Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly.
Sorghum (
Few long-term field experiments exist with sufficient detail in space and time to enable an understanding of variability in sorghum production due to dynamics in soil, nutrient, varieties, management, and weather processes and their interactions. Previous short-term field experiments at different locations and seasons, both on-farm and on-station, obtained higher grain yields, for instance, [
Moreover, over the past years concerns have grown on increased rainfall variability across seasons resulting in large yield variability and thus becoming an apparent determinant on the performance and adaptation of sorghum varieties [
The Agricultural Production System sIMulator (APSIM) [
The central zone comprising Dodoma and Singida regions is located between latitudes 6° and 06°08 S and longitudes 34°30′ and 35°45′ E. The experimental site was located at Hombolo Agricultural Research Institute (ARI) in Dodoma Region about 58 km North-East of Dodoma Municipality at latitude 05°45′ S and longitude 35°57′ E. The mean annual rainfall is 589 mm but the distribution is highly variable. The average annual temperature is 22.7°C. Soils at the experimental site are mainly sandy and loamy of low fertility. They are classified as Ferralic Cambisols in the FAO classification [
Field experiments were conducted during 2012/13 and 2013/14 seasons. Three sorghum varieties, namely, Tegemeo, Macia, and Pato (the most widely grown varieties in the central zone), were used as treatments in a randomized complete block design (RCBD) with three replications. The recommended agronomic practices such as plant spacing and weeding are similar for the three varieties. Sowing was conditioned upon the previous day having received significant rainfall so as to wet the soil. Sorghum was sown at a spacing of 0.75 m between rows and 0.30 m within the row resulting in a plant density of 12 plants m−2. Weeding was done manually three times during the season on each plot using a hand hoe.
In order to provide near-optimum conditions, diammonium phosphate (DAP) fertilizer was applied during planting to supply 25 kg P/ha and 40 kg N/ha. Another round of N fertilization was done by applying 40 kg N/ha as Urea seven weeks after planting. The phenological data collected for the three sorghum varieties included date of flowering and date of physiological maturity. These were noted when 50% of plant population in each plot had attained that respective stage. Grain maturity was regarded to have been reached when dark spots at the point of attachment of the grain to the panicle started to show which was towards the end of April for both seasons. At final harvest, total aboveground biomass and grain yield were determined.
Daily weather data during both seasons were obtained from observations at an agromet station, located about 500 m from the experimental plots. Past climate data (1961–2010) for selected weather stations, except Hombolo (1974–2010) in the central zone Tanzania, were analysed for trends. INSTAT plus (v3.36) software [
The Mann-Kendall test was used to test for significance of time series trends in total annual rainfall, seasonal rainfall, onset date, cessation date, and LGP. The Mann-Kendall test is less sensitive to outliers and has the capability to detect both linear and nonlinear trends and has been used in related studies in sub-Saharan Africa [
The Mann-Kendall test statistic is given as
The presence of a statistically significant trend is evaluated using the
The theory and parameterization of the APSIM model used in this study have been described in Ncube et al. [
Soil water dynamics between soil layers were defined by the cascading water balance method [
Soil physical and chemical properties used for the calibration of APSIM.
Soil parameters | Layers | |||||
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BD (g cm−3) | 1.38 | 1.47 | 1.44 | 1.38 | 1.51 | 1.51 |
SAT (cm cm−1) | 0.37 | 0.35 | 0.34 | 0.33 | 0.33 | 0.33 |
LL (cm cm−1) | 0.084 | 0.084 | 0.134 | 0.134 | 0.134 | 0.14 |
DUL (cm cm−1) | 0.248 | 0.299 | 0.334 | 0.278 | 0.270 | 0.270 |
Clay (%) | 19 | 20 | 23 | 25 | 34 | 30 |
Silt (%) | 5 | 4 | 4 | 5 | 2 | 4 |
CEC (cmol/kg) | 6.0 | 8.2 | 9.2 | 10.2 | 10.0 | 6.0 |
Soil C parameters | ||||||
Organic C (g 100 g−1) | 0.41 | 0.31 | 0.23 | 0.14 | 0.14 | 0.06 |
Finert |
0.4 | 0.6 | 0.8 | 0.8 | 0.9 | 0.9 |
Fbiom |
0.025 | 0.02 | 0.015 | 0.01 | 0.01 | 0.01 |
BD: bulk density; SAT: volumetric water content at saturation. LL is wilting point (volumetric water content at −15 bar pressure potential) and DUL is drained upper limit.
Soil properties of the profiles used in simulations across stations.
Properties | Dodoma | Hombolo | Mpwapwa | Manyoni | Singida |
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Soil layers/depth (cm) | 6/135 | 6/135 | 4/110 | 4/115 | 4/110 |
Sand, silt, clay (% in 0–15 cm) | 79, 5, 16 | 79, 5, 16 | 81, 6, 13 | 66, 10, 14 | 55, 21, 24 |
Textural class | Sandy loam | Sandy loam | Sandy loam | Sandy loam | Sandy clay loam |
Plant available water | 119.2 | 119.2 | 112.8 | 164.1 | 162.1 |
Organic carbon (top three layers) | 0.32, 0.21, |
0.32, 0.21, |
0.45, 0.30, |
0.56, 0.32, |
0.52, 0.38, |
Source: AfSIS.
Each APSIM module demands a number of parameters. For the SOILWAT module, which simulates the dynamics of soil water, the inputs included soil bulk density, LL15 and DUL, and two parameters,
The calibrated model was evaluated by comparing observed values for grain yield and total aboveground biomass with those from model simulations. Model performance was assessed through root mean square error (RMSE) [
Future climate data were obtained from Coupled Model Intercomparison Project phase 5 (CMIP5) under three Global Circulation Models (GCMs), namely, GFDL-ESM2M, HadGEM2-ES, and MIROC5 for mid-century RCP8.5 using the method by Hempel et al. [
Analysis of variance (ANOVA) was used to analyse yield and total biomass data from the different treatments, with variety and replication, used as fixed and random effects, respectively. Test of significance between the 2012/2013 and 2013/2014 experiments was done using a
The median for onset of rainfall begins on the last week of November to first week of December (Table
Statistical characteristics and trends of onset date, cessation date, and LGP at five stations over the period 1961–2010 in central Tanzania.
Station | Statistics | Dodoma | Mpwapwa | Hombolo | Manyoni | Singida |
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Onset | Median | Dec 13 | Dec 7 | Dec 7 | Dec 1 | Nov 26 |
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Slope | 0.00 |
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SD | 11.311 | 14.252 | 14.870 | 14.361 | 14.582 | |
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Cessation | Median | Apr 18 | Apr 13 | Apr 5 | Apr 14 | Apr 30 |
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Slope | 0.000 |
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0.029 |
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0.303 | |
SD | 10.252 | 16.041 | 11.054 | 14.281 | 16.711 | |
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LGP (days) | Median | 124 | 122 | 123 | 141 | 145 |
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Slope | 0.000 |
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0.434 | 0.000 | 0.692 | |
CV (%) | 12.510 | 13.711 | 14.281 | 13.511 | 15.982 |
Median LGP in the central Tanzania varied from 122 to 145 days depending on the location of the station (Table
Selected GCMs consistently projected increased temperatures for selected weather stations in the central zone of Tanzania. Projected temperature changes showed a mean increase in the range of 1.4–2.8°C (Table
Mean change in projected climate between baseline (1980–2010) and mid-century (2040–2069) RCP8.5.
Station | GCM | Temperature (°C) | Rainfall (%) | ||
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Average | Minimum | Maximum | |||
Dodoma | GFDL-ESM2M | 1.4 | 1.7 | 1.2 |
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HADGEM2-ES | 2.8 | 2.9 | 2.8 |
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MIROC5 | 2.2 | 2.1 | 2.4 |
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Manyoni | GFDL-ESM2M | 1.8 | 1.8 | 1.7 |
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HADGEM2-ES | 2.7 | 2.6 | 2.8 |
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MIROC5 | 2.3 | 2.1 | 2.4 |
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Singida | GFDL-ESM2M | 1.8 | 1.8 | 1.7 |
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HADGEM2-ES | 2.7 | 2.6 | 2.8 |
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MIROC5 | 2.3 | 2.1 | 2.4 |
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Grain yield, aboveground biomass, and harvest index for seasons 2012/13 and 2013/14.
Variety | 2012/13 | 2013/14 | Combined seasons | ||||
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Grain yield |
Aboveground biomass |
Grain yield |
Aboveground biomass |
Days to 50% flowering | Days to |
Plant height (max) | |
Macia |
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10517 | 4355 | 11388 | 65 | 102 | 1290 |
Pato | 3896 | 11411 | 4088 | 12394 | 76 | 118 | 1780 |
Tegemeo | 3798 | 10843 | 4012 | 11415 | 74 | 114 | 1650 |
S.E.D | 233.9 | 274.3 | 79.1 | 100.7 | 0.577 | 0.471 | 147.1 |
Table
There was no significant variation among varieties in the 2012/2013 season with respect to biomass at 50% anthesis, biomass at harvest maturity, and grain yield (Table
Intra- and interseasonal variation in biomass, grain yield, and tops weight.
Variable | 2012/2013 | 2013/2014 |
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Biomass at 50% anthesis |
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Grain yield at harvest |
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Biomass at harvest maturity |
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Genetic coefficients used by APSIM for sorghum after calibration are shown in Table
Crop parameters for three sorghum cultivars used for the simulations in APSIM.
Parameter | Source | Units | Macia | Tegemeo | Pato | |
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Thermal time accumulation | End of juvenile phase to panicle initiation | C |
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230 | 270 | 275 |
Flag stage to flowering | C |
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195 | 170 | 175 | |
Flowering to start of grain filling | C |
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80 | 80 | 100 | |
Flowering to maturity | C |
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675 | 760 | 760 | |
Maturity to seed ripening | L |
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1 | 1 | 1 | |
Photoperiod | Day length photoperiod to inhibit flowering | D | H | 11.5 | 11.5 | 11.5 |
Day length photoperiod for insensitivity | D | H | 13.5 | 13.5 | 13.5 | |
Photoperiod slope | L |
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0.01 | 0.01 | 0.01 | |
Base temperature | L |
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8 | 8 | 8 | |
Optimum temperature | D |
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30 | 30 | 30 | |
Plant height (max) | O | mm | 1290 | 1650 | 1780 |
C: calibrated; D: default; L: literature; O: observed.
Comparison between observed and simulated grain and biomass yield combined for the two seasons is shown in Table
Statistical indicators of model performance.
Parameters/cultivar | Macia | Tegemeo | Pato | |||
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RMSE (kg/ha) |
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RMSE (kg/ha) |
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RMSE (kg/ha) |
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Grain yield | 133 | 0.73 | 87 | 0.62 | 140 | 0.60 |
Biomass | 178 | 0. 93 | 418 | 0.66 | 236 | 0.83 |
Simulated grain yields for the three varieties at the experimental station are shown in Figure
Simulated grain yield of sorghum varieties under baseline (1980–2010) conditions at Hombolo.
Further examination of rainfall and yields in 1998 (the year producing the lowest simulated yields) and 2008 (the year producing the highest simulated yields) demonstrates the importance of rainfall distribution during the growing period and especially during critical stages. There was approximately 0.50 t ha−1 maize yield in 1998 compared to 2.80 t ha−1 in 2008 (Figure
Occurrences of dry spells during March and April and their relationship to simulated grain yields at Hombolo.
Years with the lowest yields | Years with the highest yields | |||||||||||||||||||||||
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1998 | 2001 | 1999 | 2008 | |||||||||||||||||||||
MAR | APR | MAR | APR | MAR | APR | MAR | APR | |||||||||||||||||
DEKAD | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 |
5-day |
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10-day |
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Yield (t/ha) | 0.48–0.57 | 0.54–0.58 | 2.65–2.88 | 2.82–2.84 | ||||||||||||||||||||
Rain (mm) | 38.8 | 98.9 | 66.4 | 63.2 | 211.6 | 117 | 182.1 | 42.3 | ||||||||||||||||
Rainy days | 6 | 8 | 6 | 8 | 12 | 5 | 10 | 11 |
√ indicates occurrence of a dry spell in a decade (10-day interval) within a month.
Mean simulated sorghum yields obtained from different locations (weather stations) across the central zone of Tanzania are shown in Figure
Simulated grain yields of Pato, Macia, and Tegemeo sorghum varieties under baseline and future climatic conditions in central Tanzania.
The field experimental results for the two seasons show considerable variations in grain yields among varieties. An early maturing variety Macia gave higher yields in both seasons compared to vars. Pato and Tegemeo. Model simulated yields reveal that the length and timing of dry spells during the growing season are major determinants of grain yields and they surpass total seasonal rainfall amount even for a hardy crop like sorghum. Results suggest that occurrence of a long dry spell (10-day or longer) during the period from flag leaf appearance to start of grain filling is critical and could significantly reduce yield. Therefore, considering the inability of smallholder farmers to construct and maintain rain-water harvesting (RWH) structures, the government and development partners should consider increasing investments in the same to ensure supplemental irrigation during critical stages. The availability of water would enable smallholders growing sorghum to leverage the uncertainty in climate, but also to tap the opportunities brought in by increased temperatures. The phenological characterization of the three varieties and subsequent calibration and validation of APSIM have provided a basis on which various kinds of simulations could be done with the aim of increasing and sustaining sorghum productivity.
This paper is based on the first chapter of a Ph.D. dissertation submitted to Sokoine University of Agriculture (SUA). Any errors in the article are the authors’ responsibility.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors thank the project “Enhancing Climate Change Adaptation in Agriculture and Water Resources in the Greater Horn of Africa (ECAW)” under the Soil Water Management Research Group (SWMRG) of Sokoine University of Agriculture for partially supporting this research.