculturalEconomics.

The authors are grateful to the staff ofthe Almond Board of California for con-tributing data to the study. Blue DiamondGrowers also contributed key data to thestudy, and the cooperation of BlueDiamond’s Director of International Sales,Rex Lake, is much appreciated. The au-thors are also grateful for the comments oftwo anonymous referees.

ReferencesAlston JM, Carman HF, Christian J, et al.

1995. Optimal Reserve and Export Policiesfor the California Almond Industry: Theory,Econometrics and Simulations. GianniniFoundation Monograph no. 42. 130 p.

Alston JM, Chalfant JA, Christian JE, et al.1997. The California Table GrapeCommission’s Promotion Program: An Evalu-ation. Giannini Foundation Monograph no.43. 120 p.

Bushnell PG, King G. 1986. The Domesticand Export Markets for California Almonds.Giannini Foundation Research Report no.334. 84 p.

26 CALIFORNIA AGRICULTURE, VOLUME 55, NUMBER 1

trunk or scaffold diameter precisely(to the micron level), and various indi-cator parameters, such as maximumdaily trunk shrinkage and daily trunkgrowth, can be gleaned from the data.These parameters can indicate the ad-equacy of irrigation.

The atmospheric-based technique ofirrigation scheduling uses weatherdata and meteorological models to cal-culate a reference crop water use (ETo)value. That approach, also known asthe water budget, is facilitated by theCalifornia Irrigation Management andInformation System (CIMIS), a net-work of automated weather stationsoperated by the California Departmentof Water Resources. Crop water use —

that is, evapotranspiration (Etc) — isestimated by multiplying ETo by cropcoefficients (Kcs) that are tree-speciesspecific. This technique is the only ap-proach that supplies actual tree wateruse (gallons/tree/day) data, making itrelatively easy, especially for growerswith drip or microsprinkler irrigation,which have known application rates,to apply water to match the ETcthroughout the season.

Four scheduling techniques

We conducted an experiment in1999 on second-year peach trees (‘Sep-tember Snow’) to evaluate what weconsidered to be optimized, state-of-the-art soil, plant and atmospheric-

based irrigation scheduling tech-niques. This work took place at theEdison International AgTAC facility inTulare County, where the soil is asandy loam. To maximize the numberof trees in our relatively small area,tree spacing was 12 × 6 feet, with aperpendicular V training system.

We evaluated one soil-based, twoplant-based and one atmospheric-based irrigation scheduling techniques.We also maintained an irrigation treat-ment that applied water in excess ofestimated ETc, ensuring that we hadsome trees that were fully irrigatedand could provide baseline values oftree water status. Irrigation rates inthis treatment, hereafter referred toas FULL, were 30% greater than theatmospheric-based treatment fromlate May through mid-July. At thattime, symptoms of anoxia appearedin the trees and FULL rates werelowered to those in the atmospheric-based regime.

The five irrigation regimes wereeach replicated four times. Each repli-cate plot was three rows × five trees,and the interior three trees were moni-

Leaf conductance is measured by Mario Salinas and Alfonso Moriana.

Attachment strap on mounting bracket oflinear variable displacement transducer(LVDT) is affixed to the tree trunk.

Linear variable displacement transducerused to measure trunk diameter fluctua-tions. Aluminum foil protects sensor fromsun and wind.

CALIFORNIA AGRICULTURE, JANUARY-FEBRUARY 2001 27

TABLE 1. Applied water, harvest yield components and canopy size data for 1999

Applied Fruit size distributionIrrigation water Harvest Harvest Harvest Harvest Harvest Orchardscheduling through fruit fruit fresh dry fruit water Undersize 56–48 Oversize shaded areamethod harvest load diameter fruit wt. fruit wt. content 64 fruit/box fruit/box 42 fruit/box Aug 5, 1999

inches no./tree mm ........... gm/fruit ........... % by wt. ....................... % by no. ........................ % floor

SOIL 18.1 12.8 73.6 201 43.7 78.2 33.4 47.5 19.1 32.9ATM 19.7 10.6 73.8 201 44.9 77.7 28.6 53.0 18.4 35.4LVDT 19.9 11.7 75.4 214 43.7 79.6 19.8 52.4 27.7 38.4STEM 21.5 12.4 74.9 211 46.9 77.7 25.2 45.8 29.0 36.3

NSD NSD NSD NSD NSD NSD NSD NSD NSD

* NSD indicates no statistically significant differences between treatments using Duncan’s Multiple Range Test at the 5% confidence level.

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tored. Drip irrigation was applieddaily, with two 1 gal/hr emitters pertree. Irrigation amounts were mea-sured with water meters. Normalgrower fertilization (through the irri-gation system) and weed, disease andinsect control were used.

The trees were thinned to 17 fruitper tree in March. During the season,calipers were used to measure fruit di-ameter on four tagged fruit on each ofthree trees per replicate. The fruit washarvested on Aug. 29, which also sig-naled the end of the differential irriga-tion treatments. All fruit was sized ona commercial sorter to assess industrysize categories (fruit per box). In addi-tion, diameters were measured withcalipers. Fresh and dry fruit weights ofsix randomly collected fruit per repli-cate were determined.

Techniques evaluated

All trees were irrigated with theatmospheric-based approach throughlate April, when the following treat-ments were initiated.

Soil based. The soil-based treat-ment, hereafter referred to as SOIL,employed EnviroScan probes (Sentek,Ltd.), which use frequency domain re-flectometry to assess soil water status.Single probes were installed on onetree in each replicate of the SOIL treat-ment. Single probes were also installedin one replicate of each of the otherfour irrigation regimes. Each probe al-lows the actual sensors to be set at spe-cific depths. In this study, sensorswere set at 6, 12, 24 and 36 inches. Thisdevice provides frequent measure-ments (every 15 minutes) that indicateboth trends in soil water status and to-tal amounts in the monitored profile.Our management protocol was based

primarily on maintaining 17 to 18 acre-inches of total soil water in the moni-tored depth (42 inches). This valuewas chosen based on soil water levelsachieved at the end of the previousseason, after rainfall and irrigation re-filled the profile. We also adjusted irri-gation in this treatment based on therate of change in total soil water. If ac-tual soil water was below or above thedesired level, irrigation amounts forthe following week were increased ordecreased by 5% or 10%.

Plant based. For the STEM treat-ment, stem water potential was mea-sured weekly with a pressure chamber(Model 3005 Soil Moisture EquipmentCo., Santa Barbara) on single leaves oneach of three trees per replicate. Plasticfoil-covered bags were placed on inte-rior shaded leaves at about 10 a.m. andthe measurements were taken betweennoon and 2 P.M.

Interpretation of SWP values for ir-rigation scheduling is complicated bythe fact that SWP is influenced byweather conditions. Therefore SWPmay decrease with time over the sea-son regardless of the adequacy of irri-gation simply because of increasingevaporative demand. There may alsobe tree-species-related differences inSWP for the same weather conditions.Although recent research by KenShackel has provided SWP guidelinesfor fully irrigated prune trees that arebased on relative humidity and airtemperature, we choose to interpretour SWP values in relation to the satis-fied treatment. If mean weekly SWP inthat treatment was more negative thanthe SWP in the well-watered treat-ment, then the following week’s irriga-tion rate for the SWP treatment was in-creased by 5% or 10%. Conversely, if

the SWP of that treatment and thewell-watered treatment were nearlyequal, then the following week’s irri-gation rate of the SWP treatment waslowered by 5% or 10%.

In the scheduling regime based ontrunk diameter fluctuations (hereafterreferred to as the LVDT treatment),sensors were placed 18 inches abovethe soil on each of three trees per repli-cate. Single trees in each replicate ofthe other scheduling approaches werealso instrumented with LVDTs.Trunk diameter was recorded every12 minutes.

Previous work has demonstratedthat although maximum daily trunkshrinkage (MDS) is a sensitive indica-tor of tree water status for maturetrees, it is much less useful for youngtrees. On the other hand, daily trunkgrowth (TDG) appears to be a goodwater stress indicator for young trees.We managed the irrigation in theLVDT treatment by comparing TDG inthat treatment with TDG in the FULLregime. Because we cut back irrigationrates in the FULL treatment in mid-July and were concerned that the TDG,being very sensitive to even mild wa-ter deficits that might develop in the

Fig. 1. Irrigation rates for each irrigationscheduling approach from late-Aprilthrough harvest.

28 CALIFORNIA AGRICULTURE, VOLUME 55, NUMBER 1

FULL trees, would fall below potentialvalues, we increased the measuredTDG values in the FULL treatment by15% from mid-July through harvest.Otherwise, management logistics werethe same as described for the STEMtreatment.

Atmospheric based. Data from theVisalia CIMIS weather station providedweekly ETo values. The Kc values weretaken from recent research on youngpeach trees by Scott Johnson at theKearney Agricultural Center. The result-ing mean estimated ETc for a givenweek was used to set the irrigation rateof this treatment (hereafter referred to asATM) for the following week.

Seasonal irrigation differences

The scheduling approaches re-sulted in different rates and seasonalamounts of applied water (table 1,fig. 1). It is notable that ATM applica-tion rates peaked in late June at 9.2gal/tree/day, while irrigation rateswith the other approaches peaked inlate July or early August. In terms oftotal irrigation applied to the harvest,SOIL applied the least (18.1 inches)and STEM the most (21.5 inches).

Protocol guidelines followed

The success of any irrigation sched-uling approach depends not only ongood instrument technology and validscientific development of the data in-terpretation protocol, but also onwhether the water managementmatches the protocol; that is, on howskillfully the manager adjusts irriga-tion based on the observed data. Fig-ures 2, 3 and 4 illustrate this for theSOIL, STEM and LVDT techniques,respectively.

Total soil water remained relativelyunchanged at 17 to 18 inches throughmid-June, followed by a gradual de-cline to slightly below 17 inches inearly July (fig. 2). This triggered an in-crease in irrigation rates in early tomid-July (fig. 1). The increased irriga-tion rates resulted in a rapid increasein total soil water that reached about19 inches in late July. This suggeststhat we did not do the best job of man-aging water in this treatment, and thatdaily evaluation of total soil waterwould have been preferable to ourweekly evaluation.

SWP measured was the same inboth the STEM treatment and theFULL regime except from mid-June toearly July, when STEM SWP was morenegative (fig. 3). This occurred eventhough we rapidly increased irrigationrates during this period (fig. 1). This il-lustrates the difficulty of translating aplant-based measurement into a water-management decision. In retrospect,we should have changed irrigationrates in increments greater than 5% to10% weekly. The same can be said forthe water management in the LVDTtreatment; relatively low TDG in bothmid-June and early July (fig. 4) trig-

gered increased irrigation (fig. 1), butthe 5% to 10% weekly rate increaseswere not enough to accelerate TDG toFULL treatment TDG rates.

Tree performance

Perhaps the best indicator of irriga-tion adequacy is the tree stress level,which is reflected by the SWP. FromMay through fruit harvest, there werevirtually no significant differences inSWP for the different irrigation sched-uling approaches, except for mid-July,when STEM levels were less negativethan the other treatments. Not surpris-ingly, irrigation rates in STEM werehigher than in the other regimes dur-ing this period (fig. 1).

There were no significant differ-ences between scheduling approachesin harvest fruit load, fruit size, freshand dry fruit weights, fruit hydration,fruit size distribution and tree growth(table 1). The fact that there was littledifference in seasonal SWP, harvestedfruit size and tree growth betweentreatments should not be surprising;all of the scheduling approaches arebased on sound agronomic principles,and all are designed to avoid signifi-cant tree stress and to achieve maxi-mum vegetative and reproductiongrowth.

Measurement variability

The water budget (ATM) approachis the only one that uses singular esti-mates (ETo and Kc) to develop man-agement parameters; in this case, ETc.Therefore, even though there is uncer-tainty in the ETc estimate due to inac-curacies in the algorithm used to cal-culate ETo and variability in the Kcvalue that existed in the experimentaldata used for its development, this un-certainty is not reflected in the waterbudget scheduling procedure. On theother hand, there is variability in thesoil-based and plant-based measure-ments. This variability depends partlyon the number of instruments usedper monitored area, but it is also re-lated to the measurement itself.

Measurement variability can be ex-pressed as the coefficient of variation(CV), which is the standard deviationdivided by the mean. For a valid com-

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Fig. 3. Stem water potential in the STEMand FULL treatments through harvest.Vertical bars are two standard errors ofthe mean.

Fig. 4. Trunk diameter growth in the LVDTand FULL irrigation treatments.

CALIFORNIA AGRICULTURE, JANUARY-FEBRUARY 2001 29

parison of the CV for the differentscheduling approaches, the same num-ber of measurements must be consid-ered for each approach. This situationoccurred in the SOIL treatment, whereeach of the four replicates had oneEnviroScan probe, LVDT and SWPvalue taken from the same tree. Al-though total soil water and SWP CVswere generally in the 4% to 8% range,the CV with TDG was much greater,reaching 45% in mid-July. We previ-ously observed this high “noise” levelin LVDT-derived measurements frommature trees. In that case, the noise de-creased as tree stress increased. Addi-tionally, the strength of the “signal”(relative change in the indicatorvalue as stress increased) for theLVDT-derived parameter was muchgreater than the SWP signal. There-fore the signal/noise ratios of bothplant-based approaches (SWP andLVDT derived) were similar undermild to moderate stress conditions.

Which approach is best?

The ideal irrigation scheduling ap-proach would have the following char-acteristics: measurements would be ac-curate, reproducible, and easy to takeand interpret; be electronic with easilyautomated and continuous data collec-tion; have visual readouts; and couldbe directly coupled to an irrigationcontroller that contained software toautomatically manage irrigation. Ad-ditionally, the monitoring instrumentswould be inexpensive to buy, operateand maintain. Finally, their use wouldresult in maximum grower profit withminimum water use. Clearly, no singlescheduling approach currently pos-sesses all of these characteristics.

We believe that the future is brightfor plant-based sensors because oftheir ability to reflect small changes inwater status, changes that would prob-ably have no negative effect on cropproduction. Because they take continu-ous measurements, management pro-tocols can be developed that makethese stand-alone scheduling tools. Forexample, daily irrigation could be in-crementally reduced until the indi-cated tree stress exceeds a mild thresh-old level. This would trigger an

incremental in-crease in thedaily irrigationrate until a well-watered thresh-old is achievedand the entirecycle is repeated.

As advancescontinue to bemade in electron-ics and data acqui-sition, and as re-search progresseson developingprotocols for us-ing soil-based andplant-based mea-surements for irri-gation decisionmaking, it is likelythat the cost andutility of the con-tinuously re-corded measure-ments will becomemore attractive.Nevertheless, wecurrently recom-mend that grow-ers use the waterbudget (ATM) toprovide a first ap-proximation, astarting point for irrigation manage-ment, and then use a soil-based orplant-based approach to fine tune de-cision making.

D.A. Goldhamer is UC Cooperative Ex-tension Water Management Specialist andM. Salinas is Research Associate, KearneyAgricultural Center, Parlier. M. SolerAnaya is former technician, Institut deRecerca I Tecnologia Agroalimentaries,Lleida, Spain, and A. Moriana Elvira istechnician, IAS-CSIC, Cordoba, Spain.

The authors gratefully acknowledgethe assistance of Kevin Day, SteveSibbett and Bob Beede, UC Farm Advi-sors, and Scott Johnson, UC PomologySpecialist. They acknowledge the fund-ing of EPRI, Edison International andthe California Energy Commission. Theyalso express appreciation to GregSchwaller and the Edison InternationalAgTAC staff and the following field as-

sistants: Jesus Salinas, Miguel Marquez,Dan Howes and Miguel Sans.

Additional readingFereres E, Goldhamer DA. 1990. Decidu-

ous fruit and nut trees. In: Stewart BA. andNielsen DR (eds.). Irrigation of AgriculturalCrops. Madison WI: Amer Soc Agron (Mono-graph no. 30). p 987-1017.

Goldhamer DA, Fereres E, Mata M, et al.1999. Sensitivity of continuous and discreteplant and soil water stress monitoring inpeach trees subjected to deficit irrigation. JAmer Soc Hort Sci 124(4):437-44.

Goldhamer DA, Snyder RL (eds.). 1989.Irrigation Scheduling: A Guide for On-FarmWater Management. UC Div Ag Nat Res,Publication no. 21454. 69 p.

Paltineau IC, Starr JL. 1997. Real-timesoil water dynamics using multisensor capaci-tance probes: Laboratory calibration. Soil SciSoc Am J 61(6):1576-85.

Shackel KA, Ahmadi H, Biasi W, et al.1997. Plant water status as an index of irriga-tion need in deciduous fruit trees. Hort Tech-nology 7(1):23-9.

Stem water potential is measured by,from left, Miquel Marquez, AlfonsoMoriana and Jesus Salinas.