Resource capture and use in semi-arid overstorey agroforestry systems. PhD thesis, University of ...

272 Pages·2016·30.05 MB·English

RESOURCE CAPTURE AND USE IN SEMI-ARID OVERSTOREY
AGROFORESTRYSYSTEMS
JAMES E. LOTI
B.Sc. (Hons)
Thesis submitted to the University of Nottingham for the degree of Doctor of
Philosophy, April 1998.
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ABSTRACT
The work reported here aimed to provide a comprehensive database of core infonnation
to support the development and validation of process-based models of resource capture
and growth in semi-arid overstorey agroforestry systems. Intensive field studies were
carried out in Kenya over a 30 month period and the results obtained were combined
with data from a previous project to produce a dataset spanning a 4.5 year period. This
dataset was then used to verify output from the HyPA R model.
Allometric procedures developed from the pipe model theory (Lott et al., 1998) were
used to estimate tree growth non-destructively throughout the observation period.
Significant differences in tree size between the sole (Td) and dispersed agroforestry
(CTd) treatments were established during the first 130 days after planting, probably
because of competition with the associated crops. The above-ground biomass and trunk
length and taper characteristics of the CTd trees remained inferior to those of Td trees
throughout the observation period, seriously undermining the economic potential of this
agroforestry system. The biomass and grain yield of CTd understorey crops were
similar to the corresponding sole crops during the first three seasons, but were negligible
in three of the final four seasons, with maize yields reaching 50 % of the equivalent sole
crop values only when seasonal rainfall was well above average. This observation
suggests that water availability was the primary limitation for CTd maize during the
final seasons of the trial, a conclusion supported by the superior perfonnance of maize
grown under net enclosures which simulated tree shade in the absence of below-ground
competition. Cowpea and maize were grown concurrently in two seasons to examine
the impact of grevillea on C4 and C3 crops with contrasting responses to shade; biomass
and grain yield were less affected in cowpea than in maize.
The tree canopy in the dispersed agroforestry (CTd) treatment reduced the daily mean
quantity of radiation incident upon the understorey crops by c. 30 % during the final
four growing seasons, although the discontinuous nature of the tree canopy caused
substantial local variation in shading intensity. Seasonal mean fractional interception
was greater for the combined canopies of the CTd treatment when soil moisture status
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was relatively high than for either of the sole canopies, suggesting the occurrence of
spatial complementarity.
Tree shade had a substantial moderating influence on meristem temperature since the
mean diurnal temperature range was reduced from a maximum of 20°C in sole maize to
13 °C under the trees, and maximum meristem temperature was decreased by up to 6 °C
relative to sole maize. However, the non-uniform shading provided by the trees caused
substantial spatial variation in thermal time accumulation and hence crop development.
Grevillea continued to grow during dry seasons and was therefore able to capture off
season rainfall which might othelWise have been lost from productive use. In addition,
adaptation of heat balance gauges for use on grevillea roots (Lott et al., 1996) showed
that substantial quantities of water could be extracted from deep-seated reserves below
the crop rooting zone during dry periods, indicating the potential for spatial and
temporal complementarity. However, transpiration by grevillea greatly exceeded
rainfall during the dry season, rapidly depleting residual water supplies which might
otheIWise have been available for crop growth. In addition, approximately two thirds of
the water used by the trees during cropping seasons was extracted from the soil surface
horizons by lateral roots at distances of up to 2 m from the trunk. Thus, the potential for
above and below-ground complementarity may be seriously undermined by the
extensive capture of water by tree roots from the crop rooting zone.
Comparison of output from the HyP AR model against the observed results provided
infonnation pertinent to future model development. The model proved to be
insufficiently flexible for end-users wishing to simulate the growth of different crops
during the same simulation cycle, or to use model output to aid management decisions
such as the timing of pruning. The allometric procedures used by the model to estimate
canopy size from trunk diameter at breast height also proved incapable of accounting for
reductions in canopy size resulting from pruning. Estimates of tree height are rounded
to the nearest metre within the model, representing a potentially serious loss of
resolution when annual increments often do not exceed 2 m. In addition, the numerous
parameters required by the model would force most end-users to rely heavily on
published information, potentially undermining the reliability of simulations.
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ACKNO~EDGEMENTS
Firstly I wish to express my deepest thanks to Dr. Colin Black. The faith you have
continually showed in me has been a constant source of encouragement. I couldn't
have hoped for such a good working relationship with my supervisor, nor for such a
great friendship. And to Professor Chin Ong who was a tremendous source of
inspiration when solving problems in the field and in honing down what is a vast
subject area to those areas of most importance. The scientific excellence that
accompanies the work of both my supervisors was a privilege to be associated with
and is an example that I have tried my best to emulate.
My thanks are due to the UK Department for International Development for their
financial support throughout this project and to The Royal Society and Nottingham
University Research fund for additional support. I also wish to pass on tremendous
gratitude to my hosts in Kenya, The International Centre for Research in
Agroforestry. Their co-operation and support during the two and half years I spent
with them, on both a professional and personal level, could not have been any better.
In particular I wish to thank Ahmed Khan, Paul Smithson, Ric Coe, Meka Rao, and
Peter Muraya. Thanks are also due to Rob Parkinson of the University of Plymouth
for his patience during the completion of this thesis.
This thesis records just a small part of the tremendous effort that has been put into the
eIRUS programme involving a great many individuals and with whom I had the
privilege to be associated. In particular I wish to thank Nick Jackson, Mark Smith
and Steve Howard for many constructive discussions and for their company during
the precarious journey to work. And thanks are due to the field technicians Raphael
Maweu, Anthony Muasa, Zipora, Nzioka, Muthoca, Boniface, Peter, and Gonzi who
gave such dedicated service to the collection of data and from whom I learnt so much.
Technical support not only played a major part during data collection but also during
the testing of the HyP AR model and the preparation of DFID reports and other
publications. Thanks are therefore due to Gerry Lawson, Denna Mobbs, Neil Crout,
Jon Absalom, Jackie Johnson, Jackie Humphreys and Bob Kear.
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lowe a great deal of gratitude to my parents and family who have stood by me
through every twist and turn of my long and chequered education. And also to my
friends in Kenya and elsewhere that brought my burdensome problems quickly back
in to perspective; Maddles, Dale Carol and Rachael, Himon and Sarry, The Coe
Family, Andre, Yen, Pete and Nicky, Skelly, Kandy, Matt and Shiv, and Kev to name
just a few.
And finally, there would not be a PhD thesis had it not been for the love and support
of Jo, and without whom the world would be a far less rewarding place. I apologise
once again for all those dull weekends and evenings you had to endure.
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ABSTRACT ................................................................................................................. U
ACKN'OWLEDGEMENTS ....................................................................................... Iv
TABLE OF CONTENTS .......................................................................................... vi
1. IN'TRODUCTION ................................................................................................... 2
1.1 Natural and managed systems in semi-arid environments; the role of
agroforestry .................................................................................................... 2
1.1.1 The semi-arid environment. ................................................................... 2
1.1.2 The developmental context.. .................................................................. 4
1.1.3 Potential benefits of agroforestry in semi-arid environments ............... 6
1.1.4 Potential disadvantages ofagroforestry in semi-arid environments .... 10
1.2 Component interactions and plant physiological responses in dryland
agroforestry .................................................................................................. 11
1.2.1 Temperature ........................................................................................... 14
1.2.2 Solar radiation ....................................................................................... 16
1.2.3 Water ..................................................................................................... 18
1.3 Systems modelling ........................................................................................ 20
1.4 Species choice ............................................................................................... 21
1.4.1 Grevillea robusta .................................................................................. 22
1.4.2 Maize ................................................................................................... 26
1.4.3 Cowpea ................................................................................................ 27
I.S Background to the project ............................................................................ 28
1.6 Aims and objectives ..................................................................................... 29
2. MATERI.ALS AND METHODS .......................................................................... 34
2.1 I..ocation and climate ................................................................................... 34
2.2 Experimental design .................................................................................... 3S
2.3 Soil characteristics ....................................................................................... 43
2.4 Growth and development ............................................................................ 46
2.4.1 Growth analysis -greviUea .................................................................. 48
2.4.1.1 Pipe model theory and tree allometries ........................................... 48
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2.4.1.2 Development of a simplified allometric method for determining total
tree biomass and leaf area for grevillea ........................................... 50
2.4.1.3 Sampling regime for measuring tree growth ................................... 54
2.4.2 Crop growth analysis ........................................................................... 56
2.4.2.1 Maize: L94 to S95/96 growing seasons ........................................... 56
2.4.2.2 Cowpea: 199111992 and 199311994 short growing seasons ........... 63
2.5 Sap flow measurements on trees and crops ................................................. 65
2.5.1 Constant temperature heat balance sap flow technique ....................... 65
2.5.2 Adaptation and calibration of the heat balance technique for
grevillea. .............................................................................................. 68
2.5.3 Adaptation and calibration of the heat balance technique for maize ... 69
2.5.4 Calibration of the heat balance technique for roots ............................. 70
2.6 Photosynthesis and transpiration by crops .................................................. 75
2.6.1 PP Systems CIRAS I: operation and use in field conditions .............. 75
2.6.2 Routine measurements of crops ........................................................... 76
2.6.3 Light response curves .......................................................................... 77
2.7 Effects of natural and artificial shade on the crop micro-environment ....... 77
2.7.1 Radiation .............................................................................................. 77
2.7.2 Meristem and soil temperatures ........................................................... 79
2.7.3 Weather and additional microclimate measurements .......................... 81
2.7.4 Soil moisture ........................................................................................ 82
2.7 Statistical analysis, and database manipulation and management ............... 83
3. GROWTH, BIOMASS ACCUMULATION AND LEAF AREA INDEX ••••••• 92
3.1 Grevillea ...................................................................................................... 92
3.2 Crop growth in CIRUS ................................................................................ 97
3.3 Growth response of crops with differing photosynthetic pathways .......... 109
3.4 Complementarity of resource use and system productivity ...................... 114
4. SHADING ............................................................................................................ 119
4.1 Impact of tree canopy and artificial shade on the crop microclimate ........ 119
4.1.1 Temperature ....................................................................................... 119
4.1.2 Solar radiation ................................................................................... 125
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4.2 Response of maize to shading by the grevillea canopy and artificial
shade nets ................................................................................................... 133
4.2.1 Temperature effects and thermal time ............................................... 133
4.2.2 Photosynthesis, transpiration and radiation conversion coefficients. 134
5. WATER USE .................................................................................•.•..•••••••••••••.••• 143
5.1 Grevillea - diurnal trends, daily totals and plot level estimates ................. 143
5.2 Maize -diurnal trends, daily totals and scaling to obtain plot level
estimates .................................................................................................... 161
5.3 Water use ratio in maize ............................................................................ 167
5.4 Complementarity of water use ................................................................... 169
5.4.1 Contribution of lateral roots to total water uptake ............................. 169
5.4.2 Compensation in water uptake .......................................................... 173
6. COMPARISON OF OUTPUT FROM HYPAR WITH OBSERVED MAIZE
YIELDS IN CIRUS ...•......•...............................................................•••••..••.••••••••••••• 177
6.1 The HyP AR model .................................................................................... 177
6.2 Input files ................................................................................................... 178
6.2.1 Crop and cultivar-dependent parameters ........................................... 178
6.2.2 Tree parameters ................................................................................. 181
6.2.3 Soil and soil water characteristics ..................................................... 184
6.2.3 Meteorological data ........................................................................... 185
6.3 Initial validation ......................................................................................... 187
6.4 Sensitivity analysis .................................................................................... 188
6.4.1 Soil hydrology and water relations .................................................... 188
6.4.2 Cultivar specific parameters .............................................................. 194
6.5 Second validation ...................................................................................... 209
6.6 Conclusions and recommendations ........................................................... 210
6.6.1 Inconsistency of parameter sensitivity .............................................. 210
6.6.2 Tree/crop interactions during the annual cycle .................................. 211
6.6.3 Allometric estimation of canopy size ................................................ 211
6.6.4 Accumulation of thermal time ........................................................... 212
6.6.5 Model output ..................................................................................... 212
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