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A SIMPLE METHOD FOR PREDICTING COCONUT YIELDS

S.G. Reynolds*

Philippine Journal of Coconut Studies. December 1979

INTRODUCTION

Forecasting of crop yields is now a widespread practice. Advance knowledge of yield levels is

vital to government planners and economists and by estate managers for policy formulation among others.

Also agronomists frequently need to record yields prior to harvests on farms or trials plots over which they

do not have complete control. Although Salter and Goode (1!"# noted the difficulties of establishingrelationships between climatic parameters and coconut yields$ Abeywardena (1!%# developed a crop&

forecasting formula based on rainfall with a multiple correlation coefficient of r ' .). *owever$ the use

of this formula is limited by the paucity of reliable climatic information. Also for relatively small areas it is

not sufficiently sensitive to show yield differences due to management factors. Smith (1!# developed a

method for recording potential fruit production on coconut palms with different levels of precision. At its

simplest level$ a count of the total number of fruit on each palm is an appro+imate measure of annual

production. ,n practice total nut counts from ground level are difficult to make. -his paper describes an

alternative system that compares predicted and actual yield totals.

MATERIALS AND METHODS

As part of a coconut&cattle raising proect (/eynolds et al.$ 1"% a and b# in 0estern Samoa$

accurate production records were required for a series of . and ).2 hectare paddocks to assess influenceof different grass species on coconut yields. ,n addition to the routine collection of fallen nuts$ sample tree

counts were made with the aid of a pair of binoculars. A stratified random sampling system was used so

that in each ).2 hectare paddock$ ) palms or between % and 13 of the palms were sampled. -he system

involved random selection of rows and then palms within each selected row. ,n nut counting only 4large5

nuts were recorded. -hus buttons and nuts smaller than about 1&1.2 cm in length by visual assessment

were ignored as they were difficult to count$ often being obscured by lower more mature nut bunches and

an unknown number would drop off before maturity.

-his sample was used to assess the mean number of 4large5 nuts per palm and multiplication by

the average number of palms per hectare gave a figure for the number of 4large5 nuts per hectare. ,n order

to assess total nut production per hectare per year this figure would have to be increased by a percentage

factor to allow for the unrecorded nuts smaller than 1&1.2 cm. -his was difficult to assess for practical

reasons$ but a tentative figure of 23 was adopted based on a careful count of nuts between about 2 cm and

1&1.2 cm in si6e. Smaller nuts were difficult to assess so it was e+pected that one result of this study

could be a refinement of this percentage factor.

Study Area I

7ounts of fallen nuts were made in four ).2 hectare paddocks from September 1") to 8ay

1"%. Sample tree counts were made in 9ctober 1")$ 1"2 and 1"". -he palms were of the Samoan -allvariety appro+imately years old in 1") spaced at .1) m + .1)m. -his area was used for preliminary

studies and to ascertain the measure of closeness of actual collected nut totals and the predicted figures.

Study Area II

:Pasture Agronomists with UNDP/FAO Project SA !"/##$% De&artment o' Agriculture% A&ia% (estern Samoa.


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7ounts of fallen nuts were made in 1) . hectare paddocks from ;une 1"! to 8ay 1"%.

Sample tree counts were made in >3.

7alculations of standard error show that figures for number of 4large5 nuts per tree are precise to

within ? >.%% to ? 2.2 at .23 probability level. -o estimate the paddock mean to ? 1$ ? 2 and ? > at .23 probability level$ samples consisting of !&$ 2&>2 and "&% palms respectively would be required.

Study Area II

)3 gives a predicted nut total which comes

closest to agreeing with the collection total.

As this is close to the >>3 derived in Area 1 and a mean factor for the four periods from both

areas is >>.23$ it is suggested that until more data are available$ a percentage factor of >>3 be adopted.

CONCLUSIONS

Although the method has been e+amined only in paddocks of si6es . and ).2 ha (and clearly

factors like climate and sampling time need to be e+amined in more detail# the close agreement between

nut totals based on collections and predictions suggests that the described method of selecting

appro+imately 13 of the palms in an area and counting 4large5 nuts$ i.e. greater than about 1 to 1.2 cmin length$ with the aid of a pair of binoculars to derive a mean figure for 4large5 nuts per palm is a useful

technique. -he mean figure multiplied by the number of palms per hectare and increased by a percentage

factor of >23 should result in a fairly precise estimate of annual coconut production per hectare.

9ne annual visit to a coconut plot or plantation would be sufficient to obtain a good estimate of

e+pected yield. ecause of the considerable amount of labor and high degree of uncertainty involved in

undertaking ground collection and counting of nuts$ it is suggested that the described method be used

instead. Actual copra yields can be estimated by deriving information on average dry copra yield per nut

from simple nut parameters as described by /eynolds (1"2#.


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ACKNOWLEDGEMENT

-he author is grateful to 8r. F. Bati and 8r. 8. Faamoe for their assistance with nut collections

and to 0S-C7 for the land allocation which made this proect possible. -he @epartment of Agriculture in

0estern Samoa and FA9$ /ome approved the publications of these results.

LITERATURE CITED

Abeywardena, V. 1968. Forecasting coconut crops using rainfall data (A preliminary study). !ird "ession FA# $or%ing &arty on 'oco. &rod., &rotand &roc., ndonesia 919 "eptember. 1968.

*eynolds, ". +. 19-. "ome notes on a coconut surey of t!e 6/ acre Vailele trial area. 0iest. And &ast . Agron. *ep. "er., o. 1, 2ept. Agric.,Apia. $. "amoa.

3333333333333, "truc%, *., 4ati, F., Faamoe, 5. and Faimata V. 198a. A report on p!ase of t!e cattle under coconuts gra7ing trial on 5alualoc% at Vailele, $estern "amoa, 1 April 191 April 198. 0iest. And &ast. Agron. *ep. "ep., o. :9, 2ept. Agric. Apia, $."amoa.

3333333333333, 198b. A report on p!ase : of t!e cattle under coconuts gra7ing trial on ew &lace loc% at Vailele, $estern "amoa, 1/ 5ay199 5ay 198. 0iest. and &ast. Agron. *ep. "er., o. /, Agric., Apia. $. "amoa.

"alter, &.;., and +oode, ;. &lant 'rops.

"mit!, *.$. 1969. !e '... met!ods of yield recording e?perimental coconut palms,'oco. nd. oard, @ingston, ;amaica, 5imeo s!eet.

Table 1 Nut !r"du#t$"% data $% Area I


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Padd"#&Nu'ber

O#t 1()* t" Se!t1()+

Nut!r"du#t$"%,-a,!er$"d

O#t"ber 1(). t" Se!te'ber1())

O#t"ber 1()) t" May1()/

Collected Predicted Collected Predicted Collected Predicted1 6,091 6,170 6,086 5,387 2,501 2,8392 6,019 5,560 6,506 5,352 3,805 3,146

3 5,787 5,599 5,493 5,251 3,091 3,2444 5,735 4,772 5,673 5,518 3,771 3,484X 5,908 5,525 5,940 5,377 3,292 3,178Differences between collected and predicted nt !ield for t"ree periods not si#nificant atP$0%05%

&able 2% 't prodction data in (rea ))

Paddoc* '+ber

't prodction"aperiod

Dece+ber 1976 to 'o-% 1977Collected Predicted

8 3,872 4,534

9 3,576 3,415

10 2,871 3,353

11 2,837 3,662

12 4,090 3,662

13 5,263 4,275

14 3,425 4,507

15 3,810 2,827

16 3,672 3,232

17 3,714 3,842

18 4,151 2,861

19 5,908 3,672

X 3,932 3,653

Differences between collected and predicted nt !ield not si#nificant at P$0%05%

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