Dry spell mitigation to upgrade semi-arid rainfed agriculture : Water harvesting and soil nutrient management for smallholder maize cultivation in Machakos, Kenya

Improvements in on-farm water and soil fertility management through water harvesting may prove key to up-grade smallholder farming systems in dry sub-humid and semi-arid sub-Sahara Africa (SSA). The currently experienced yield levels are usually less than 1 t ha-1, i.e., 3-5 times lower than potential levels obtained by commercial farmers and researchers for similar agro-hydrological conditions. The low yield levels are ascribed to the poor crop water availability due to variable rainfall, losses in on-farm water balance and inherently low soil nutrient levels. To meet an increased food demand with less use of water and land in the region, requires farming systems that provide more yields per water unit and/or land area in the future. This thesis presents the results of a project on water harvesting system aiming to upgrade currently practised water management for maize (Zea mays, L.) in semi-arid SSA. The objectives were to a) quantify dry spell occurrence and potential impact in currently practised small-holder grain production systems, b) test agro-hydrological viability and compare maize yields in an on-farm experiment using combinations supplemental irrigation (SI) and fertilizers for maize, and c) estimate long-term changes in water balance and grain yields of a system with SI compared to farmers currently practised in-situ water harvesting. Water balance changes and crop growth were simulated in a 20-year perspective with models MAIZE1&2. Dry spell analyses showed that potentially yield-limiting dry spells occur at least 75% of seasons for 2 locations in semi-arid East Africa during a 20-year period. Dry spell occurrence was more frequent for crop cultivated on soil with low water-holding capacity than on high water-holding capacity. The analysis indicated large on-farm water losses as deep percolation and run-off during seasons despite seasonal crop water deficits. An on-farm experiment was set up during 1998-2001 in Machakos district, semi-arid Kenya. Surface run-off was collected and stored in a 300m3 earth dam. Gravity-fed supplemental irrigation was carried out to a maize field downstream of the dam. Combinations of no irrigation (NI), SI and 3 levels of N fertilizers (0, 30, 80 kg N ha-1) were applied. Over 5 seasons with rainfall ranging from 200 to 550 mm, the crop with SI and low nitrogen fertilizer gave 40% higher yields (**) than the farmers’ conventional in-situ water harvesting system. Adding only SI or only low nitrogen did not result in significantly different yields. Accounting for actual ability of a storage system and SI to mitigate dry spells, it was estimated that a farmer would make economic returns (after deduction of household consumption) between year 2-7 after investment in dam construction depending on dam sealant and labour cost used. Simulating maize growth and site water balance in a system of maize with SI increased annual grain yield with 35 % as a result of timely applications of SI. Field water balance changes in actual evapotranspiration (ETa) and deep percolation were insignificant with SI, although the absolute amount of ETa increased with 30 mm y-1 for crop with SI compared to NI. The dam water balance showed 30% productive outtake as SI of harvested water. Large losses due to seepage and spill-flow occurred from the dam. Water productivity (WP, of ETa) for maize with SI was on average 1 796 m3 per ton grain, and for maize without SI 2 254 m3 per ton grain, i.e, a decerase of WP with 25%. The water harvesting system for supplemental irrigation of maize was shown to be both biophysically and economically viable. However, adoption by farmers will depend on other factors, including investment capacity, know-how and legislative possibilities. Viability of increased water harvesting implementation in a catchment scale needs to be assessed so that other down-stream uses of water remains uncompromised.

Dynamical aspects of atmospheric data assimilation in the tropics

A faithful depiction of the tropical atmosphere requires three-dimensional sets of observations. Despite the increasing amount of observations presently available, these will hardly ever encompass the entire atmosphere and, in addition, observations have errors. Additional (background) information will always be required to complete the picture. Valuable added information comes from the physical laws governing the flow, usually mediated via a numerical weather prediction (NWP) model. These models are, however, never going to be error-free, why a reliable estimate of their errors poses a real challenge since the whole truth will never be within our grasp. The present thesis addresses the question of improving the analysis procedures for NWP in the tropics. Improvements are sought by addressing the following issues: - the efficiency of the internal model adjustment, - the potential of the reliable background-error information, as compared to observations, - the impact of a new, space-borne line-of-sight wind measurements, and - the usefulness of multivariate relationships for data assimilation in the tropics. Most NWP assimilation schemes are effectively univariate near the equator. In this thesis, a multivariate formulation of the variational data assimilation in the tropics has been developed. The proposed background-error model supports the mass-wind coupling based on convectively-coupled equatorial waves. The resulting assimilation model produces balanced analysis increments and hereby increases the efficiency of all types of observations. Idealized adjustment and multivariate analysis experiments highlight the importance of direct wind measurements in the tropics. In particular, the presented results confirm the superiority of wind observations compared to mass data, in spite of the exact multivariate relationships available from the background information. The internal model adjustment is also more efficient for wind observations than for mass data. In accordance with these findings, new satellite wind observations are expected to contribute towards the improvement of NWP and climate modeling in the tropics. Although incomplete, the new wind-field information has the potential to reduce uncertainties in the tropical dynamical fields, if used together with the existing satellite mass-field measurements. The results obtained by applying the new background-error representation to the tropical short-range forecast errors of a state-of-art NWP model suggest that achieving useful tropical multivariate relationships may be feasible within an operational NWP environment.