performance of sorghum varieties under variable rainfall...

Research ArticlePerformance of Sorghum Varieties under Variable Rainfall inCentral Tanzania

Barnabas M Msongaleli1 S D Tumbo2 N I Kihupi2 and Filbert B Rwehumbiza3

1Department of Geography and Environmental Studies University of Dodoma PO Box 395 Dodoma Tanzania2Department of Engineering Sciences and Technology Sokoine University of Agriculture PO Box 3003 Morogoro Tanzania3Department of Soil and Geological Sciences Sokoine University of Agriculture PO Box 3008 Morogoro Tanzania

Correspondence should be addressed to Barnabas M Msongaleli bmsongaleliyahoocouk

Received 30 January 2017 Revised 17 March 2017 Accepted 4 April 2017 Published 27 April 2017

Academic Editor Jerry Hatfield

Copyright copy 2017 Barnabas M Msongaleli et alThis is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in anymedium provided the originalwork is properly cited

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas Thisstudy used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghumvarieties based on long-term historical rainfall and projected climate Analyses of historical rainfall indicate a mix of nonsignificantand significant trends on the onset cessation and length of the growing seasonThe study confirmed that rainfall variability indeedaffects yields of improved sorghum varieties Further analyses of simulated sorghum yields based on seasonal rainfall distributionindicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season Simulation results for futuresorghum response however show that impacts of rainfall variability on sorghum will be overridden by temperature increase Weconclude that in the event where harms imposed by moisture stress in the study area are not abated even improved sorghumvarieties are likely to perform poorly

1 Introduction

Sorghum (Sorghum bicolor L Moench) is an important andwidely adapted small-grain cereal grown in the tropics andsubtropics and a staple food grain in food-insecure regions ofAsia Africa and Central America [1] Sorghum ranks secondin importance after maize in Africa with a mean yield of08 tha froma cultivated area of about 24million hectares [2]According to a review by Keya and Rubaihayo [3] sorghumranks fifth after maize cassava rice and wheat as staple inTanzania Nonetheless sorghum plays a significant role infighting hunger and food insecurity in central Tanzania

Few long-term field experiments exist with sufficientdetail in space and time to enable an understanding ofvariability in sorghum production due to dynamics in soilnutrient varieties management and weather processes andtheir interactions Previous short-term field experiments atdifferent locations and seasons both on-farm and on-stationobtained higher grain yields for instance [4] (265 t haminus1)and [5] (258 t haminus1) for var Tegemeo contrary to the resultsby Saadan et al [6] which showed that vars Pato and

Macia were superior to var Tegemeo Although short-termfield experiments provide data with high degree of accuracy[7] they suffer from the failure to capture the interannualvariability due to environmental conditions Results from theprevious experiments on improved sorghum varieties showthat grain yields vary among varieties and across locationsand seasonsThus being short lived the ensuing experimentsdo not permit derivation of robust conclusions about yieldperformance and adaptation of sorghum varieties over along-term period

Moreover over the past years concerns have grownon increased rainfall variability across seasons resultingin large yield variability and thus becoming an apparentdeterminant on the performance and adaptation of sorghumvarieties [8 9] Thus studies are essential which wouldcombine long-term period and multiple locations (spatial-temporal analysis) under variable rainfall and soils to elu-cidate sorghum varietiesrsquo performance Moreover alongsidesuch studies analyses of rainfall trends are deemed nec-essary to understand the vulnerability of semiarid regionsto historical and projected future conditions Some rainfall

HindawiInternational Scholarly Research NoticesVolume 2017 Article ID 2506946 10 pageshttpsdoiorg10115520172506946

2 International Scholarly Research Notices

analyses have shown decreasing trends (eg [10]) associatedwith decreases in the number of rainy days while othershave revealed neither abrupt changes nor trends [11] Thesecontrasting results suggest the need for undertaking locationspecific analyses of rainfall trends to ascertain contentiousassertions on the same A combination of field experimentsand computer simulation models could be an appropriateoption to comprehend the biophysical (climatic and soilconditions) factors and their interactions affecting crop yieldand productivity [12 13]

The Agricultural Production System sIMulator (APSIM)[14] is able to simulate growth and yield under differentmanagement practices and has been used by several studiesunder semiarid environments (eg [15 16]) This studytherefore used the APSIM model to simulate sorghumgrowth and yield patterns over the current (baseline) climateunder existing soil conditions and local management prac-tices across selected locations in semiarid central TanzaniaSpecifically the study used APSIM crop simulation modelto investigate how rainfall variability may affect yields ofimproved sorghum varieties based on long-term historicalrainfall and projected climate

2 Materials and Methods

21 Study Area The central zone comprising Dodoma andSingida regions is located between latitudes 6∘ and 06∘08 Sand longitudes 34∘301015840 and 35∘451015840 EThe experimental site waslocated at Hombolo Agricultural Research Institute (ARI)in Dodoma Region about 58 km North-East of DodomaMunicipality at latitude 05∘451015840 S and longitude 35∘571015840 E Themean annual rainfall is 589mm but the distribution is highlyvariable The average annual temperature is 227∘C Soils atthe experimental site are mainly sandy and loamy of lowfertility They are classified as Ferralic Cambisols in the FAOclassification [17]

22 Experimental Design and Data Collection Field experi-ments were conducted during 201213 and 201314 seasonsThree sorghum varieties namely Tegemeo Macia and Pato(the most widely grown varieties in the central zone) wereused as treatments in a randomized complete block design(RCBD) with three replications The recommended agro-nomic practices such as plant spacing andweeding are similarfor the three varieties Sowing was conditioned upon theprevious day having received significant rainfall so as to wetthe soil Sorghum was sown at a spacing of 075m betweenrows and 030m within the row resulting in a plant densityof 12 plants mminus2 Weeding was done manually three timesduring the season on each plot using a hand hoe

In order to provide near-optimum conditions diammo-nium phosphate (DAP) fertilizer was applied during plantingto supply 25 kg Pha and 40 kgNha Another round of Nfertilization was done by applying 40 kgNha as Urea sevenweeks after planting The phenological data collected for thethree sorghum varieties included date of flowering and dateof physiological maturity These were noted when 50 ofplant population in each plot had attained that respective

stage Grain maturity was regarded to have been reachedwhen dark spots at the point of attachment of the grain to thepanicle started to show which was towards the end of Aprilfor both seasons At final harvest total aboveground biomassand grain yield were determined

23 Historical Climatic Trends Daily weather data duringboth seasons were obtained from observations at an agrometstation located about 500m from the experimental plots Pastclimate data (1961ndash2010) for selected weather stations exceptHombolo (1974ndash2010) in the central zone Tanzania wereanalysed for trends INSTAT plus (v336) software [18] wasused to summarize the daily data into annual monthly andseasonal totals and to determine the onset taken as the firstoccasion after the earliest possible date on which a runningtotal of at least 20mmof rain was reached in four consecutivedays with at least two days being wet and that no dry spell of10 days or more occurred in the next 30 days [19] Cessationof the rainy season was obtained through a water balancemethod and verified by visual daily display in INSTAT andlength of growing period (LGP) was taken as the durationbetween the onset and cessation dates

The Mann-Kendall test was used to test for significanceof time series trends in total annual rainfall seasonal rainfallonset date cessation date and LGP The Mann-Kendall testis less sensitive to outliers and has the capability to detectboth linear and nonlinear trends and has been used in relatedstudies in sub-Saharan Africa [20 21] The median measurewas used to show onset and cessation dates and days of LGPas it is relatively unaffected by extreme values compared tothe mean

The Mann-Kendall test statistic is given as

119878 = 119873minus1sum119894=1

119873sum119895=119894+1

sgn (119909119895 minus 119909119894) (1)

where 119878 is the Mann-Kendall test statistic 119909119894 and 119909119895 arethe sequential data values of the time series in the years 119894and 119895 (119895 gt 119894) and 119873 is the length of the time series Apositive 119878 value indicates an increasing trend and a negativevalue indicates a decreasing trend in the data series The signfunction is given as

sgn (119909119895 minus 119909119894) =

+1 if (119909119895 minus 119909119894) gt 00 if (119909119895 minus 119909119894) = 0minus1 if (119909119895 minus 119909119894) lt 0

(2)

For119873 larger than 10119885MK approximates the standard normaldistribution [22] and is computed as follows

119885MK =

119878 minus 1radicVar (119878) if 119878 gt 00 if 119878 = 0119878 + 1radicVar (119878) if 119878 lt 0

(3)

International Scholarly Research Notices 3

Thepresence of a statistically significant trend is evaluatedusing the 119885MK value In a two-sided test for trend the nullhypothesis1198670 should be accepted if |119885MK| lt 1198851minus1205722 at a givenlevel of significance 1198851minus1205722 is the critical value of 119885MK fromthe standard normal table (eg for 5 significance level thevalue of 1198851minus1205722 is 196)24 Model Description Calibration and Evaluation Thetheory and parameterization of theAPSIMmodel used in thisstudy have been described in Ncube et al [23] APSIM hasbeen tested in a diverse range of systems and environments aswell as model performance in long-term cropping systems insemiarid and subhumid environments in sub-Saharan Africa[24 25] The sorghum module used in the present studysimulates the growth of a sorghum crop on a daily time step(on an area basis and not a single plant) Sorghum growth inthis module responds to climate (temperature rainfall andradiation from the met module) soil water supply (from theSoilWat module) and soil nitrogen (from the SoilNmodule)Crop development is controlled by temperature (thermaldegree days) and photoperiod Thermal time accumulationswere derived using an algorithm described in Jones andKiniry [26] using observed phenology and weather dataa base temperature of 8∘C and an optimal temperature of30∘C Genetic coefficients used by APSIM for sorghum areexpressed in thermal degrees and photoperiod The factorcontrolling the effect of photoperiod was set to a minimumvalue of 001 to eliminate the effect of photoperiod from thevarieties as ldquomodernrdquo varieties are photoperiod insensitive[27] In the present study the APSIM model was evaluatedfor simulation of days after sowing to flowering andmaturitydry matter accumulation (biological yield) and grain yield

Soil water dynamics between soil layers were defined bythe cascading water balance method [28] Its characteristicsin the model are specified by the drained upper limit (DUL)lower limit of plant extractable water (LL15) and saturatedwater content (SAT) Soil characteristics of a soil profileopened up at the experimental site including soil texture pHof soil organic carbon content and cation exchange capacityare shown in Table 1 Characteristics for the additional soilprofiles used in simulations at different locations across thestudy area were obtained from the available soil databases(Table 2)

Each APSIM module demands a number of parametersFor the SOILWAT module which simulates the dynamics ofsoil water the inputs included soil bulk density LL15 andDUL and two parameters 119880 and CONA which determinefirst- and second-stage soil evaporation LL15 and DUL andSAT were estimated according to Saxton et al [29] Theparameters119880 andCONA were set at 60mmday 1 and 3mmday 1 respectively values acceptable for tropical conditions[30] A value of 07 was used for SWCON a coefficient thatspecifies the proportion of the water in excess of field capacitythat drains to the next layer in one day [30] The bare soilrunoff curve number (cn2_bare) was set to 50 to account forthe low runoff because of the flat topography and high infil-tration rates due to the sandy soil nature of the experimentalsite ([31] cited by [32]) Parameters influencing soil fertility

aremainly represented inAPSIM-SoilN2module For the soilN model the organic carbon content for each soil layer wasmeasured at the experimental siteThe initial soil N was set at25 kgha (20 kg NO3-Nha and 5 kg NH4

+-Nha) for the toptwo layers based on published data around central Tanzania[33] and 119875 was assumed nonlimiting

The calibrated model was evaluated by comparingobserved values for grain yield and total abovegroundbiomass with those from model simulations Model perfor-mance was assessed through root mean square error (RMSE)[34]

RMSE = radic 1119873 sum(119894 minus 119884119894)2 (4)

and index of agreement or 119889-statistic [35]119889 = 1 minus [ sum119899119894=1 (119894 minus 119884119894) (119894 minus 119884119894)sum119899119894=1 (10038161003816100381610038161003816119894 minus 11988411989410038161003816100381610038161003816 + 10038161003816100381610038161003816119884119894 minus 11988411989410038161003816100381610038161003816)] (5)

where 119884 and 119884 are respectively the simulated observedandmeanof the observed values and 119899 is the number of obser-vations For good agreement between model simulations andobservations d-statistic should approach unity

25 Future Climate Data for Sorghum Yield ProjectionsFuture climate data were obtained from Coupled ModelIntercomparison Project phase 5 (CMIP5) under threeGlobal CirculationModels (GCMs) namely GFDL-ESM2MHadGEM2-ES and MIROC5 for mid-century RCP85 usingthe method by Hempel et al [36] Subsequently simulationswere performed for the three sorghum varieties under cur-rent (1980ndash2010) climate and yields compared with thoseobtained under future climatic conditions The RCP85 isa high emissions scenario corresponding to projections ofhigh human population (12 billion by 2100) high rates ofurbanization and limited rates of technological change allresulting in emissions approaching 30Gt of carbon by 2100compared with 8Gt in 2000 [37]

26 Statistical Analysis Analysis of variance (ANOVA) wasused to analyse yield and total biomass data from the differenttreatments with variety and replication used as fixed andrandom effects respectively Test of significance between the20122013 and 20132014 experiments was done using a 119905-test for pairwise comparison of means Analysis of vari-ance was performed using GENSTAT (v 14) software (VSNinternational Ltd Hempstead England) whereas paired 119905-test was performed using Microsoft Excelsrsquo add-in Analyse-it (Analyse-it Software Ltd The Tannery 91 Kirkstall RoadLeeds LS31HS United Kingdom)

3 Results and Discussion

31 Trends in Onset and Cessation Dates and Length ofGrowing Period The median for onset of rainfall beginson the last week of November to first week of December(Table 3) Standard deviation varied between 11 and 15 days


Table 1 Soil physical and chemical properties used for the calibration of APSIM

Soil parameters Layers150mma 150mma 150mma 250mma 350mma 300mma

BD (g cmminus3) 138 147 144 138 151 151SAT (cm cmminus1) 037 035 034 033 033 033LL (cm cmminus1) 0084 0084 0134 0134 0134 014DUL (cm cmminus1) 0248 0299 0334 0278 0270 0270Clay () 19 20 23 25 34 30Silt () 5 4 4 5 2 4CEC (cmolkg) 60 82 92 102 100 60Soil C parametersOrganic C (g 100 gminus1) 041 031 023 014 014 006Finertb 04 06 08 08 09 09Fbiomc 0025 002 0015 001 001 001BD bulk density SAT volumetric water content at saturation LL is wilting point (volumetric water content at minus15 bar pressure potential) and DUL is drainedupper limitaLayer thickness (mm)bProportion of soil carbon assumed not to decomposecProportion of decomposable soil carbon in the more labile soil organic matter pool

Table 2 Soil properties of the profiles used in simulations across stations

Properties Dodoma Hombolo Mpwapwa Manyoni SingidaSoil layersdepth(cm) 6135 6135 4110 4115 4110

Sand silt clay (in 0ndash15 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 clayloam

Plant availablewater 1192 1192 1128 1641 1621

Organic carbon(top three layers)

032 021011

032 021011

045 030015

056 032012

052 038020

Source AfSIS

Table 3 Statistical characteristics and trends of onset date cessation date and LGP at five stations over the period 1961ndash2010 in centralTanzania

Station Statistics Dodoma Mpwapwa Hombolo Manyoni Singida

Onset

Median Dec 13 Dec 7 Dec 7 Dec 1 Nov 26119885MK minus007ns minus0030ns minus1196lowast minus0911ns minus0680nsSlope 000 minus0091 minus0321 minus0225 minus0131SD 11311 14252 14870 14361 14582

Cessation

Median Apr 18 Apr 13 Apr 5 Apr 14 Apr 30119885MK minus0337ns minus1188ns 0970ns minus0755ns 1692lowastSlope 0000 minus0083 0029 minus0070 0303SD 10252 16041 11054 14281 16711

LGP (days)

Median 124 122 123 141 145119885MK minus0303ns minus0419ns 2092lowast minus0480ns 1876lowastSlope 0000 minus0067 0434 0000 0692CV () 12510 13711 14281 13511 15982

119885MK is Mann-Kendall trend test slope (Senrsquos slope) is the change (days)annum lowast is statistically significant at 005 probability level ns is nonsignificant trendSD is standard deviation CV is coefficient of variation


The results indicated that the onset dates in the last 50years have changed with all stations depicting early trendsHowever the trends are not statistically significant except forHombolo station According to the analysed data cessationof rainfall starts from the first week of April (at Hombolo)to last week of April (at Singida) (Table 3) Munishi [38] alsoreported similar findings in central Tanzania with slightlyearlier onset and cessation datesThemedian date for rainfallcessation was characterized by high standard deviation (gt10days) at all stations implying high variability in the pattern ofend of the rainy season However these results are contrary toother studies which have shown less variable cessation datesthan onset dates [19 39]

Median LGP in the central Tanzania varied from 122 to145 days depending on the location of the station (Table 3)All stations had higher coefficients of variation (gt13) inLGP which indicate high year to year variability of LGPexcept for Dodoma (12) Higher coefficients of variation(gt13) in LGP give less confidence in crop selection basedon maturity period From the analyses a mix of increasingand decreasing trends in LGP was obtained Singida andHombolo stations show statistically significant increasingtrends in LGP (Table 3) However Dodoma Mpwapwa andManyoni stations had nonsignificant decreasing trends inLGP resultswhich are in agreementwith findings fromearlierstudies which indicate that LGP has been shortening with adecreasing trend of number of rainy days during the growingseason [19 38 40]

32 Climate Change Projections Selected GCMs consistentlyprojected increased temperatures for selected weather sta-tions in the central zone of Tanzania Projected temperaturechanges showed a mean increase in the range of 14ndash28∘C(Table 4) In contrast the projected change in rainfall acrossthe stations showed decline except for MIROC5 whichshowed an increase of +45ndash73 (Table 5) While projectedrainfall changes were variable and uncertain the projectedtemperature changes showed strong consistency with anupward trend

33 Field Experimental Results Table 5 shows grain yieldsand aboveground biomass obtained during the two experi-mental seasons

There was no significant variation among varieties in the20122013 season with respect to biomass at 50 anthesisbiomass at harvest maturity and grain yield (Table 6) How-ever during the season of 20132014 significant variation(119875 lt 005) in the three variables was observed amongvarieties Further there was interseasonal variation in plantbiomass at 50 anthesis grain yieldand biomass at harvestas indicated by the 119905-statistic in Table 6

34 Model Calibration and Evaluation Genetic coefficientsused by APSIM for sorghum after calibration are shown inTable 7

Comparison between observed and simulated grain andbiomass yield combined for the two seasons is shown inTable 8 Statistical indicators show the simulation efficiency

of APSIM model in simulating sorghum Root mean squareerror (RMSE) which is an overall measure of model perfor-mance and compares simulated versus observed values showsa good agreement because the lower the values of RMSE thebetter the model in explaining most of the variations in thedataset Moreover data indicate that the simulated grain andbiomass yield values reasonably matched observed valuesowing to the agreement index (119889-statistic) ranging from 06to 09 across the varieties The 119889-statistic values close to 1are regarded as better simulations and according to thesestatistical indicators the model performance was deemedsatisfactory to allow continuation of simulations both forlong-term (temporal) and at different locations (spatial)

35 Influence of Water Stress on Sorghum Grain Yield Sim-ulated grain yields for the three varieties at the experimen-tal station are shown in Figure 1 The simulation packageconsisted of planting between 15 December and 15 Januarya row spacing of 090m and a population of 9 plants perm2 without N fertilizer under baseline weather (1980ndash2010)Results indicated that simulated yields varied among varietieswith the range of 265ndash288 t haminus1 for the highest yields and048ndash057 t haminus1 for the lowest yields

Further examination of rainfall and yields in 1998 (theyear producing the lowest simulated yields) and 2008 (theyear producing the highest simulated yields) demonstratesthe importance of rainfall distribution during the grow-ing period and especially during critical stages There wasapproximately 050 t haminus1 maize yield in 1998 compared to280 t haminus1 in 2008 (Figure 1) This was probably due towater stress It means that yields simulated by APSIM arehighly sensitive to wetdry spell sequences during the cropgrowing season Baigorria et al [41] observe that not only isincreasing persistence of wetdry day occurrences importantbut also the timing within the growing season is importantwhen these wetdry spells occurred Decadal analyses ofrainfall for occurrences of 5- and 10-day dry spells shown inTable 9 indicate that in 1998 the occurrence of a 10-day dryspell during the first decade in March caused strong waterstresses which significantly reduced sorghum grain yieldsOn the contrary sorghum experienced only a brief waterstress period (5-day dry spell during the same period) as aresult much higher yields were obtained in 2008 Accordingto the sowing dates in the simulation package the periodrepresents the crop growth stages from flag leaf appearanceto start of grain filling Premachandra et al [42] indicate thatas the most sensitive period for sorghum response to droughtamong phenological phases

36 Simulations under Both the Baseline and Future ClimatesMean simulated sorghum yields obtained from differentlocations (weather stations) across the central zone of Tan-zania are shown in Figure 2 Taking into account uniformfarmersrsquo management practices across the study area thesimulated sorghum yields were envisaged to be influenced bythe response to rainfall and soil variability However despitethe differences in rainfall projections shown by the GCMs


Table 4 Mean change in projected climate between baseline (1980ndash2010) and mid-century (2040ndash2069) RCP85

Station GCM Temperature (∘C) Rainfall ()Average Minimum Maximum

DodomaGFDL-ESM2M 14 17 12 minus85HADGEM2-ES 28 29 28 minus14

MIROC5 22 21 24 73Manyoni

GFDL-ESM2M 18 18 17 minus30HADGEM2-ES 27 26 28 minus52

MIROC5 23 21 24 70Singida


MIROC5 23 21 24 45Table 5 Grain yield aboveground biomass and harvest index for seasons 201213 and 201314

Variety

201213 201314 Combined seasons

Grain yield(kgha)

Abovegroundbiomass(kgha)

Grain yield(kgha)


Days to 50flowering

Days toharvestmaturity

Plant height(max)mm

Macia 4064lowast 10517 4355 11388 65 102 1290Pato 3896 11411 4088 12394 76 118 1780Tegemeo 3798 10843 4012 11415 74 114 1650SED 2339 2743 791 1007 0577 0471 1471lowastMeans over three replications SED = standard error of differences of means

Table 6 Intra- and interseasonal variation in biomass grain yield and tops weight

Variable 20122013 20132014 119905-statisticBiomass at 50 anthesis 277ns 549lowast 389lowastlowastGrain yield at harvest 141ns 2108lowast 508lowastBiomass at harvest maturity 323ns 5049lowast 860lowastlowastSignificant at 119875 lt 005 lowastlowastsignificant at 119875 lt 001 ns = not significant

Table 7 Crop parameters for three sorghum cultivars used for the simulations in APSIM

Parameter Source Units Macia Tegemeo Pato

Thermal time accumulation

End of juvenile phase to panicle initiation C ∘C day 230 270 275Flag stage to flowering C ∘C day 195 170 175

Flowering to start of grain filling C ∘C day 80 80 100Flowering to maturity C ∘C day 675 760 760

Maturity to seed ripening L ∘C day 1 1 1

Photoperiod

Day length photoperiod to inhibit flowering D H 115 115 115Day length photoperiod for insensitivity D H 135 135 135

Photoperiod slope L ∘Ch 001 001 001Base temperature L ∘C day 8 8 8

Optimum temperature D ∘C day 30 30 30Plant height (max) O mm 1290 1650 1780

C calibrated D default L literature O observed

Table 8 Statistical indicators of model performance

Parameterscultivar Macia Tegemeo PatoRMSE (kgha) 119889-Stat RMSE (kgha) 119889-Stat RMSE (kgha) 119889-Stat

Grain yield 133 073 87 062 140 060Biomass 178 0 93 418 066 236 083


Table 9 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 yields1998 2001 1999 2008

MAR APR MAR APR MAR APR MAR APRDEKAD 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 35-day radic radic radic radic radic radic radic radic radic radic radic10-day radic radic radic radicYield (tha) 048ndash057 054ndash058 265ndash288 282ndash284Rain (mm) 388 989 664 632 2116 117 1821 423Rainy days 6 8 6 8 12 5 10 11radic indicates occurrence of a dry spell in a decade (10-day interval) within a month

000

050

100

150

200

250

300

350

MaciaPatoTegemeo

Gra

in y

ield

(th

a)

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009Year

Figure 1 Simulated grain yield of sorghum varieties under baseline (1980ndash2010) conditions at Hombolo

(Table 6) the simulated average sorghum yields were consis-tently higher under all GCMs (with HADGEM-ES giving thehighest) compared with baseline for all the three sorghumvarieties The increased yield therefore may be attributedto temperature increases Similarly Turner and Rao andZinyengere et al [43 44] find sorghum gaining in terms ofgrain yields fromhigher temperatures in specific regions withlower baseline temperatures (below 20∘C) Simulation resultsfrom the current study could answer the questions aboutfuture development trajectory in the study area Moreoveras discussed by Enfors et al [45] increasing investments insmall-scale water system technologies provides opportunities

for the small-scale farming systems that dominate the studyarea to leverage the uncertainty of the future climates

4 Conclusions

The field experimental results for the two seasons show con-siderable variations in grain yields among varieties An earlymaturing variety Macia gave higher yields in both seasonscompared to vars Pato and Tegemeo Model simulated yieldsreveal that the length and timing of dry spells during thegrowing season are major determinants of grain yields andthey surpass total seasonal rainfall amount even for a hardy


000

050

100

150

200

250

Pato Tegemeo Pato Tegemeo Pato Tegemeo Pato Tegemeo Pato TegemeoMaciaHombolo

MaciaDodoma

MaciaMpwapwa

MaciaManyoni

MaciaSingida

Gra

in y

ield

(th

a)

BaselineGFDL

HADGEMMIROC

Figure 2 Simulated grain yields of Pato Macia and Tegemeo sorghum varieties under baseline and future climatic conditions in centralTanzania

crop like sorghum Results suggest that occurrence of a longdry spell (10-day or longer) during the period from flagleaf appearance to start of grain filling is critical and couldsignificantly reduce yieldTherefore considering the inabilityof smallholder farmers to construct and maintain rain-waterharvesting (RWH) structures the government and devel-opment partners should consider increasing investments inthe same to ensure supplemental irrigation during criticalstages The availability of water would enable smallholdersgrowing sorghum to leverage the uncertainty in climatebut also to tap the opportunities brought in by increasedtemperatures The phenological characterization of the threevarieties and subsequent calibration and validation of APSIMhave provided a basis on which various kinds of simulationscould be done with the aim of increasing and sustainingsorghum productivity

Disclosure

This paper is based on the first chapter of a PhD dissertationsubmitted to Sokoine University of Agriculture (SUA) Anyerrors in the article are the authorsrsquo responsibility

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors thank the project ldquoEnhancing Climate ChangeAdaptation in Agriculture and Water Resources in theGreater Horn of Africa (ECAW)rdquo under the Soil Water Man-agement ResearchGroup (SWMRG) of SokoineUniversity ofAgriculture for partially supporting this research

References

[1] P Q Craufurd V Mahalakshmi F R Bidinger et al ldquoAdap-tation of sorghum characterisation of genotypic floweringresponses to temperature and photoperiodrdquo Theoretical andApplied Genetics vol 99 no 5 pp 900ndash911 1999

[2] M K Maredia D Byerlee and P Pee ldquoImpacts of food cropimprovement research evidence from sub-Saharan AfricardquoFood Policy vol 25 no 5 pp 531ndash559 2000


[3] S Keya and P Rubaihayo ldquoProgress in on-farm production andproductivity in the east african community 50 years after inde-pendencerdquo Kilimo Trust Technical Paper No 8 InternationalSymposium on Agriculture EAC partner states at 50 yearsNovember 2013

[4] N Hatibu H F Mahoo E M Senkondo et al ldquoStrategies forsoil-water management for dryland crop production in semi-arid Tanzaniardquo inProceedings of Tanzania Society of AgriculturalEngineers vol 6 pp 83ndash97 1993

[5] T L Bucheyeki E M Shenkalwa T X Mapunda and L WMatata ldquoYield performance and adaptation of four sorghumcultivars in Igunga and Nzega districts of Tanzaniardquo Communi-cations in biometry and crop science vol 5 no 1 pp 4ndash10 2010

[6] H M Saadan M A Mgonja and A B Obilana Performanceof the Sorghum Variety Macia in Multiple Environments inTanzania 2000

[7] J Mugwe D Mugendi J Kungu and M-M Muna ldquoMaizeyields response to application of organic and inorganic inputunder on-station and on-farm experiments in central KenyardquoExperimental Agriculture vol 45 no 1 pp 47ndash59 2009

[8] L R Stone and A J Schlegel ldquoYield-water supply relationshipsof grain sorghum and winter wheatrdquoAgronomy Journal vol 98no 5 pp 1359ndash1366 2006

[9] K Traore J B Auneb and B Traore ldquoEffect of organic manureto improve sorghum productivity in flood recession farming inYelimane Western Malirdquo American Scientific Research Journalfor Engineering Technology and Sciences vol 23 no 1 pp 232ndash251 2016

[10] N Batisani and B Yarnal ldquoRainfall variability and trends insemi-arid Botswana implications for climate change adaptationpolicyrdquo Applied Geography vol 30 no 4 pp 483ndash489 2010

[11] R Lazaro F S Rodrigo L Gutierrez F Domingo andJ Puigdefabregas ldquoAnalysis of a 30-year rainfall record(1967ndash1997) in semi-arid SE spain for implications on vegeta-tionrdquo Journal of Arid Environments vol 48 no 3 pp 373ndash3952001

[12] R Matthews W Stephens T Hess T Middleton and AGraves ldquoApplications of cropsoil simulationmodels in tropicalagricultural systemsrdquoAdvances in agronomy vol 76 pp 31ndash1242002

[13] BMMsongaleliAssessment of the impacts of climate variabilityand change on rainfed cereal crop productivity in central Tan-zania [PhD dissertation] Sokoine University of AgricultureMorogoro Tanzania 2015

[14] B A Keating P S Carberry G L Hammer et al ldquoAn overviewof APSIM a model designed for farming systems simulationrdquoEuropean Journal of Agronomy vol 18 no 3-4 pp 267ndash2882003

[15] D S MacCarthy and P L G Vlek ldquoImpact of climate changeon sorghum production under different nutrient and cropresiduemanagement in semi-arid region of Ghana a modellingperspectiverdquo African Crop Science Journal vol 20 pp 243ndash2592012

[16] W Mupangwa J Dimes S Walker and S Twomlow ldquoMea-suring and simulating maize (Zea mays L) yield responsesto reduced tillage and mulching under semi-arid conditionsrdquoAgricultural Sciences vol 2 no 3 pp 167ndash174 2011

[17] A C Guzha ldquoEffects of tillage on soil microrelief surfacedepression storage and soil water storagerdquo Soil and TillageResearch vol 76 no 2 pp 105ndash114 2004

[18] R Stern D Rijks I Dale and J Knock INSTAT (InteractiveStatistics) Climate Guide 2006

[19] N I Kihupi A K P R Tarimo and H O Dihenga ldquoSpatialand temporal variation of growing season characteristics inTanzaniardquo Journal of the Geographical Association of Tanzaniavol 32 pp 33ndash49 2007

[20] D Mazvimavi ldquoInvestigating changes over time of annualrainfall in ZimbabwerdquoHydrology and Earth System Sciences vol14 no 12 pp 2671ndash2679 2010

[21] G Hadgu K Tesfaye G Mamo and B Kassa ldquoTrend andvariability of rainfall in Tigray Northern Ethiopia analysis ofmeteorological data and farmersrsquo perceptionrdquoAcademia Journalof Agricultural Research vol 1 pp 88ndash100 2013

[22] K Yenigun V Gumus and H Bulut ldquoTrends in streamflow ofthe Euphrates basin Turkeyrdquo Proceedings of the Institution ofCivil Engineers Water Management vol 161 no 4 pp 189ndash1982008

[23] B Ncube J P Dimes M T van Wijk S J Twomlow and KE Giller ldquoProductivity and residual benefits of grain legumesto sorghum under semi-arid conditions in south-western Zim-babwe unravelling the effects of water and nitrogen using asimulation modelrdquo Field Crops Research vol 110 no 2 pp 173ndash184 2009

[24] A M Whitbread M J Robertson P S Carberry and J PDimes ldquoHow farming systems simulation can aid the devel-opment of more sustainable smallholder farming systems insouthern Africardquo European Journal of Agronomy vol 32 no 1pp 51ndash58 2010

[25] Z J Mkoga S D Tumbo N Kihupi and J Semoka ldquoExtrapo-lating effects of conservation tillage on yield soil moisture anddry spell mitigation using simulation modellingrdquo Physics andChemistry of the Earth Parts ABC vol 35 no 13-14 pp 686ndash698 2010

[26] C A Jones and J Kiniry CERES-Maize A Simulation Model ofMaize Growth and Development Texas A ampMUniversity PressCollege station TX USA 1986

[27] M Kouressy M Dingkuhn M Vaksmann and A B Heine-mann ldquoAdaptation to diverse semi-arid environments ofsorghum genotypes having different plant type and sensitivityto photoperiodrdquo Agricultural and Forest Meteorology vol 148no 3 pp 357ndash371 2008

[28] J T Ritchie ldquoSoil water balance and plant water stressrdquo inUnderstanding Options for Agricultural Production Y Tsuji YGordon Gerrit Hoogenboom and Philip K Thornton Edspp 45ndash58 Kluwer Academic DodrechtTheNetherlands 1998

[29] K E Saxton W J Rawls J S Romberger and R I PapendickldquoEstimating generalized soil-water characteristics from texturerdquoSoil Science Society of America Journal vol 50 no 4 pp 1031ndash1036 1986

[30] R Chikowo Climatic Risk Analysis in Conservation Agriculturein Varied Biophysical and Socio-Economic Settings of SouthernAfrica Food and Agriculture Organization of the UnitedNations (FAO) Rome Italy 2011

[31] J Hussein ldquoA review of methods for determining availablewater capacities of soils and description of an improvedmethodfor estimating field capacityrdquo Zimbabwe Journal of AgriculturalResearch vol 21 pp 73ndash87 1983

[32] J Rurinda P Mapfumo M T van Wijk et al ldquoComparativeassessment of maize finger millet and sorghum for householdfood security in the face of increasing climatic riskrdquo EuropeanJournal of Agronomy vol 55 pp 29ndash41 2014


[33] S Pierce A M Mbwaga G Ley et al Chemical Characteristicsof Soil And Sorghum from Striga-Infested Regions of Tanza-nia and The Influence of Fertiliser Application University ofSheffield Sheffield UK 2003

[34] DWallach ldquoEvaluating cropmodelsrdquo inWorking withDynamicCrop Models Evaluation Analysis Parameterization and Appli-cations D Wallach D Makowski and J W Jones Eds pp 11ndash54 Elsevier Amsterdam The Netherlands 2006

[35] C JWillmott S G Ackleson R EDavis et al ldquoStatistics for theevaluation and comparison of modelsrdquo Journal of GeophysicalResearch vol 90 pp 8995ndash9005 1985

[36] S Hempel K Frieler L Warszawski J Schewe and F PiontekldquoA trend-preserving bias correctionmdashthe ISI-MIP approachrdquoEarth System Dynamics vol 4 no 2 pp 219ndash236 2013

[37] K Riahi A Grubler and N Nakicenovic ldquoScenarios of long-term socio-economic and environmental development underclimate stabilizationrdquo Technological Forecasting and SocialChange vol 74 no 7 pp 887ndash935 2007

[38] P K T Munishi ldquoAnalysis of climate change and its impacts onproductive sectors particularly agriculture in Tanzaniardquo TechRep Ministry of Finance and Economic Affairs 2009

[39] P Camberlin and R E Okoola ldquoThe onset and cessation of thelsquolong rainsrsquo in Eastern Africa and their interannual variabilityrdquoTheoretical Applied Climatology vol 75 pp 43ndash54 2003

[40] M A Lema and A E Majule ldquoImpacts of climate changevariability and adaptation strategies on agriculture in semi-arid areas of Tanzania the case of Manyoni District in SingidaRegion TanzaniardquoAfrican Journal of Environmental Science andTechnology vol 3 no 8 pp 206ndash218 2009

[41] G A Baigorria J W Jones D W Shin A Mishra and J JOrsquoBrien ldquoAssessing uncertainties in crop model simulationsusing daily bias-corrected regional circulation model outputsrdquoClimate Research vol 34 pp 211ndash222 2007

[42] G S Premachandra D T Hahn and R J Joly ldquoLeaf waterrelations and gas exchange in two grain sorghum genotypesdiffering in their pre- and post-flowering drought tolerancerdquoJournal of Plant Physiology vol 143 no 1 pp 96ndash101 1994

[43] N C Turner and K P C Rao ldquoSimulation analysis of factorsaffecting sorghum yield at selected sites in eastern and southernAfrica with emphasis on increasing temperaturesrdquoAgriculturalSystems vol 121 pp 53ndash62 2013

[44] N Zinyengere O Crespo S Hachigonta and M TadrossldquoLocal impacts of climate change and agronomic practices ondry land crops in Southern Africardquo Agriculture Ecosystems andEnvironment vol 197 pp 1ndash10 2014

[45] E I Enfors L J GordonGD Peterson andD Bossio ldquoMakinginvestments in dryland development work participatory sce-nario planning in the Makanya catchment Tanzaniardquo Ecologyand Society vol 13 no 2 p 42 2008

Submit your manuscripts athttpswwwhindawicom

Nutrition and Metabolism

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Food ScienceInternational Journal of

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International Journal of



Microbiology

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

AgricultureAdvances in


PsycheHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BiodiversityInternational Journal of


ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GenomicsInternational Journal of


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Forestry ResearchInternational Journal of


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Cell BiologyInternational Journal of


Evolutionary BiologyInternational Journal of



analyses have shown decreasing trends (eg [10]) associatedwith decreases in the number of rainy days while othershave revealed neither abrupt changes nor trends [11] Thesecontrasting results suggest the need for undertaking locationspecific analyses of rainfall trends to ascertain contentiousassertions on the same A combination of field experimentsand computer simulation models could be an appropriateoption to comprehend the biophysical (climatic and soilconditions) factors and their interactions affecting crop yieldand productivity [12 13]

The Agricultural Production System sIMulator (APSIM)[14] is able to simulate growth and yield under differentmanagement practices and has been used by several studiesunder semiarid environments (eg [15 16]) This studytherefore used the APSIM model to simulate sorghumgrowth and yield patterns over the current (baseline) climateunder existing soil conditions and local management prac-tices across selected locations in semiarid central TanzaniaSpecifically the study used APSIM crop simulation modelto investigate how rainfall variability may affect yields ofimproved sorghum varieties based on long-term historicalrainfall and projected climate

2 Materials and Methods

21 Study Area The central zone comprising Dodoma andSingida regions is located between latitudes 6∘ and 06∘08 Sand longitudes 34∘301015840 and 35∘451015840 EThe experimental site waslocated at Hombolo Agricultural Research Institute (ARI)in Dodoma Region about 58 km North-East of DodomaMunicipality at latitude 05∘451015840 S and longitude 35∘571015840 E Themean annual rainfall is 589mm but the distribution is highlyvariable The average annual temperature is 227∘C Soils atthe experimental site are mainly sandy and loamy of lowfertility They are classified as Ferralic Cambisols in the FAOclassification [17]

22 Experimental Design and Data Collection Field experi-ments were conducted during 201213 and 201314 seasonsThree sorghum varieties namely Tegemeo Macia and Pato(the most widely grown varieties in the central zone) wereused as treatments in a randomized complete block design(RCBD) with three replications The recommended agro-nomic practices such as plant spacing andweeding are similarfor the three varieties Sowing was conditioned upon theprevious day having received significant rainfall so as to wetthe soil Sorghum was sown at a spacing of 075m betweenrows and 030m within the row resulting in a plant densityof 12 plants mminus2 Weeding was done manually three timesduring the season on each plot using a hand hoe

In order to provide near-optimum conditions diammo-nium phosphate (DAP) fertilizer was applied during plantingto supply 25 kg Pha and 40 kgNha Another round of Nfertilization was done by applying 40 kgNha as Urea sevenweeks after planting The phenological data collected for thethree sorghum varieties included date of flowering and dateof physiological maturity These were noted when 50 ofplant population in each plot had attained that respective

stage Grain maturity was regarded to have been reachedwhen dark spots at the point of attachment of the grain to thepanicle started to show which was towards the end of Aprilfor both seasons At final harvest total aboveground biomassand grain yield were determined

23 Historical Climatic Trends Daily weather data duringboth seasons were obtained from observations at an agrometstation located about 500m from the experimental plots Pastclimate data (1961ndash2010) for selected weather stations exceptHombolo (1974ndash2010) in the central zone Tanzania wereanalysed for trends INSTAT plus (v336) software [18] wasused to summarize the daily data into annual monthly andseasonal totals and to determine the onset taken as the firstoccasion after the earliest possible date on which a runningtotal of at least 20mmof rain was reached in four consecutivedays with at least two days being wet and that no dry spell of10 days or more occurred in the next 30 days [19] Cessationof the rainy season was obtained through a water balancemethod and verified by visual daily display in INSTAT andlength of growing period (LGP) was taken as the durationbetween the onset and cessation dates

The Mann-Kendall test was used to test for significanceof time series trends in total annual rainfall seasonal rainfallonset date cessation date and LGP The Mann-Kendall testis less sensitive to outliers and has the capability to detectboth linear and nonlinear trends and has been used in relatedstudies in sub-Saharan Africa [20 21] The median measurewas used to show onset and cessation dates and days of LGPas it is relatively unaffected by extreme values compared tothe mean

The Mann-Kendall test statistic is given as

119878 = 119873minus1sum119894=1

119873sum119895=119894+1

sgn (119909119895 minus 119909119894) (1)

where 119878 is the Mann-Kendall test statistic 119909119894 and 119909119895 arethe sequential data values of the time series in the years 119894and 119895 (119895 gt 119894) and 119873 is the length of the time series Apositive 119878 value indicates an increasing trend and a negativevalue indicates a decreasing trend in the data series The signfunction is given as

sgn (119909119895 minus 119909119894) =

+1 if (119909119895 minus 119909119894) gt 00 if (119909119895 minus 119909119894) = 0minus1 if (119909119895 minus 119909119894) lt 0

(2)

For119873 larger than 10119885MK approximates the standard normaldistribution [22] and is computed as follows

119885MK =

119878 minus 1radicVar (119878) if 119878 gt 00 if 119878 = 0119878 + 1radicVar (119878) if 119878 lt 0

(3)

























032 021011

032 021011

045 030015

056 032012

052 038020

Source AfSIS



Onset


Cessation


LGP (days)
























Variety


Grain yield(kgha)


Grain yield(kgha)


Days to 50flowering


Plant height(max)mm










Photoperiod












000

050

100

150

200

250

300

350

MaciaPatoTegemeo

Gra

in y

ield

(th

a)

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009Year




4 Conclusions



000

050

100

150

200

250


MaciaDodoma

MaciaMpwapwa

MaciaManyoni

MaciaSingida

Gra

in y

ield

(th

a)

BaselineGFDL

HADGEMMIROC



Disclosure




Acknowledgments


References


















































Journal of




Agronomy





Microbiology

















































032 021011

032 021011

045 030015

056 032012

052 038020

Source AfSIS



Onset


Cessation


LGP (days)
























Variety


Grain yield(kgha)


Grain yield(kgha)


Days to 50flowering


Plant height(max)mm










Photoperiod












000

050

100

150

200

250

300

350

MaciaPatoTegemeo

Gra

in y

ield

(th

a)

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009Year




4 Conclusions



000

050

100

150

200

250


MaciaDodoma

MaciaMpwapwa

MaciaManyoni

MaciaSingida

Gra

in y

ield

(th

a)

BaselineGFDL

HADGEMMIROC



Disclosure




Acknowledgments


References


















































Journal of




Agronomy





Microbiology














































Variety


Grain yield(kgha)


Grain yield(kgha)


Days to 50flowering


Plant height(max)mm










Photoperiod












000

050

100

150

200

250

300

350

MaciaPatoTegemeo

Gra

in y

ield

(th

a)

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009Year




4 Conclusions



000

050

100

150

200

250


MaciaDodoma

MaciaMpwapwa

MaciaManyoni

MaciaSingida

Gra

in y

ield

(th

a)

BaselineGFDL

HADGEMMIROC



Disclosure




Acknowledgments


References


















































Journal of




Agronomy





Microbiology





























000

050

100

150

200

250

300

350

MaciaPatoTegemeo

Gra

in y

ield

(th

a)

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009Year




4 Conclusions



000

050

100

150

200

250


MaciaDodoma

MaciaMpwapwa

MaciaManyoni

MaciaSingida

Gra

in y

ield

(th

a)

BaselineGFDL

HADGEMMIROC



Disclosure




Acknowledgments


References


















































Journal of




Agronomy





Microbiology


























000

050

100

150

200

250


MaciaDodoma

MaciaMpwapwa

MaciaManyoni

MaciaSingida

Gra

in y

ield

(th

a)

BaselineGFDL

HADGEMMIROC



Disclosure




Acknowledgments


References


















































Journal of




Agronomy





Microbiology








































































Journal of




Agronomy





Microbiology

























performance of sorghum varieties under variable rainfall...

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