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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%