Vergara - 1976 - Physiological and morphological adaptability of ri

208 cits-ems awn RICE
DISCUSSION
TANAKA (Chaimian): Dr. Munakata presented a comprehensive review of statistical analyses
of relationships between grain yield, spikelet number. and ripening grade. and the climatic
conditions during panicle-primordia development and ripening. along with available physiological
infomiation on related subjects in thc literature.
The statistical approach is certainly a useful method in predicting the grain yield of a crop
before harvest. from data on the climatic conditions under which the crop is growing. He tried
further to relate the results of statistical analyses to various physiological phenomena. Many
tendencies disclosed by statistical analysis are well explained by or do not conflict with phsiological
information obtained under controlled experimental conditions. However. I feel that his
statistical analyses have limitations because these were carried out by pooling heterogeneous
data, including the tbllotving:
(a) Various varieties included in his survey differ in their response to temperature or light.
(b) Since the cultural methods. soil, and seasonal change of climatic conditions during the
growth are different. the population situation at a given growth stage. such as at llowering,
is dilferent from case to case.
(c) The response of a rice plant to a climatic factor at a given growth stage is influenced by
the physiological status of plants at that growth stage, which is the result of environmental
conditions in previous growth stages.
I can understand the difiiculty of statistical approach. although I am not very familiar with
it. However. I would like to have some more biological influence in such a method and to see
that statistical analysis can be used not only to predict the grain yield or to describe the phenomena.
but also to establish methods to improve productivity.
l do not know how to do it. But. if there is more information on the relationships between
an individual physiological process and the environmental condition, it may be possible for
statistical workers to compose more realistic simulations that make biological sense. If this is
the case. l would like to request them to indicate here and now that type of information they
require to compose simulations which have implications for the improvement of crop productivity’.
Nlany experts who are intending to carry out experiments along that line are participating
in this symposium.
FISCHER: What surprises me is how closely one can relate farmers‘ yields to climate. in other
words how fertility. tareeds. poor water management, birds, etc, have been eliminated by the
farmers. 0r were the fields somehow selected later for freedom from such obvious limitations
to jeield’?
itfrmokata: Since rice culture in Japan is supported by high levels of cultural practices. and
extension services. such obvious limitations in rice yield as those you mentioned are comparatively
unimportant. Thus. rice production appears to be dependent strongly on climatic factors.
especially solar radiation and temperature. In the warm region of Japan there is much evidence
that the rice yield is higher in highland areas than in lowland. In order to determine the cause,
many trials mere conducted. As a result. higher yield in highland areas was attributed to climatic
lhctors. especially temperature. rather than soil factors.
NEELEY: 1. When identifying critical points. you place a great deal of emphasis on identifying
which stage gives the largest multiple correlation coefficient. You mentioned the problem of
differences in variation of light and temperature at different grovnh stagey. But even when that
is taken into account. it must be kept in mind that the R? values only indicate how well the
model used fits the data. For example. just because the R-‘ value was less for ripening at heading
stage than at [ 20] and [+20]. does not necessarily‘ mean that temperature and light were less
important at heading. but may only mean that the model was not as good at the heading stage.
Further. it is difficult to compare the R‘ values at one stage to the R’ at another because the
R~‘ values are interdependent. From what was presented, it appears that the same dependent
variable data were used at each growth stage. Further. one might expect that the temperatures
are correlated from one stage to another.
2. How many parameters were estimated in the type IV model? It appears that there were
34. Are that many necessary? tt-‘hcre does the model come from?
ilfrmcrkara: l. The difference in R1 values among stages is considered to show the difference
in correlation between independent variables (climatic factors) and dependent variables (yield