Draft inception report, June 2012mori123kokusai.livedoor.blog/Annexes_30May2013.pdf ·...

1

Annexes

Annexes to Report “Determinants of the Productivity and Sustainability of Irrigation Schemes in

Zimbabwe & Pre-Investment Framework”

Contents Annex 1 Stakeholder consultations ......................................................................................................... 2

Annex 2 Selected irrigation schemes for field survey ............................................................................ 4

Annex 3 Existing dams in Zimbabwe ................................................................................................... 10

Annex 4 Proposed Large Dams Which Require Funding ..................................................................... 16

Annex 5 The irrigation database ........................................................................................................... 17

Annex 6 Risk categories in irrigation performance .............................................................................. 19

Annex 7 Ongoing Irrigation Schemes ................................................................................................... 21

Annex 8 Proposed irrigation scheme ranking matrix ............................................................................ 22

Annex 9 Crop yields ............................................................................................................................. 25

Annex 10 Analysis of field survey data (diagnostic study) .................................................................. 27

2

Annex 1 Stakeholder consultations

Name Organisation Position Contact Details Dates

Government Ministries and Departments/Task teams

Mr. Muzambindo MoAMID Principal Director Meeting: 18/07/12

@09:00: Done. To

arrange another meeting

Dorcas Tawonashe MoAMID Senior Economist Several meetings held.

Dr C. Zawe DoI Acting Director Several meetings held

Mr S. Kadaira DoI Act. Asst. Director

(Planning)

Attending meeting

together.

Mr Zvavamwe DoI Acting Chief

(Economist)

0772938763

Mr Bezzel Chitsungo DoI Acting Asst. Director

(Development)

eng.chitsungo@

gmail.com

M. Rupfutse DoI Act. Chief

(Development)

16 July 2012 @12:00.

Done

Mr V. Charegwa DoI Engineer 16 July 2012 @12:00.

Done

Mr R. Chitsiko MoWRM PS Tuesday 2 August 2012.

Done

Mr R. Moritaki DoI JICA Expert (Irrigation

Policy Advisor)

moritaki.jica@g

mail.com

Several

State Owned Entities (Parastatals)

Maxwell Chikanda AMA1 Director (Production) 09 July 2012 at

15:00hrs. Done

Mr. Mboma ARDA2 Acting General

manager

0712401884 27 July 2012 – 15:00

Met but requires LoI

from MAMID. Meeting

28/08/12. Done

Mr. Mushamba REA3 Business Development

Executive

Meeting 28/08/12. Done

DDF4

National Irrigation

Policy Workshop

Attended. 8 May 2012

National Water Policy

Workshop

Attended. 25 July 2012

Development Partners, Programmes and projects

John New Zim-AIED

(USAID)

Chief of Party Meeting: 10 July 2012.

Done

Emelda Berejena Zim-AIED

(USAID)

Irrigation Specialist 0773367141

emeldaberejena

@yahoo.com

Meeting: 10 July 2012

at 10:00 Done

Kuda Ndoro Zim-AIED

(USAID)

Deputy Chief of Party 0772243706 Meeting: 10 July 2012

at 10:00. Done

Martin Ager FAO Meeting done.

Joylin Ndoro Dutch Embassy Spoke on the phone.

Abla Benamouche IFAD Country Programme

Manager

Meeting: 14 July 2012

at 15:00hrs. Done.

Irrigation Working

Group

0772268468 Meeting: 15th June

2012.

Zimbabwe

Agricultural

Competitiveness

Workshop

Zim-ACP

(USAID)

Godfrey Mudimu

(Deputy Chief of Party)

0772315523 Attended: 10- 11 July

2012

Farmer Organisations

1 Agricultural Marketing Authority 2 Agricultural Rural Development Aency 3 Rural Electrification Agency 4 District Development Fund

mailto:[email protected]






3

Graham Mullet Evaluation

Consortium

(CFU)

CEO Eastlea

Shopping

Centre

Meeting: 7/8/12 at

12:00pm. Done

Adiel Karima ZCFU Secretary General Harare

Agricultural

Showgrounds,

P. O. Box CY

610, Causeway,

Harare.

Tel: 773059-61,

773039-40

Email: info@zc

fu.org.zw

Sent letter of

introduction – 28/08/12

Mr Tsimba ZCFU Acting Executive

Director

0773801933

(PA)

Delivered letter of intro

– 27/08/12. Done

Combined Biri

Irrigators

Association

Chairman

Private Sector

Francis Macheka Agribank Executive Director:

Retail banking and

Agriculture

Development

04 774400-20

774429-33

Direct:

04774394

fmacheka@agri

bank.co.zw

Meeting: 05/9/12. Done

Helen Makanha IDBZ5 Head: Agric Unit 04 779004/10;

779013/14

hmakanha@idb

z.co.zw

Meeting: 7/8/12 at

10:00. Sent email with

ToR 6/8/12. Done

5 Infrastructure Development Bank of Zimbabwe







4

Annex 2 Selected irrigation schemes for field survey

A representative set of irrigation schemes was selected for a field survey to collect irrigation data at

scheme and farm level. The field data were collected for a diagnostic analysis of irrigation

performance. The criteria for selection that were maintained are:

Selection criteria

The selection schemes should represent:

- Different irrigation categories;

- Various performance levels. Successful schemes will be compared against less successful or

problematic schemes. The objective of comparing well performing schemes with poorly

performing schemes, is to find out the key factors for success. Emphasis is on the successful

schemes;

- Different Natural Regions in the country. Emphasis is put the drier Natural Regions (III and IV)

where water is a limiting factor;

- Different scheme sizes (0-20ha; 20-100; 100+ and 1000+ hectares);

- Different land ownerships in each category such as leasehold, freehold and communal;

- Different management types: farmer managed and government managed schemes

- Different irrigation technologies. Water is considered to be the most limiting resource in irrigation

development, hence emphasis is put to comparing efficiencies of irrigation technologies.

- Schemes that the GoZ and stakeholders see as priorities. The Department of Irrigation indicated

priority schemes to be included in the assessment.

Based on these criteria, a number of 124 schemes has been selected for sampling together with the

Department of Irrigation. Below the full list of schemes is shown, their natural regions and the

provinces. The figure shows the distribution of the sampled schemes over the country.

Location of sampled schemes

A set of questionnaires was designed for the sampling, collecting information at scheme level and

farm level. From the 124 schemes that were sampled, a few schemes have been disqualified for

further processing as the questionnaires were not properly addressed (i.e. farms linked to wrong

scheme). In the end, a number of 110 schemes and 300 farms have been used for the analysis.

5

Number of schemes and farms included in the sample

Scheme Category

Schemes Farms Characteristics

A1 14 43 Small scale irrigation, communal resettlement

Inherited infrastructure shared by many farmers

Land tenure: Offer Letter or 99-year leases

A2 34 38 Private sector, commercial resettlement

Inherited infrastructure is shared by many farmers

Land tenure: Offer Letter or 99-year leases

Communal 53 190 Small scale community schemes

Land ownership: none, land tenure: communal

Legal registration: communal (shared resources)

Garden 9 29 Individual smallholder irrigation, informal irrigation

Land ownership: none, land tenure: communal

Legal registration: cooperative

Total 110 300

The A1 and A2 schemes sampled were mostly in Natural Region IIb and III, while the Communal

schemes and Gardens were mostly in the Natural Regions III, IV and V.

Number of schemes in the sample for the different natural regions

Natural region A1 A2 Communal Gardens Total no of schemes

I 1 1 1 0 3

II a 1 9 1 0 11

II b 5 17 3 2 27

III 4 5 17 3 29

IV 2 2 17 2 23

V 0 1 14 2 17

6

List of selected irrigation schemes

MANICALAND PROVINCE

Name of

scheme

District Category Area

(Ha)

Region Performance

(DoI perception)

Enumerator

Lawrence

dale1

Makoni A1 2 2b Poor Munyengeter

wa

Claremont Nyanga A1 5 2 Best Maereka B

Premier central Mutasa A1 100 2 Average Mutumwa K

Mutunha Buhera Communal 15 3 Poor Matienga I

Osborne Mutare Communal 2b Average Magurure B

Chiduku

ngowe

Makoni Communal 2b Average Munyengeter

wa P

Mupangwa Mutasa Communal 20 1 Average Mutumwa K

Nyamaropa Nyanga Communal 539 3 Best Maereka B

Nyanyadzi Chimanimani Communal Poor

Meikles Chipinge Communal 200 5 poor Mugariwa A

Middle Sabi

Farm35

Chipinge A2 5 Best Mugariwa A

Middle Sabi

Farm1

Chipinge A2 5 Average Mugariwa A

Tara Farm Makoni A2 2b Best Munyengeter

wa

Lawrence

dale2

Makoni A2 2b Average Munyengeter

wa

Fernkelly Mutare A2 2b Average Magurure

Berry Farm Mutasa Old

settlement

2 Best Mutumwa K

Mudzimu Buhera Garden 6 Average Matienga I

Ruwangwe Nyanga Garden 2 4 Poor Maereka B

Chisumbanje Chipinge Estate Best Mugariwa A

Easten

Highlands

Mutasa Estate Best Mutumwa k

MASHONALAND CENTRAL PROVINCE

Name of

scheme


(Ha)

Natural

region

Performance Enumerator

Chimhanda Rushinga Communal 72 4 Average Mutanga C

Mushumbi

agriventures

Mbire Communal Poor Takadiwa E

Dotito Mt Darwin Communal 50 3 Good Hwati G

Geluke Farm Bindura A1 2a Average Chivende M

Chipoli Shamva A1 3 Poor Murisa S

Rockwood 2 Centenary A1

Camperdown Guruve Old

Resettlement

2b Good Takadiwa E

Maguwo Rushing Gardens 4 Good Mutanga C

Dyaraishe Mbire Gardens 5 Poor Takadiwa E

Galloway Mazoe A2 2a Poor Muza D

Panache Mazoe A2 2a Good Muza D

Pearson Mazoe A2 2a Average Muza D

Teregwai Bindura A2 2b Good Chivende M

Pimento park Bindura A2 40 2a Poor Chivende M

Woodlands B Shamva A2 2a Average Murisa S

Inyika Shamva A2 Murisa S

Gomo Lot1 Guruve A2 2b Good Takadiwa E

7

Amanda Mt Darwin A2 2b Poor Hwati G

Mwonga Centenary A2

MASHONALAND EAST PROVINCE

Name of

scheme


(Ha)

Natural

region


Chibvuti Goromonzi A1 200 2 good Maringe

Mug Murehwa A1 100 2a average Chipunza

Cholo Seke A1 92 2b Non-

performing

Chiwodza

Scorror

(Musabayana)

Wedza A2 100 2b good Jenami

Masasa of

scorror

(Machaka )

Wedza A2 2b average Jenami

Welcome home Seke A2 30 2b average Chiwodza

Showers

(Chitongo)

Murehwa A2 82 2a average Chipunza

Exeter Marondera A2 60 2b average Tigerepayi

Gorejena

(Nyakonda)

Marondera A2 100 2b good Tigerepayi

Chifumbi of

meadows

(Kaukonde)

Goromonzi A2 220 2 good Maringe

Alymersfield

(Tapfumaneyi)

Goromonzi A2 60 2 poor Maringe

Karimba Marondera A2 3500 2b poor Tigerepayi

Murara Mtoko Communal 18 3 average Manhambara

Nyagande U M P Communal 12 4 poor Murimi

Nyahoni Chikomba Communal 20 3 average Muguti

Kudzwe Mudzi Communal 50 5 poor Chimambo

Nyaitenga Mtoko Old

resettlements

18 3 average Manhambara

Nhekairo Wedza Gardens 2b average Jenami

Dendera Mudzi Gardens 5 good Chimambo

MASH WEST PROVINCE

District

Farm Name Category Area

(HA)

Natural

region


Makonde Alaska Garden 10 IIb Average Lazaro J

Zvimba Banket Garden 7 IIb Good Baye

kariba Gatche gatche Communal 13 V Average Ruvengo

Hurungwe Magunje Communal 32 III Poor Rukarwa

Kadoma Ngezi A communal 205 III Good Matekwe

Chegutu Coburn A2 220 IIb Good Kanombirira

Makonde Amidale A2 150 IIb Average Lazaro

Makonde Highbery A2 120 IIb Average Lazaro

Makonde Chengu A2 150 IIb Average Lazaro

Zvimba Koodoo A2 90 IIb Poor Baye

Zvimba Fenemere A2 200 IIb Good Baye

Kadoma Railway A2 120 IIb Average Matekwe

Chegutu Lothian A2 180 IIb Poor Kanombirira

Chegutu Paarl Farm A1 120 IIb Average Kanombirira

Makonde Emily Park A1 200 IIb Good Lazaro

8

Hurungwe Mauya A1 180 III Poor Rukarwa

Zvimba CUT Farm Estate 1900 IIb Baye

MATEBELELAND NORTH PROVINCE

Name of

scheme


(Ha)

Region Performance Enumerator

Lungwangwa Binga Communal 5 Best Muzhinyi V

Lambo Hwange Communal 2.4 5 Poor Muzhinyi V

Tshongokwe Lupane Communal 24 5 Average Chidewu C

ARDA Jocholo Lupane Estate 500 5 Best Chidewu C

Mathema Farm Tsholotsho A1 3 5 Best Ncube N

Digils Park Bubi A1 40 5 Average Kanguwi F

Vukaswene Umguza Garden 4 Average Ncube N

Anju Umguza A2 204 4 Best Ncube N

MIDLANDS PROVINCE

Name of scheme District Category Area

(Ha)

Natural

region


Mkwena Shurugwi A1 3 Best Gucha

Sibanda Shurugwi A1 3 Poor Gucha

Hove Gweru A2 3 Best Maburuse

Gwenyaya Gweru A2 275 3 Poor Luah M

Chemahorororo Gokwe Communal 16 3 Best Kasiyani

Sengwa Gokwe Communal 22 3 Poor Kasiyani

Gwave Gokwe Communal Average Kasiyani

Mhende Mvuma Communal 304 3 Average Kasiyani

Madododo Zvishavane Garden 1 3 Best Maqele S

Msumhe Mberengwa Garden 3 Poor Mahlaba M

Plot 33

Sherwood

Kwekwe A2 10 3 Average Dube R

Bonstead

Sebakwe

Kwekwe A2 20 Average Dube R

Igogo Farm Kwekwe A2 136 Poor Dube R

Shamwari Farm Kwekwe A2 Best Dube R

MASVINGO PROVINCE

Name of

scheme


(Ha)

Natural

region


Dromore Masvingo A1 12 3 Average Mandiudza T

Munjanganja Gutu Communal 50 4 Best Mugwagwa J

Dinhe Mwenezi Communal 35 5 Average Mazira A

Rozva Bikita Communal 76 3 Average Mugwagwa J

Nyamakwe Chivi Communal 15 4 Poor Nemera M

Mkwasine (P.K

Chigura)

Chiredzi A2 5 Average Bondera M

Triangle Chiredzi Estate 5 Best Bondera M

Tokwane

Ngundu

Masvingo Old

Resettlement

285 4 Best Mutusva R

Mushandike Masvingo Old

Resettlement

600 3 Poor Mandiudza T

Chomugwaku Masvingo Old 30 4 Average Mutusva R

9

Resettlement

MATEBELELND SOUTH PROVINCE

Scheme

Name


(HA)

Natural

region


Makhado Gwanda A2 20 5 Average Moyo N

Lindmill

Farm

Umzingwane A2 5 Average Mwale S

Bradford Insiza A2 5 Poor Mugabe J

Bishopstone Beitbridge A1 200 5 Best Tashaya N

Bulembe Flat

20

Umzingwane A1 20 Average Mwale S

Makwe Gwanda Communal 202 Best Moyo N

Tshankwa Bulilima 34 Average Zhou Z

Riverange Beitbridge 20 Average Tashaya N

Tongwe Beitbridge 27 Poor Tashaya N

Thornvill Mangwe 80 Best Juru T

Ingwizi

outgrowers

Mangwe 100 Average Juru T

Mzinyethini Umzingwane 33 Average Mwale S

Antelope Matobo 150 Average Mwedzo T

Khumalo

Patrick

Matobo Gardens 3 Average Mwedzo T

Thombo Insiza 0.8 Poor Mugabe J

10

Annex 3 Existing dams in Zimbabwe

DAM

FULL

SUPPLY

NET YEAR PROVINCE DISTRICT USE OWNER 10% POTENTIAL

AREA

UNDER

CONSTRAINTS/REMARKS

OSBORNE 401620 1994 Manicaland Mutasa IR G 162500 10000 1000 Developments at Musikavanhu, Nyanyadzi South

RUTI 150000 1976 Manicaland Buhera IR G 77700 540 260 Needs funds for Bonde

RUSAPE 66964 1971 Manicaland Makoni WS/IR G 47200 1200 8000 Water is released in conjunction with Ruti dam

MHAKWE 540 1994 Manicaland Chimanimani IR G 291 20 Needs plans

NERUTANGA 1765 1971 Manicaland Buhera WS/IR G 1160 55

NYAHANGARE 320 1993 Manicaland Buhera IR G 120 20 Needs funds and irrigation plans

NYAMAROPA 1750 1975 Manicaland Nyanga IR G 753 450 400 Extension under construction

ARCADIA 58285

Mashonaland

Central Bindura IR P 40000

MWENJE 36117 1969

Mashonaland

Central Mazowe IR/WS/IN G 27082 150 10 50ha being developed by DDF. Need add. plans

MAZOWE 39357 1920

Mashonaland

Central Mazowe IR G 18300

JUMBO 21000 1993

Mashonaland

Central Concession IR/MI G/P 7500

MUFURUDZI 11677 1969

Mashonaland

Central Madziwa IR G 9000 100 50 Needs funds. 70ha designed

CHIMHANDA 5300 1986

Mashonaland

Central Rushinga IR/WS G 2200 90 70 20ha designed. Needs funds

BUMURURU 2000 1977

Mashonaland

Central Muzarabani IR/WS ARDA

EASTWOLDS 24000

Mashonaland

Central Mazowe IR P 6200

MASEMBURA 28653

Mashonaland

Central Bindura IR P 14681

PEMBI 2250 1961

Mashonaland

Central Mazowe IR/WS P 1250

WILLIAM LAURIE 20000

Mashonaland

Central Mazowe IR P 6500

11

HARAVA 9026 1973

Mashonaland

West Manyame WS/IR

City of

HRE 3380

KUSHINGA-

PHIKELELA 7721 1993

Mashonaland

East Marondera IR/WS G 4130

CHIKOMBA 5461 1968

Mashonaland

East Chivhu IR/WS 2000

MAHUSEKWA 2992 1989

Mashonaland

East Seke IR G 2458 100 20 needs funds & mobilisation of farmers

NYAVA 2734 1992

Mashonaland

East Shamva IR/WS G 1130 needs plans and funds

NYAMAPUNGA 1000

Mashonaland

East IR G 254

NYAMATANDA 1215

Mashonaland

East IR G 396 15 fully utilised

Rufaro, Nyambuya,

Nyakambiri 7606

Mashonaland

East Marondera WS/IN/IR G 3966

MANYAME 480236 1976

Mashonaland

West Zvimba WS/IR G 107220 300 20 50ha under construction

MAZVIKADEI 343779 1988

Mashonaland

West Zvimba IR/MI 99700 1200

CHIVERO 247181 1952

Mashonaland

West Zvimba WS/IR G 89300

CLAW 65455 1973

Mashonaland

West Kadoma WS/IR G 25400

NGESI 22686 1945

Mashonaland

West Kadoma IR/WS 14818

MAMINA 11361 1987

Mashonaland

West Chegutu IR 9330 300 216 needs funds for 50ha extension

SURISURI 9971 1971

Mashonaland

West Chegutu IR G 2890

BLOCKLEY 6220 1977

Mashonaland

West Karoi IR/WS 3260

CHIBERO 3000 1974

Mashonaland

West Chegutu WS/IR 2300

Clifton, MAYNARD

etc 17519 1968

Mashonaland

West Chegutu WS/IR

BHIRI-Manyame 172463

Mashonaland

West Zvimba IR NSSA/P 75084

MUTIRIKWI 1378082 1960 Masvingo Masvingo WS/IR G 383958 supply to sugar estates

MANYUCHI 303470 1985 Masvingo Mwenezi IR 104000 400 Mwenezi Dev. Corp. has 30 years right to the bulk of the water

12

MANJIRENJI 274179 1966 Masvingo Zaka IR 105000

BANGALA 126588 1962 Masvingo Masvingo IR G 130000

MUZHWI 106961 1990 Masvingo Masvingo IR/MI G 60000 728 supply to sugar estates

SIYA 105455 1976 Masvingo Bikita IR G 78210 200 300 also committed to sugar estates

MUSHANDIKE 37252 1938 Masvingo Masvingo IR 9200 624

MBINDANGOMBE 22583 1988 Masvingo Chivi IR G 6044 170

TOKWANE DAM 14467 1990 Masvingo Chiredzi IR 6500 supply to sugar estates

CHINGAMI 355 1990 Masvingo Mwenezi IR 231

CHINYAMATUMWA 2320 1992 Masvingo Bikita IR G 600 100 40 System needs to be electrified

NYAJENA 6185 Masvingo Masvingo IR 4900

GOZHO 1230 1972 Masvingo Masvingo IR 622

JIRI 20000 Masvingo Chiredzi IR 8980 supply to sugar estates

MABVUTE 3219 1993 Masvingo Zaka IR 1616 75

MAGUDU 5845 1991 Masvingo Masvingo IR 682 50

MAKONESE 2000 Masvingo Chivi IR 569 64

MASHOKO 1512 1993 Masvingo Bikita IR G 910 21 51

MHENDE 4000 1965 Masvingo Chirumanzu IR 1000 23

MUNJANGANJA 1969 1994 Masvingo Gutu IR G 584 51 51

MUTERI 74214 Masvingo Chiredzi IR 33344 supply to sugar estates, private

BANGA 1300 1986 Masvingo Chivi IR 600 61

NYATARE 2597 1987 Masvingo Zaka IR 2496 23

ROSWA 2819 1990 Masvingo Bikita IR G 1450 80 36

TUGWANE 3055 1987 Masvingo Masvingo IR 2060

TURRAMURA 324 Masvingo Gutu IR 154

KHAMI 3256 1928

Matabeleland

North Umgusa IR G 1000

UMGUSA Dams 4347 1945

Matabeleland

North Umgusa IR 361

KALOPE 1802 1990

Matabeleland

North Hwange IR 432 30

13

LUNGWALA 10800 1992

Matabeleland

North Binga IR 1720 110 110

MAMANDE 11736

Matabeleland

North IR/IN 1200

NGWENYA 1359 1952

Matabeleland

North IR 450

TSHONGOKWE 491 1990

Matabeleland

North Lupane IR 1750 6 6

INSIZA 173491 1971

Matabeleland

South Umzingwane WS/IR G 39800

ZHOVHE 130460 1990

Matabeleland

South Bet Bridge IR 45000 1000 needs funds urgently, ADB plans stalled

INGWEZI 67180 1967

Matabeleland

South Bulalima-Mangwe IR/WS 7440

UMZINGWANE 42179 1958

Matabeleland

South Umzingwane IR/WS G 13000

MTSHABEZI 52000 1994

Matabeleland

South Umzingwane IR/WS G 11350

SHASHANI 27340 1992

Matabeleland

South Matobo IR/WS G 9000 400

SILALABUHWA 23220 1967

Matabeleland

South Gwanda IR/WS 10743 400 400

ANTELOPE 12525 1971

Matabeleland

South Kezi IR/WS 6025

TULIMAKWE 6122 1966

Matabeleland

South Gwanda IR/MI 1620 202

VALLEY 5427

Matabeleland

South Kezi IR 103 200 200

MPOPOMA 2159 1951

Matabeleland

South Umgusa IR 600

LOWER MUJENI 10450

Matabeleland

South Gwanda IR/WS 7040

MASHOLOMOSHE 1068 1968

Matabeleland

South IR 40 silted

MBEMBESWANE 2318 1956

Matabeleland

South IR/WS 90 6

MHLANGWA,

MANGWE,

BULILIMA 14283

Matabeleland

South Bulalima-Mangwe WS/IR 1436 55

MOZA 3213 1987

Matabeleland

South Bulalima-Mangwe IR 1282 40

14

SIWAZE 2330

Matabeleland

South Insiza IR/WS 576 23

SUKWE 1700 1968

Matabeleland

South IR 360

UPPER INSIZA 8828

Matabeleland

South Umzingwane IR 4000

SEBAKWE 2 265733 1957 Midlands Chirumhanzu/Kwekwe WS/IR 95930

NGEZI 72320 1979 Midlands Kwekwe WS/IR G 33290

AMAPONGOKWE 37587 1980 Midlands Zvishavane IR/WS 5930

GWENORO 31357 1980 Midlands Shurugwi IR/WS 26205

EXCHANGE 14506 1972 Midlands Kwekwe IR 4932 14 14

INSUKAMINI 7792 1987 Midlands Gweru IR 2190 100

NGONDOMA 7487 1967 Midlands Kwekwe IR G 4770

LOWER ZIVAGWE 6993 1954 Midlands Kwekwe IR/WS G 900 45 45

CHIMWE 6416 1992 Midlands Mberengwa IR 1400

BIRI 2390 1986 Midlands Mberengwa IR 1905 20 1000x103m3 available for further development

HAMA 1725 1989 Midlands Chirumhanzu IR 1278 31 31

MABWEMATEMA 2300 Midlands Zvishavane IR 141 5

SHURUGWI 2116 Midlands Shurugwi IR/IN 1090

SOMALALA 1700 Midlands Kwekwe IR 580 7

IR- irrigation, MI- Mining,

HY- Hydroelectric, IN-

Industry, WS- Water Supply,

G- Government, P- Private

15

More recent completed dams

DAM

FULL SUPPLY

NET

(EXPECTED)

YEAR PROVINCE DISTRICT USE OWNER 10% POTENTIAL

AREA

UNDER CONTRAINTS/REMARKS

NAME

CAPACITY,

103m3

OF

COMPLETION YIELD IRRIGA ha

IRRIGA

ha

Dotito 2350 2002

Mashonaland

Central Mt Darwin WS/IR G 1040 70 Water supply to Dotito Growth Point and Irrigation of 70ha

Dande 160000 2005

Mashonaland

Central Guruve IR ARDA 50244 4000 Construction under suspension due to disputes over payments

Chivake 5000 2002 Masvingo Chivi WS/IR G 456 Water Supply to Ngundu Growth Point and Irrigation at Banga Scheme

Matezva 6600 2002 Masvingo Bikita WS/IR G 710 60 Water supply to Bikita Minerals and Irrigation of 60ha

Chikombedzi 1200 2002 Masvingo Chiredzi WS G 400 Water supply to Chikombedzi Growth Point

Hauke 4000 2003

Matabeleland

North Bubi WS/IR G 1000 20 Water supply to Siginda Growth Point and Irrigation of 20ha

Mondi Mataga 39000 2003 Midlands Mberengwa WS/IR G 500 Water supply to Mataga Growth Point and Irrigation of 65ha

Padres Pools 3200 2003 Midlands Kwekwe WS G 2648 Water supply to Connemara Prison

Mutange 4950 2004 Midlands Gokwe WS/IR G 1300 100 Water supply to Gokwe Growth Point and Irrigation of 100ha

IR- irrigation, MI- Mining, HY- Hydroelectric, IN- Industry, WS- Water Supply, G- Government, P- Private

16

Annex 4 Proposed Large Dams Which Require Funding

DAM

FULL SUPPLY

NET YEAR PROVINCE DISTRICT USE OWNER 10% POTENTIAL

AREA

UNDER CONTRAINTS/REMARKS

NAME

CAPACITY,

103m3 BUILT YIELD IRRIGA ha

IRRIGA

ha

Silverstroom 140,000

Mashonaland

Central Centenary IR/WS G 33345 2000 Irrigation of 2000ha and water supply to Centenary T/ship

Kudu 1,551,400

Midlands/Mash

West Kadoma/Gokwe IR G 380000 25000 Irrigation of 25000ha

Mhondoro 15,700 Mashonaland West Chegutu IR G 5290 6700 Irrigation of 6700ha

Leopard 80,000 Mashonaland West Kadoma IR G 34700 650 Irrigation of 650ha

Kunzwi 157,930 Mashonaland East Goromonzi/Murewa WS/IR G 70000 600 Water supply to Harare and irrigation of potentially 600ha

Bubi-Lupane 40,230 Matabeleland North Lupane WS G 11000 Water supply to Lupane

Ziminya 94,000 Matabeleland North Nkayi IR/WS G 20000 3000 Irrigation of 3000ha and water supply

Tuli-Manyange 33,000 Matabeleland South Gwanda IR G 22000 2400 Irrigation of 2400ha

Chitowe 1,542,000 Manicaland Chipinge/Chiredzi IR G 754200 35700 Irrigation of 35700ha

Kondo 3,565,000 Manicaland Mutare IR G 1521500 72000 Irrigation of 72000ha

Lundi-Tende 2,000,000 Masvingo Chivi/Mwenezi IR G 257677 2400 Irrigation of 2400ha

Mkwasine 160,000 Masvingo Chiredzi IR G 34000 2800 Irrigation of 2800ha

Gwenoro Dam raising 48,100 Midlands Gweru WS G 36000 Water supply to Gweru

IR- irrigation, MI- Mining, HY- Hydroelectric, IN- Industry, WS- Water Supply, G- Government, P- Private

17

Annex 5 The irrigation database

An irrigation database has been developed within the project. The database stores baseline

information on performance and risk factors in irrigation and is structured around 3 categories:

Crop productivity

Institutional arrangements

Marketing arrangements

Irrigation data

Data collected

Crop production, bio-

physical data

Natural region

Water source

Irrigated area

Irrigation technology

Water delivery security

Crop production levels, actual yields

Input: labour, fertilisers, chemicals

Water use efficiency

Crop choice

Institutional arrangements

Water rights

Land rights

Government managed, farmer managed, or jointly managed scheme

Land ownerships (leasehold, freehold and communal)

O&M arrangements and costs for maintenance of scheme infrastructure

Shared infrastructure

Regulatory agreements

Organisations involved with the irrigation schemes

Marketing arrangements Distance from input and output markets

Type of market

Contract farming or not

Prices

Volumes of inputs and outputs traded

Challenges indicated by farmers in marketing

Summary of data collected from the field survey using questionnaires

Two database forms were prepared to enter the data, one for schemes and one for farms. Each farm is

linked to its corresponding scheme with an ID. Drop down menus with prefixed titles/values were

used to ensure uniform entries which facilitate the data processing. After all data was entered, queries

from database were done to analyse the data and present the results grouped into farm, scheme,

irrigation category or natural region level (see Annex 10).

18

Example database forms

Database structure

19

Annex 6 Risk categories in irrigation performance

Adequacy and reliability of water supply: The source of water and means of conveying the water to

the field have an important influence on the performance of an irrigation scheme. As irrigation is the

control and application of water for improving production, this risk category can be viewed as one of

the most important. Without adequate and reliable water supply, for given productivity objective,

irrigation cannot be successful.

The impact of climate change may affect the adequacy and reliability of water supply to irrigation

schemes; hence need to understand the potential impact of climate change and put in the necessary

adaptation and mitigation measures.

This category affects both the productivity and operational costs in an irrigation schemes cash flow.

Supply of inputs: This category refers to the risk associated with the supply of essential inputs for

improving the productivity and profitability of the scheme. The inputs that can be considered under

this category are: water, fertilisers, chemicals, labour, improved seeds, appropriate machinery. This is

mainly an analysis of input markets and water supply.

This category affects both the productivity and operational costs in an IRRIGATION schemes cash

flow.

System design: The design of a water control system for applying water to the field is an important

risk category in the IRRIGATION scheme cash flow model. Does the system deliver adequate water

for the productivity goal? Is the system management and operating requirements suitable for the farm

level institutional arrangements? Are the maintenance costs affordable? Does the system allow for the

required, scheme and farm level, water use efficiency?

Support infrastructure: An irrigation scheme requires support infrastructure to be fully productive.

Support infrastructure important for enhancing irrigation productivity may include: feeder roads,

electricity, telecommunication, post-harvest facilities, among others. In discussions during the study,

some stakeholders have included the importance of schools and tertiary education and health facilities

for the long term improvement of agriculture in Zimbabwe. While these are important facilities and

may have an important role to play in irrigation performance they were not included in the analysis.

Market availability: The availability of markets for the produce from irrigation schemes help to

convert the produce into cash. Does the produce from the farmers have a market? Is the quality and

quantity produced suitable for the target market? Is the market accessible to the scheme? Does the

market offer a profitable price? Are there opportunities to value add produce so as to target a different

market?

Institutional participants at all levels: irrigation schemes have a number of participants performing

various activities. These participants may include scheme owners and staff, loan financiers, grant

financiers, buyers, service providers, government and local authorities. What is the attitude of each of

these participants on scheme performance, net cash flow? How do the scheme level institutional

arrangements reduce risk to the cash flow? What is the impact of national and local level institutional

arrangements on scheme performance?

20

Production related risk category: This risk category has three sub-components: technical knowhow;

cost control and general management. Do the scheme implementers have the required level of

knowledge and skill to attain the desired productivity within an acceptable cost structure?

Scheme completion: The construction of scheme construction affects the returns of the scheme if the

target irrigable area is not reached or water delivery is not to expected standard. Do all participants

understand the standards to be attained at scheme construction completion? Are these standards

verified at commissioning? Do they meet the farmers’ requirements?

Environmental: The irrigation activities should limit the longterm impact on the environment. Good

agricultural practices and the need to comply with international and local environmental laws may

have direct or indirect effects on the scheme cash flow through possible attainable yields, cost of

additional infrastructure, operational and overhead costs.

Policy, regulations, and legal environment: Government policy and legal framework affects the

scheme performance by setting an enabling environment for investment and production to be

enhanced. The rule of law on property (land and water) and contracts determines the performance of

irrigation schemes.

Force majeure: These are natural events that happen and affect scheme performance. How can the

effects of natural events be mitigated to enhance scheme performance, and protect the cash flow?

Financing: The availability of funding in ways that make it accessible to finance the various activities

in irrigation scheme categories is important for scheme performance. The mitigation of the negative

impacts of the above risk categories greatly enhances the chances of funding to be accessible to

irrigation scheme farmers.

21

Annex 7 Ongoing Irrigation Schemes

Ongoing Irrigation Schemes in 2013 National Budget

Scheme Province

Budgeted

Cost

1 ARDA Transau Manicaland 400,000

2 Bannockburn Midlands 400,000

3 Bengura Masvingo 300000

4 Bulawayo Kraal Matebeland North 600,000

5 Chesa Mutondwe

Mashonaland

Central 410,000

6 Chiduku Twikiri Manicaland 600,000

7 Chipoli D

Mashonaland

Central 300,000

8 Chimwe Chegato Midlands 240,000

9 Dangarendove Mashonaland East 200,000

10 Fanisoni Matebeland North 100,000

11 Gatche Gatche Mashonaland West 500,000

12 Hauke Matebeland North 90,000

13 Igudu Mashonaland East 250,000

14 Kwalu Matebeland South 500,000

15 Masembura

Mashonaland

Central 270,000

16 Matezva Masvingo 300,000

17 Meikles Manicaland 100,000

18 Mhende Midlands 350,000

19 Nyamangara Mashonaland West 100,000

20 Pollards Matebeleland North 270,000

21 Seke-Sanyati Mashonaland West 500,000

22 Shashe Masvingo 300,000

23 Silalabuwa Matebeleland South 550,000

24 Thembanani Matebeleland North 50,000

25 Wenimbi Mashonaland East 500,000

26 Nyanyadzi Manicaland 310,000

8,490,000

22

Annex 8 Proposed irrigation scheme ranking matrix

Proposed ranking matrix,

Example Manicaland Province Communal

Project name

Criteria Mudzimu Gardens

Chivoko Gardens

Mutunha

Murambinda

Nerutanga

Masenga

Deure Ruti Bonde Marovanyati

Shinja Mhakwe

Scheme parameters

Estimated costs

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

Po

ints

EIRR

NPV

No. Beneficiaries

Area (ha)

Economic Performance

NPV

EIRR

Production of priority crops

Employment creation

Social impact

Effect on rural livelihoods

Effect on women

Food security at household level

Environmental impact

Effect on ecosystem

Climate smart

Effect on water logging

23

Technical considerations

Adoption of new technologies

Availability of service facilities

Ease of operation and management

Financial consideration

Farmers income improvement

Availability of credit

Legal considerations

Land tenure security

Water permit adequacy

Registered association/company

Managerial considerations

New crop for the farmers

Marketing and post harvest requirements

Opportunity for PPPs

Total points per project

Rank

25

Annex 9 Crop yields

Crop yield distribution for the main crops (kg/ha), data from field survey

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

5

10

15

20

25

0 1800 3600 5400 7200 More

CU

MU

LA

TIV

E F

RE

QU

EN

CY

%

FR

EQ

UE

NC

Y (

NO

.)

YIELD (KG/HA)

WHEAT YIELD DISTRIBUTION (KG/HA)

Frequency Cumulative %

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

5

10

15

20

25

30

35

667 15,556 30,444 45,333 60,222 75,111 More

CU

MU

LA

TIV

E F

RE

QU

EN

CY

%

FR

EQ

UE

NC

Y (

NO

.)

YIELD (KG/HA)

TOMATOES YIELD DISTRIBUTION (KG/HA)


26

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

1

2

3

4

5

6

7

8

250 1687.5 3125 4562.5 More

CU

MU

LA

TIV

E F

RE

QU

EN

CY

%

FR

EQ

UE

NC

Y (

NO

.)

YIELD (KG/HA)

SOYA BEAN YIELD DISTRIBUTION (KG/HA)


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

2

4

6

8

10

12

14

900 11925 22950 33975 More

CU

MU

LA

TIV

E F

RE

QU

EN

CY

%

FR

EQ

UE

NC

Y (

NO

.)

YIELD (KG/HA)

POTATO YIELD DISTRIBUTION (KG/HA)


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

10

20

30

40

50

60

CU

MU

LA

TIV

E F

RE

QU

EN

CY

%

FR

EQ

UE

NC

Y (

NO

.)

YIELD (KG/HA)

MAIZE YIELD DISTRIBUTION (KG/HA)


27

Annex 10 Analysis of field survey data (diagnostic study)

1. Set up of analysis

The estimates from this analysis will provide indicators of the marginal productivity of land, water and irrigation, fertilizer, chemicals, labour, mechanisation, etc for selected crops. The results will help identify the regional, ecological, farm, and farmer characteristics associated with production among farmers in regions of Zimbabwe. The study derives technical efficiency measures for determinant factors on farms from the estimated production functions. Based on this analysis, it is possible to assess the correlations and regressions of production functions to yield in various regions and production systems to identify the determinants of productivity in irrigated agriculture in Zimbabwe.

The study matrix will include the following characterization parameters

Farm size

Natural Region

Farm category

Institutional Framework

Technology Investment

1.1 Yield Gap Definition Yield gaps are defined as the difference between the potential yield and the average yield a farmer currently achieves. This yield gap indicates, in a quantitative way, the increase in yield that can be obtained over the current yield levels under specifically defined management practices. Different measures could be used to estimate potential yield. The agronomic yield potential—defined as the yield obtained on experimental stations with no physical, biological, or economic constraints; using the best known techniques; applying sufficient inputs to stimulate crop growth to the maximum; and eliminating all pre-harvest and post-harvest losses. This is the maximum achievable yield and reflects the knowledge frontier and best known management practices at any given point in time. The yield gap is then estimated comparing the potential yield with yields obtained using current farming practices in areas with similar agro-ecological conditions (e.g. climate, physical and chemical soil characteristics, water availability etc.). The exploitable yield potential is the yield obtained with no physical or biological constraints with the goal of maximizing profits. The exploitable yield is lower than the agronomic yield potential given that it is constrained by economic considerations (output and input prices). The yield gap reflects mainly differences in management practices (for example, the amount of fertilizer used, land preparation, time of the year of different practices) under similar agro-ecological conditions. The national average yield is not an appropriate indicator of farm-level performance because it is an average across agro-climatic zones, soil types, crop ecologies, crop types, and technologies. For this reason, it is important to obtain average yields from homogenous agro-ecological conditions, similar to those used to measure potential yields, and also under similar production systems (technologies). Yield gaps have at least two components. The first of these cannot be narrowed, is not exploitable, and mainly owes to factors that are generally not transferable, such as the environmental conditions and some of the built-in technologies that are available at research stations or experimental farms. The second component arises when farmers use amounts of inputs and cultural practices different from the ones needed to achieve the agronomic yield potential and is mainly the result of differences in management practices. The differences in management practices, on the other hand, could result from deficiencies and lack of knowledge of the production technology, or it could reflect economic constraints given that, for instance, the level of fertilizer used by producers could maximize profits, not yields. Efforts to

28

narrow the yield gap without considering economic aspects may be counterproductive and may actually result in inefficient allocation of inputs, reducing farmers’ incomes. In other words, a large yield gap implies that farmers did not fully adopt the existing technologies because they were not packaged appropriately or because economic conditions made them unattractive. A small yield gap, on the other hand, indicates that the available technologies are almost fully used. Yield gaps can be determined with advanced approaches by estimating yield potential using detailed spatial information on soil associations (including soil water-holding capacity, slope, depth, and texture) and climate (radiation, temperature, rainfall) to model the response of different genetic materials simulating growth on a daily basis for the duration of a growing period. Of all the factors that affect crop performance, the most important are the efficiency of the use of radiation, the availability of water and nutrients, factors contributing to the soil water balance, and those affecting soil fertility. The yield gaps can be estimated by comparing these estimated values with those observed in different regions and conditions or by simulating production of the same crops under farming conditions. Estimates of yield gap magnitudes are challenging and important. These are limited in application if the causes of these yield gaps are not explained using practical constraints and related agents including the potential rates at which these gaps will narrow or widen. This task is only possible if one can identify the underlying causes of yield losses in farmers’ fields. The extensive list of factors that commonly affect crop growth and yields in farmers’ fields is varied. These factors include stresses that are biotic in nature and others that are mainly abiotic. In general, some of these factors are easy to measure while some are difficult to detect. The challenges faced in pursuit of understanding yield gaps for any given agro-ecological farming system in Zimbabwe are to identify among the many possible drivers for yield losses the few that have the greatest influence and to quantify the additional yield that could be realized if these constraints were removed. A goal of yield gap analysis is to quantify the percent of total losses attributable to each factor.

Figure 1 Relative Yields and Yield Gap in Analysis

Several approaches can be used to study causes of yield gaps, each with their own advantages and shortcomings. Figure 1 shows a bar chart of the reduction in yield from the maximum modelled yield potential to the lower average farmer field yield. Determination of yield gaps is used to measure the potential for improving agricultural productivity. In the study estimation of yield gaps is done for more than eight crops as an incremental output that could be produced if the study farms in natural agro-ecological regions close this yield gap through changes in management practices and the use of inputs in the context of present knowledge and available technologies. The concept of a yield gap is frequently used in technical agronomic analysis of production as a measure of performance because it implies a comparison between

29

yields actually obtained under particular agro-ecological conditions on commercial farms and the maximum or potential yield in the same region. 1.2 Measurement of Yield Potential The potential yield is determined by producing the crop without constraints that are normally found at the farm level, such as nutrient and water stress, inadequate cultivation practices. There are reasons for the extensive use of yields and yield gaps as a measure of production performance in agriculture. These include the fact that information needed to estimate yields, such as data on production and cultivated area in the case of crops or production can be directly observed and easy to obtain. The other reason is that yields are used as a measure of productivity and technical efficiency of the production process, and the narrowing of the yield gap is frequently targeted as a means to reach other goals. Yield potential is an economic concept, rather than a physical quantity, which makes estimation both challenging and complicated. By definition, yield potential is an idealized state in which a crop grows without any biophysical limitations other than uncontrollable factors, such as solar radiation, air temperature, and rainfall in rain-fed systems. Therefore, to achieve yield potential requires perfection in the management of all other yield-determining production factors (such as plant population; the supply and balance of 17 essential nutrients; and protection against losses from insects, weeds, and diseases) from sowing to maturity. Such perfection is impossible under field conditions, even in relatively small test plots let alone in large production fields. The use of yields as a measure of productivity is convenient because the difference between potential and observed yields could also be explained by economic constraints, because the optimal technical yield does not correspond with the yield that maximizes profits or minimizes costs. Apart from stimulating increased production (yield), closing the yield gap is frequently aimed also to improve the efficiency of land and labour use, to reduce the cost of production, and to increase sustainability. Thus, yield potential is sometimes estimated by crop models that assume perfect management and lack of all yield-reducing factors. The validity of such models relies on validation under field conditions, which can never achieve perfect management. We are left with a circuitous loop in which simulations are based on mathematical relationships that capture our current understanding of plant physiological processes that determine maximum possible net primary productivity (NPP) and the portion of NPP converted to grain yield, and these simulations are validated against field studies that attempt to establish perfect growth conditions but can never achieve it. The uncertainty as to whether highest possible yields were achieved in the validation field studies justifies conjunctive use of other methods to estimate yield potential. Other approaches include surveys of highest recorded historical yields at agricultural research stations, highest yields achieved in long-term experiments that included treatments thought to provide optimal management, and the yields achieved by contest-winning farmers who participate in sanctioned yield contests. At broader scales of relevance to food production capacity and regional to global food security, measurement of yield potential is even more difficult because of spatial variations in the climatic and soil conditions across the thousands of fields in a given production domain. Here, we consider three main techniques for assessing yield potential and yield gaps over relevant spatial scales, each with its own strengths and weaknesses.

30

Figure 2 Biotic and Abiotic factors that affect production

Socioeconomic Constraints to Production include suboptimal planting (timing or density), labour availability (man days), profit maximization, risk avoidance strategies, lack of knowledge of best practice management, unpredictable prices of key inputs, high transport and logistics costs, distorted markets for fertilizer nnutrient deficiencies and imbalances mainly in nitrogen, phosphorus, potassium, zinc, and other essential nutrients (supply, demand, prices), inefficiencies at harvest and storage problems, inability to secure credit, incomes and market prices (agricultural), farmer Training and knowledge on best practices, and market information.

1.3. Econometrics. Econometrics relate to the responsiveness of crop yields to price increases. This is known as the own-price elasticity of yields and is often a critical parameter in models of international agriculture. The relationship between yield elasticities and causes of the yield gap is clear: If yields are highly responsive to prices, then much of the gap must be attributable to input levels and management practices that are readily adjusted, such as fertilizer rates or weed and insect control. Alternatively, low yield elasticities imply that average yields are not constrained by factors amenable to such rapid changes. 1.4. Inferior Technology Performance The best expression of production performance and the prospects for longer term increases in output is the growth of TFP, the ratio of output to inputs in the production process, with productivity increased when growth in output outpaces growth in input. Productivity growth is the best kind of growth to aim for rather than attaining a certain level of output by increasing inputs, because when some of the inputs (for example, land) are constrained, output growth is subject to diminishing marginal returns. There could also be negative effects on the quality of natural resources and on the sustainability of the production process. Productivity varies due to differences in the environment in which production occurs, differences in production technology, and differences in the efficiency of the production process. We are interested in productivity changes related to technology and efficiency in different environments. The efficiency of a production unit is the comparison between observed and optimal values of its outputs and inputs. This comparison takes the form of the ratio of observed to maximum potential output obtainable from given inputs or, alternatively, the ratio of minimum potential inputs to observed inputs required to produce a given amount of output. These technologies represent the current state of our knowledge of what can be produced and how to combine resources to produce desired products. Thus, technological change occurs when technical knowledge increases. It is important to distinguish two components of production efficiency: technical efficiency and allocative efficiency. The formal definition of technical efficiency is when a production unit is experiencing an increase in any output requiring a reduction in at least

31

one other input. This definition implies that an inefficient producer could produce the same output with less of at least one input or could use the same inputs to produce more of at least one output. On the other hand, allocative or price efficiency refers to the ability to combine inputs and outputs in optimal proportions in light of prevailing prices. The production technology describes the possibilities for the transformation of input vector xt into output vector yt in a particular cropping season t. We define the technology to produce a single output (y) using multiple inputs as: (x1,,xn) in season t as: P(y) = {xi • R+2 | (y, xi) • t} and i = {1, n} This technology is an input possibility set with the amounts of two inputs needed to obtain one unit of output where the unit shown represents the technological frontier. The frontier of the input possibilities for a given output vector is defined as the input vector that cannot be reduced by a uniform factor without leaving the set. 1.5 Yields, Productivity, and Efficiency In practice, the production function giving efficient combinations of inputs to obtain a certain output is generally not known and must be estimated econometrically or using data envelopment analysis.

Figure 3 shows the unit isoquant of the technology representing the efficient combinations of inputs. This isoquant cannot be observed because of data limitations. What we observe is only one point at the isoquant (frontier) representing the recommended combination of land and labour from the experimental station (point A*). We also observe point A representing the average production unit, which can be both technically and allocatively efficient given the relative land and labour prices. What A can do is increase yields, moving up through the isoquant toward A*, but then it becomes allocative inefficiency, and there is no incentive for A to adopt the recommended combination of inputs. The yield gap could measure potential expansion of production when A is inefficient and produces within P(y) and not at the frontier.

Figure 3 Yield gaps and efficiency

at the point of allocative efficiency, the greater the difference between the input combination in A* and A, the more the yield gap will overestimate the potential. Thus, the best case for the yield gap to be an adequate approximation of potential output expansion occurs when (1) the observed average yields are obtained by using an input combination similar to the one used in the reference technology A* and (2) this combination is allocatively efficient. This is the case of point B in Figure 3, where prices are now represented by c1–c1•. But in this particular case the difference in yields

32

results from differences in efficiency and the yield gap is a good indicator of potential output expansion. 1.6 The Standardized Gross Value of Production - SGVP We are interested in the measurement of production from irrigated agriculture that can be used to compare across systems and across regions. If only one crop is considered, production could be compared in terms of mass. The difficulty arises when comparing different crops, say beans and maize, as one tonne of beans is not readily comparable to one tonne of maize. When only one irrigation system is considered, or irrigation systems in a homogenous region where prices are similar, production can be measured as net value of production yield and gross value of production using local values. The Standardized Gross Value of Production (SGVP) was used for cross system comparison as obviously there are differences in local prices at different locations throughout the country. To obtain SGVP, equivalent yield is calculated based on local prices of the crops grown, compared to the local price of the predominant, locally grown crop. This should not be adjusted for free on board FOB/cost insurance freight CIF and internal transport since we are interested in the productivity of irrigation, rather than the efficiency of markets, transport system, and project location. For example, if the local price of tomato is three times the local price of maize, we consider the production yield of 10 tons/ha of tomato to be equivalent to 30 tons/ha of maize. Total production of all crops is then aggregated on the basis of ‘maize equivalent’ and the gross value of output is calculated as this quantity of wheat multiplied by the world market price of wheat. The point of this is to capture local preferences— for example, specialized varieties that may have a low international price, but are locally highly valued—and also to capture the value of non-traded crops.

where, SGVP is the standardized gross value of production, Yi is the yield of crop i, Pi is the local price of crop i, Pworld is the value of the base crop traded at world prices, Ai is the area cropped with crop i, and Pb is the local price of the base crop.

The base crop is the main tradable crop cultivated in the study area, which is taken as maize for Zimbabwe. To eliminate distortions due to price fluctuations, for local as well as for international prices, averages are used: first, local prices per crop and per year. It could be argued that the indicator should be net value added rather than gross. There are two reasons to work with the gross figure. First, it is far easier to measure—many of the deductions that must be made to get from gross to net value added are susceptible to distortions (subsidies and taxes on inputs, credit, and irrigation services, for example) or otherwise very difficult to measure (appropriate prices for family labour, and the opportunity cost of land and water). Second, we note that the most common indicator of agricultural performance (yield per unit land) is itself a gross indicator, unqualified by indications of input levels, soil type, or even variety. Despite this simplicity, yield serves as a fundamental indicator of performance. 2 Yield Gap Estimation To be able to determine potential production expansion of different crops based on yield gaps, we need to obtain yields from similar production systems in homogenous agro-ecological conditions across the region. The study used the maximum observation of yield in the data obtained from similar agro-ecological and technological status to define yield potential, current yield defined as the 95% confidence limit from the survey sample in similar homogenous agro-ecological regions and technologies (including genetic resources). This method of estimating yield in the study has been necessitated by the limitations in data at both study and general national level. There is not enough information on agro-ecological regional yields of specifically defined genotypes. It is not possible to

33

use a common potential yield for all genotype and all natural agro-ecological regions. The use of the 95% confidence limit crop yield over the mean yield has been driven by the observation that the skewness in the observed yield suggests a generally low end heavy distribution with very high spikes of single outliers. These outliers have the tendency to shift the mean yield significantly. 2.1 Actual Yields and Yield Gaps in Homogeneous Agro-ecological Zones Information on production and yields of different crops is, in most cases, available only at national or aggregated national levels. The yield used in the study is derived from the data gathered through data aggregation for homogenous agro-ecological regions assuming that the genotype technology is similar across the agro-ecological region. The yield gaps are then aggregated at the homogenous agro-ecological development domain level, our basic spatial unit of analysis, to obtain yield gaps in homogeneous agro-ecological conditions and in areas with similar economic conditions and similar constraints and opportunities for development.

2.2 Spatial Analysis Using Geographic Information System Methods Geographic factors such as agro-ecological conditions, soils distribution, roads, rivers, dams, irrigation sites, farm categories, population distribution, and production and market locations and infrastructure are important in agricultural development. Our analytic approach involves gaining a better appreciation of regional patterns of agriculture potential and economic factors determining challenges and opportunities for agricultural development. GIS tools and databases were used to visualize similarities and differences across the region. We conduct our spatial analysis in two stages. First, we illustrate the spatial extent, distribution, and intensity of agricultural production across the country and juxtapose that information with some of the local or regional key resources and infrastructure features. Second, we use the information from the first state to disaggregate the region into geographic units in which similar agricultural development problems or opportunities are likely to occur. The goal is to use spatial information attributes that constrain or enable different agricultural development options and develop a single set of domain criteria that would allow us to consistently compare strategic options across the homogenous agro-ecological regions. There are three key attributes that need to be considered to define these homogenous agro-ecological regions agricultural potential, population density, and market access. Agricultural potential of any location is a strong indicator of its advantage in agricultural production, market access and population density determines its comparative advantage. 2.3 Stochastic Production Frontier

Model specification

Before we estimate the parameters of interest we embark on deciding what variables to use for our regressions. There are a number of reasons for this. First, too many variables will draw on the degrees of freedom particularly if the number of observations is not large enough. Second, regressions involving maximum likelihood can become very complicated when there is over-parameterization. The key to reliable regressions is flexibility and parsimony. Taking this into account, we select our variables taking advantage of simple approaches as much as we can.

We start with the production frontier from equation with the full set of variables except those of the inefficiency part. First, using the F-test we test joint significance of variables and drop those that fail the test. This gives a smaller subset of the above variables and their interactions. The second step uses one-step regression of the production function with the reduced set of variables plus the inefficiency equation with full set of the inefficiency variables. Our last regression is to confirm that the variables we dropped in the first step are indeed insignificant. We run a one-step regression using the refined set of variables for the production function and the inefficiency equation.

Panel Regression results

We assume that there is no technical change during the panel period for the data and that the period is homogenous in time within the homogenous agro-ecological regions. Therefore, any changes in this period will be attributed to changes in inputs, technical inefficiency, and idiosyncratic

34

random shocks. We use two approaches in our panel analysis. First we apply the group the data in the homogenous agro-ecological regions and on homogenous farm type categories on homogenous genotypes. The Fixed Effects regression in particular assumes that individual effects are correlated with the exogenous variables. Unique management characteristics or farm-specific conditions have an impact on decisions impacting on yields. The usefulness of panel data is the ability to use this approach to eliminate these unobservable effects.

The estimates of elasticities from these regressions are given in Table 3 below. These elasticities are comparable to those from the pooled regression. The signs on the coefficients are similar and the magnitudes not that different.

2.4 Variability and co-movements:

The main measure of “variability” in statistics is the variance defined as the average squared deviation of observations with respect to the mean value. Another measure of variability is the standard deviation that is calculated as the square root of the variance and it has the advantage of being expressed in the same units as the mean. The coefficient of variation (CV) is a normalized value of the standard deviation calculated as the ratio between the standard deviation and the mean. It has the advantage of being unit-free and it can be interpreted as a sort of “average” deviation or average “shock” in the value as a percentage of the mean.

For example, if the mean price is USD 80/t, and the standard deviation is 20, this can be interpreted as a kind of “average” or “standard” deviation or shock of USD 20/t with respect to the mean value of the price. This number implies a coefficient of variation of USD 20/t over USD 80/t, that is 0.25. This can be interpreted as this price having an “average” deviation or variation of 25% above or below the mean. The main advantage of the CV is that is can be compared across variables that are measured in different units, for instance a CV of prices can be compared with a CV of yields.

Some statistical variables evolve to a certain extent in parallel, so that in general they increase or decrease at the same time. The degree of co-movement between two variables is measured by the covariance, which can also be normalized into a coefficient of correlation. Correlations coefficients can be interpreted as the percentage of the variance of two variables that is due to the co-movement between the two. A coefficient of correlation of 0.80 between the price of crop A and that of crop B can be interpreted as if 80% of the variation of these prices was explained by their movement in the same direction. A negative coefficient of correlation of -0.30 between the price and yield of crop B means that 30% of the variation of prices and yields is explained by their movement in opposite directions. The coefficient of correlation can take values between -1 (perfect co-movement in opposite directions) and 1 (perfect co-movement).

2.5 Cobb-Douglas Production Function The study examines trends in input use and yield over the panel period. Values over time are expressed in constant terms using farm-gate output prices over the five year survey period. This procedure enables us to track characteristics during the study time block of farm output productivity based on changes in physical production per unit of land and labour and effectively purges out the effects of price variations caused largely by exogenous shocks to the sector.

We also use econometric techniques to identify the major determinants of agricultural productivity growth on irrigated farms after controlling for other factors, and to examine the significance of the various productivity determinants. To achieve this aim, we estimate Cobb Douglas production function for the main crops identified in the study. We estimate fixed effects models to control for unobserved time-invariant effects, which would otherwise contribute to parameter bias. In this way, the use of farm panel data can provide a more accurate indication of the factors driving irrigated agricultural productivity in Zimbabwean farms. The Cobb-Douglas production function is widely used for productivity analysis due to its relative simplicity and convenience in specification and interpretation.

This study estimates frontier production and yield response functions for different natural region zones using a unique panel data on Zimbabwean farms. The estimates from this analysis will provide indicators of the marginal productivity impact of to irrigation productivity of the various determinants of productivity for selected crops. The results will help identify the farm characteristics

35

associated with improved crop production on farms in the country. The study derives technical efficiency measures for each farm from the estimated production frontier function, and assesses the potential for increasing the crop yield response of irrigation.

Estimating parameters of a Cobb-Douglas production function

The wide variety of inputs used in the production process can be grouped into three categories:

Natural Resources (R) -- nature made inputs.

Labour (L) -- physical and mental work.

Capital (K) -- human made inputs.

The six factors Cobb-Douglas production function used for maize is:

Q = A * (La) * (Fb) * (Sc) * (Ed) * (Ce) * (Df) * (Tg) * (Xh) = f(L,F,S,E,C,D,T,X).

where:

L = labour,

F = fertiliser,

S = seed

E = electricity

C = chemicals herbicides and insecticides

D = farm size

T = education

X = years of experience

Q = product.

a,b,c,d,e.f,g,h index parameters for proportional increase driven by inputs

I. Decreasing returns to scale: a + b + c + d + e + f + g + h < 1

With decreasing returns to scale, a proportional increase in all inputs will increase output by less than the proportional constant.

II. Increasing returns to scale: a + b + c + d + e + f + g + h > 1

With increasing returns to scale, a proportional increase in all inputs will increase output by more than the proportional constant.

36

3 RESULTS OF ANALYSIS

3.1 Yield Analysis

Figure 4. Mean Crop Yield Variation with Natural Regions in Zimbabwe

The mean yield measured in kg/hectare by homogenous agro-ecological natural regions will show representative yield response of crops to inputs and management. The main inputs are defined in the categories of Natural Resources (R) - nature made inputs, Labour (L) -- physical and mental work and Capital (K) -- human made inputs. The mean yield is an aggregate of all cropping inputs within the homogenous agro-ecological natural region.

Correlation of Yield with Natural Region (Homogenous Climate Zones)

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

Correlation Coefficient (Yield vs Natural Region)

Figure 5. Correlation Between Yield and Natural Regions in Zimbabwe

0

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CROP

MEAN CROP YIELD VARIATION WITH NATURAL REGION

I

II

III

IV

V

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4

CORRELATION COEFFICIENT WITH YIELD KG/HA

WHEAT REGION 5

WHEAT REGION 4

WHEAT REGION 3

WHEAT REGION 2

TOMATOES REGION 5

TOMATOES REGION 4

TOMATOES REGION 3

TOMATOES REGION 2

MAIZE REGION 5

MAIZE REGION 4

MAIZE REGION 3

MAIZE REGION 3

MAIZE REGION 2

MAIZE REGION 1

BEANS REGION 5

BEANS REGION 4

BEANS REGION 3

BEANS CROP REGION 2

WHEAT ALL REGIONS

TOMATOES ALL REGIONS

TOBACCO ALL REGIONS

SOYA BEANS ALL REGIONS

POTATOES ALL REGINS

OTHR ALL REGIONS

ONION ALL REGIONS

MAIZE ALL REGIONS

LEAFY VEGETABLES ALL REGIONS

COTTON ALL REGIONS

GROUND NUTS ALL REGIONS

CARROTS ALL REGIONS

CABBAGE ALL REGIONS

BEANS ALL REGIONS

ALL CROPS ALL REGIONS

DETERMINANTS OF PRODUCTIVITY IN IRRIGATION WITH NATURAL REGION

37

Dummies to describe agro-ecological natural regions start with Natural Region 1 as numeric 1, Natural Region 2a as numeric 2, Natural Region 2b as numeric 2, Natural Region 3 as numeric 4, Natural Region 4 as numeric 5, Natural Region 5 as numeric 6. Figure 5 above clearly shows the natural tendency for most crops to be more productive in Natural Region 1 reducing to Natural Region 5. This is to be expected since Natural Region 1 has more favourable environment for crop production. This includes higher rainfall, and lower evaporation. This observation is not true for crops like beans, groundnuts, leafy vegetables, onion and wheat. These crops exhibit statistically low correlation tendencies to increase yield with the increase in the numeric dummy for natural regions. The magnitude of the positive correlation for identified crops with natural region is much lower than the magnitude for negative correlation with natural regions for the rest of the crops. This trend highlights the natural suitability of crops like cotton and cabbage to hotter drier climates as well as the suitability of crops like groundnuts and leafy vegetables to cooler wetter climates. Most of the crops respond well to the hotter drier regions once water is made available through irrigation.

Figure 6. Maximum Crop Yield in Various Natural Regions of Zimbabwe

Figure 6 is showing the maximum observed yield for each crop in each region. In the absence of

good data on potential yields for the crops under study in the various homogenous agro-ecological

natural regions, the maximum yield observed over the five year study period is taken as approaching

the potential yield. In order to reduce the impact of outlier yield records to the mean it is useful to

use the 95% yield confidence limit. Figure 7 below show the median yield. A similar trend is visible

with the yield reducing with the shift from natural region 1 to natural region 5. The highest agro-

biodiversity is shown for cropping in natural regions 3 and natural regions 4. The least cropping

diversity is seen in natural region 1 which is the country’s highest agro-ecological potential region.

0

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CROPPING

MAXIMUM CROP YIELD IN NATURAL REGION

I

II

III

IV

V

38

Figure 7. Median of Crop Yield in various Natural Regions

Figure 8. Mean Crop Field Yield Comparison with Farm Categories

In figure 8 one can deduct the technical inefficiencies to crop production inherent in the farm categories. The highest diversity on cropping is observed in small gardens and in communal irrigation projects. Yield is highest in gardens dropping down in communal irrigation projects. The generally lower yields are observed for most crops in the A1 category with the exception of tomatoes, cabbages and potatoes with higher yields in A1 category. The general crop yields is higher for A2 farm category than A1 and communal projects with the exception of cabbages and potatoes that also show lower yields than A1. Three main conclusions based on the yield trends are:

0

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ED

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CROPPING

MEDIAN YIELD KG/HA

I

II

III

IV

V

MEAN CROP YIELD VARIATION WITH CATEGORY

0

10,000

20,000

30,000

40,000

50,000

60,000

GARDENS COMMUNAL A1 A2

ME

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YIE

LD

(K

G/H

A)

WHEAT TOMATOES TOBACCO SOYABEAN POTATO

OTHERS ONIONS MAIZE LEAFY VEGETABLES GROUNDNUTS

COTTON CARROTS CABBAGE BEANS

39

1. The smaller fields in gardens makes the family labour management effective. The drop in yield in communal schemes can hence be explained by reducing effectiveness of management on the increasing cropped area. Figure 9 also supports the argument that the farmers management and investment is effectively reduced by increasing cropped areas. This is explained by the negative correlation between yield and cropped areas.

2. Gardens generally procure seeds and seedlings from high yielding varieties. Communal farmers are most likely to use seed from the previous harvests reducing the crop technology productivity.

3. Other possible explanations include the fact that most communal schemes are managed by Government departments. Over dependence on government support can reduce technical efficiency due to slower response to mitigation on determinants of productivity compared to locally driven small garden schemes. This is also supported by the fact that the yield of communal and A1 schemes is comparable with A2 having generally higher yield than A1. The institutional framework of gardens and A2 farmers is similar in so far as the role of farmers and government. This is also true for communal and A1 schemes as they depend on government for important decisions.

The technical possibilities of closing the yield gap depend on the availability of improved crop varieties and on knowledge of the optimum use of water, fertilizer, and control of pests and diseases. We looked separately at the information on availability in these different areas.

Figure 9. Distribution of Crop Yield with Field Size for the Different Natural Regions

ALL CROPS YIELD VARIATION WITH FIELD SIZE

0

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0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 200.00

FIELD SIZE (HA)

YIE

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(K

G/H

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Natural Region IIA Natural Region IIB Natural Region III Natural Region IV

BEAN CROP YIELD VARIATION WITH FIELD SIZE

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Natural Region I Natural Region IIA Natural Region IIB Natural Region III Natural Region IV Natural Region V

40

The response of beans to natural regions is definitive. High yield are observed in small field

particularly in the hot and dry natural regions 4 and 5. The role of irrigation is clear in this instance.

Crop selection for each homogeneous natural region is essential if crop yields are to be optimised.


MAIZE CROP YIELD VARIATION WITH FIELD SIZE

0

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0.00 20.00 40.00 60.00 80.00 100.00 120.00

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POTATOES CROP YIELD VARIATION WITH FIELD SIZE

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0.00 2.00 4.00 6.00 8.00 10.00 12.00

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WHEAT CROP YIELD VARIATION WITH FIELD SIZE

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41


Figure 12. Histogram and Normal Distribution of Crop Yield for all Natural Regions

The two main crops grown by most farmers in all homogenous zones of agro-ecological potential production throughout the country are maize and bean. Further analysis of all the records of yield from both crops into histogram distribution show the following character: Over 85% of the farmers are producing low yields at about 25% of the maximum yield. The maximum yield is about four times higher than the median or mean. This expresses the high potential to improve on productivity if the correct inputs, capital, environment and labour are employed. Characteristics of the yield gap

MAIZE CROP YIELD VARIATION WITH FIELD SIZE

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Garden Community A1 A2 Large Scale ARDA

MAIZE YIELD DISTRIBUTION (KG/HA)

0

10

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467

2,003

3,539

5,075

6,611

8,147

9,683

11,219

12,756

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15,828

17,364

More

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BEANS YIELD DISTRIBUTION (KG/HA)

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200 1,844 3,489 5,133 6,778 8,422 10,067 11,711 13,356 More

YIELD (KG/HA)

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BEAN CROP YIELD VARIATION WITH FIELD SIZE

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0.00 5.00 10.00 15.00 20.00 25.00 30.00 FIELD SIZE (HA)

Garden Community A1 A2 Large Scale ARDA

42

are studied further in detail in the stochastic production frontier. The yield is still visibly high in the smaller field of gardens for maize and communal schemes for beans. Other driving forces pushing for better performances from the small holder gardens and community projects could be from the soil nutritional supplement using manure. Statistics from the presence and high crop and livestock diversity in garden and communal schemes suggest this possibility. This low productivity character is also highlighted in all the other crops as shown in both figure 13 and table 5.xx below. The majority – 80% of the farmers in wheat and potatoes are producing about 30% of the maximum observed yield which in this instance was assumed to be closer to the potential yield. Tomatoes and onions demonstrate a slightly different character in that 80% of the farmers are producing about 25% of the maximum yield observed. The yield gap is higher in horticulture crops compared to the field crops. In all instances the yield gap is significant with high potential for improving productivity.

Figure 13. Histogram and Normal Distribution of Crop Yield for all Natural Regions

Table 1. Statistical Analysis of Yield response to selected crops by natural regions

MEAN YIELD VARIATION PER NATURAL REGION STANDARD DEVIATION IN YIELD PER NAT.

REGION

CROP NATURAL REGIONS NATURAL REGIONS

I II III IV V I II III IV V

BEANS 1,250 3,311 3,233 3,382 4,101 2,196 2,696 4,070 3,448

CABBAGE 14,000 12,143 17,500 15,556 8,060 3,536

CARROTS 26,000 14,604 13,312 7,076

COTTON 1,553 1,040 1,000 950 113

GROUNDNUTS 3,423 5,367 620 6,181

LEAFY VEGETABLES 1,397 16,843 8,338 9,885 781 7,790 7,745 3,114

MAIZE 6,604 4,343 4,857 2,758 3,043 4,775 2,518 3,470 1,919 2,693

ONIONS 5,353 26,707 7,429 33,750 4,029 19,122 9,822 32,377

OTHERS 28,555 5,950 9,605 6,301 2,044 2,092 8,697 9,415

POTATO 23,667 7,423 16,640 5,000 5,333 21,008 7,946 12,031 3,082

SOYABEAN 1,832 2,350 892 3,173

SWEET POTATOES

24,000 1,200

8,775 33,343 30,116

TOBACCO 5,781 38,513 21,779 30,116 7,622 16,058 7,622

TOMATOES 21,779 7,221 10,625 7,221 2,437 1,597 841 2,836

WHEAT YIELD DISTRIBUTION (KG/HA)

0

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YIELD (KG/HA)

FR

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TOMATOES YIELD DISTRIBUTION (KG/HA)

0

5

10

15

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25

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667 15,556 30,444 45,333 60,222 75,111 More

YIELD (KG/HA)

FR

EQ

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.)

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ONIONS YIELD DISTRIBUTION (KG/HA)

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2

4

6

8

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18

600 20450 40300 60150 More

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FR

EQ

UE

NC

Y (

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.)

0%

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POTATO YIELD DISTRIBUTION (KG/HA)

0

2

4

6

8

10

12

14

900 11925 22950 33975 More

YIELD (KG/HA)

FR

EQ

UE

NC

Y (

NO

.)

0%

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43

WHEAT 2,729 3,945 2,558 3,571

Table 2. Statistical Analysis of Yield response to selected crops by natural regions

MAXIMUM CROP YIELD PER NATURAL REGION MEDIAN YIELD VARIATION PER NATURAL REGION

CROP NATURAL REGIONS NATURAL REGIONS

I II III IV V I II III IV V

BEANS 1,250 9,000 12,500 15,000 12,500 2,500 2,500 1,700 3,000

CABBAGE 25,000 27,000 20,000 14,000 10,000 17,500

CARROTS 41,000 19,607 25,500 14,604

COTTON 2,500 1,120 1,000 1,560 1,040 1,000

GROUNDNUTS 4,000 12,500 3,500 2,000

LEAFY VEGETABLES 2,181 22,690 20,000 13,867 1,389 19,840 6,800 10,667

MAIZE 15,000 14,000 18,900 8,400 10,000 5,688 3,839 4,000 2,125 2,000

ONIONS 10,000 48,000 33,613 80,000 3,226 21,120 4,317 23,000

OTHERS 30,000 8,500 29,500 45,000 28,555 5,650 7,000 2,950

POTATO 45,000 22,000 26,000 9,000 5,333 23,000 4,166 25,000 6,000 5,333

SOYABEAN 3,000 6,000 2,000 800

TOBACCO 33,120 90,000 80,000 2,200 23,900 3,000

TOMATOES 80,000 23,333 48,000 23,333 3,000 4,000 3,200 4,000

WHEAT 4,688 7,000 4,000 9,000 3,500 3,263 3,000 2,900

95% CONFIDENCE CROP YIELD VARIATION WITH NATURAL REGION

CROP NATURAL REGIONS

I II III IV V

BEANS 1,216 1,046 1,805 1,393

CABBAGE 139,768 6,195 31,766

CARROTS 13,970 63,575

COTTON 2,360 1,016 0

GROUNDNUTS 1,539 15,354

LEAFY VEGETABLES 1,939 19,352 6,475 3,866

MAIZE 5,011 785 1,042 551 1,111

ONIONS 10,009 47,502 7,026 51,519

OTHERS 18,360 3,329 4,816 3,803

POTATO 52,187 7,349 14,939 3,827 #NUM!

SOYABEAN 567 7,882

TOBACCO 4,860 27,876 16,677

TOMATOES 16,677 4,843 10,203 4,843

WHEAT 6,054 1,073 508 2,977

Results in Table 2 above give a wholesome insight into the variation of the yield characteristics of specified crops and the homogenous agro-ecological zones of the country. The maximum yield recorded for the crops in the five year study period are assumed to be close to the potential yield for each region. For the purpose of the study the potential yield is estimated using the maximum observed yields over the five years, Bearing in mind that there is no data on potential yields for the study areas with such diverse environmental and climatic conditions as well as such diverse seed types and crop types,. This approach is the only meaningful practical estimation of the maximum farm level crop productivity under the combination of both the seed type used and environmental/climatic limitations. The 95% confidence limit level yield observed is more useful in determining the aggregate yield. The presence of extreme outliers has the potential of significantly affecting our mean yield estimates. The high level of confidence on the estimation of yield would reduce the impact of these unique outliers.

44

Table 3. Yield Gap Analysis By Crop And Natural Regions Aggregate

BEANS

NR I BEANS

NR II BEANS

NR III BEANS

NR IV BEANS

NR V CARROT

S NR III CARROT

S NR IV COTTON

NR II COTTON

NR IV COTTON

NR V

Mean Yield kg/ha 1,250 3,311 3,233 3,382 4,101 26,000 14,604 1,553 1,040 1,000 Potential Yield kg/ha 9,000 9,000 12,500 15,000 12,500 41,000 19,607 2,500 1,120 1,000 Aggregate Yield Gap kg/ha 7,750 5,689 9,267 11,618 8,399 15,000 5,004 947 80 0 Count 1 15 28 22 26 6 2 3 2 2 Yield Gap as percentage Potential yield 86 63 74 77 67 37 26 38 7 0

GROUNDNUTS NR

III

GROUNDNUTS NR

IV

CABBAGE NR II

CABBAGE NR III

CABBGE NR IV

LEAFY VEGETABLES NR

II

LEAFY VEGETABLES NR

III

LEAFY VEGETABLES NR

IV

LEAFY VEGETABLES NR

V

Mean Yield kg/ha 3,423 5,367 14,000 12,143 17,500 1,397 16,843 8,338 9,885 Potential Yield kg/ha 4,000 12,500 25,000 27,000 20,000 2,181 22,690 20,000 13,867 Aggregate Yield Gap kg/ha 577 7,133 11,000 14,857 2,500 784 5,847 11,663 3,982 Count 3 3 2 9 2 3 3 8 5 Yield Gap as percentage Potential yield 14 57 44 55 13 36 26 58 29

MAIZE

NR I MAIZE

NR II MAIZE NR III

MAIZE NR IV

MAIZE NR V

ONION NR II

ONION NR III

ONION NR IV

ONION NR V

Mean Yield kg/ha 6,604 4,343 4,857 2,758 3,043 5,353 26,707 7,429 33,750 Potential Yield kg/ha 15,000 14,000 18,900 8,400 10,000 10,000 48,000 33,613 80,000 Aggregate Yield Gap kg/ha 8,396 9,657 14,043 5,642 6,957 4,647 21,293 26,184 46,250 Count 6 42 45 49 25 3 3 10 4 Yield Gap as percentage Potential yield 56 69 74 67 70 46 44 78 58

POTATO

NR I POTATO

NR II POTATO

NR III POTATO

NR IV POTATO

NR V

SOYA BEAN NR

II

SOYA BEAN NR

III

SWEET POTATO

S NR II

SWEET POTATO

S NR III

Mean Yield kg/ha 23,667 7,423 16,640 5,000 5,333 1,832 2,350 24,000 1,200 Potential Yield kg/ha 45,000 22,000 26,000 9,000 5,333 3,000 6,000 24,000 1,200 Aggregate Yield Gap kg/ha 21,333 14,577 9,360 4,000 0 1,168 3,650 0 0 Count 3 7 5 5 1 12 3 1 1 Yield Gap as percentage Potential yield 47 66 36 44 0 39 61 0 0

45

Table 4. Yield Gap Analysis By Crop And Natural Regions Aggregate– Cont….

TOBACC

O NR II TOBACC

O NR III TOBACCO NR IV

Mean Yield kg/ha 5,781 38,513 21,779 Potential Yield kg/ha 33,120 90,000 80,000 Aggregate Yield Gap kg/ha 27,339 51,488 58,221 Count 15 8 15

Yield Gap as percentage Potential yield 83 57 73

TOMATOES NR II

TOMATOES NR III

TOMATOES NR IV

TOMATOES NR V

Mean Yield kg/ha 21,779 7,221 10,625 7,221 Potential Yield kg/ha 80,000 23,333 48,000 23,333 Aggregate Yield Gap kg/ha 58,221 16,112 37,375 16,112 Count 15 12 12 12 Yield Gap as percentage Potential yield 73 69 78 69

WHEAT

NR II WHEAT

NR III WHEAT

NR IV WHEAT

NR V

Mean Yield kg/ha 2,729 3,945 2,558 3,571 Potential Yield kg/ha 4,688 7,000 4,000 9,000 Aggregate Yield Gap kg/ha 1,958 3,055 1,442 5,429 Count 3 11 13 6 Yield Gap as percentage Potential yield 42 44 36 60

YIELD GAP ANALYSIS OF WHEAT PER REGION

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

WHEAT NR II WHEAT NR III WHEAT NR IV WHEAT NR V

CROPPING NATURAL REGION

YIE

LD

KG

/HA

0

10

20

30

40

50

60

70

PE

RC

EN

TA

GE

YIE

LD

GA

P

Mean Yield kg/ha Potential Yield kg/ha

Aggregate Yield Gap kg/ha Count

Yield Gap as percentage Potential yield

46

Figure 14 Yield Gap Analysis for Crops for Aggregate

YIELD GAP ANALYSIS OF LEAFY VEGETABLES PER REGION

0

5,000

10,000

15,000

20,000

25,000

LEAFY VEGETABLES NR II LEAFY VEGETABLES NR III LEAFY VEGETABLES NR IV LEAFY VEGETABLES NR V


YIE

LD

KG

/HA

0

10

20

30

40

50

60

70

PE

RC

EN

TA

GE

YIE

LD

GA

P




YIELD GAP ANALYSIS OF TOMATOES PER REGION

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

TOMATOES NR II TOMATOES NR III TOMATOES NR IV TOMATOES NR V


YIE

LD

KG

/HA

0

10

20

30

40

50

60

70

80

90

PE

RC

EN

TA

GE

YIE

LD

GA

PMean Yield kg/ha Potential Yield kg/ha



47

Figure 15 Yield Gap Analysis for Crops for Aggregate

YIELD GAP ANALYSIS OF MAIZE PER REGION

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

MAIZE NR I MAIZE NR II MAIZE NR III MAIZE NR IV MAIZE NR V


YIE

LD

KG

/HA

0

10

20

30

40

50

60

70

80

PE

RC

EN

TA

GE

YIE

LD

GA

P




YIELD GAP ANALYSIS OF BEANS PER REGION

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

BEANS NR I BEANS NR II BEANS NR III BEANS NR IV BEANS NR V


YIE

LD

KG

/HA

0

10

20

30

40

50

60

70

80

90

100

PE

RC

EN

TA

GE

YIE

LD

GA

PMean Yield kg/ha Potential Yield kg/ha



48

Figure 16. Yield Gap Summary Aggregate by Crop and Region

Table 5. Summary Statistics for Yield Gap on Crops at Farm Level

BEANS Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha

Potential Yield (Max)

kg/ha

Yield Gap kg/ha

Yield Gap as

Percentage Potential

Yield

Mean 3,468.2 3,505.2 12,489.1 8,984.0 71.3

Standard Error 324.7 45.4 199.6 384.4 2.6

Median 2,500.0 3,381.8 12,500.0 10,000.0 80.0

Mode 2,000.0 3,233.0 12,500.0 10,000.0 80.0

Standard Deviation

3,164.5 435.9 1,914.3 3,687.1 25.3

Kurtosis 2.8 6.4 (0.3) 0.1 1.6

Skewness 1.8 (0.9) (0.5) (0.7) (1.5)

Range 14,800.0 2,851.1 6,000.0 14,800.0 98.7

Minimum 200.0 1,250.0 9,000.0 - -

Maximum 15,000.0 4,101.1 15,000.0 14,800.0 98.7

Sum 329,475.0 322,475.0 1,149,000.0 826,525.0 6,561.0

Count 95.0 92.0 92.0 92.0 92.0

Confidence Level(95.0%)

644.7 90.3 396.4 763.6 5.2

CABBAGE Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 12,274.2 12,298.1 26,833.3 14,559.2 54.5

Standard Error 2,257.9 154.7 166.7 2,201.1 8.3

Median 12,500.0 12,143.3 27,000.0 14,500.0 53.7

Mode 10,000.0 12,143.3 27,000.0 17,000.0 63.0

Standard Deviation

7,821.6 536.0 577.4 7,624.9 28.9

Kurtosis (0.4) 12.0 12.0 (0.2) (0.4)

Skewness 0.1 3.5 (3.5) (0.2) (0.1)

Range 25,800.0 1,856.7 2,000.0 25,800.0 95.6

YIELD GAP ANALYSIS SELETED CROPS

1

10

100

1,000

10,000

100,000

BE

AN

S N

R I

BE

AN

S N

R II

BE

AN

S N

R II

I

BE

AN

S N

R IV

BE

AN

S N

R V

CA

RR

OTS

NR

III

CA

RR

OTS

NR

IV

CO

TTO

N N

R II

CO

TTO

N N

R IV

CO

TTO

N N

R V

GR

OU

ND

NU

TS N

R II

I

GR

OU

ND

NU

TS N

R IV

CA

BB

AG

E N

R II

CA

BB

AG

E N

R II

I

CA

BB

GE

NR

IV

LEA

FY V

EG

ETA

BLE

S N

R II

LEA

FY V

EG

ETA

BLE

S N

R II

I

LEA

FY V

EG

ETA

BLE

S N

R

LEA

FY V

EG

ETA

BLE

S N

R V

MA

IZE

NR

I

MA

IZE

NR

II

MA

IZE

NR

III

MA

IZE

NR

IV

MA

IZE

NR

V

ON

ION

NR

II

ON

ION

NR

III

ON

ION

NR

IV

ON

ION

NR

V

PO

TATO

NR

I

PO

TATO

NR

II

PO

TATO

NR

III

PO

TATO

NR

IV

PO

TATO

NR

V

SO

YA

BE

AN

NR

II

SO

YA

BE

AN

NR

III

SW

EE

T P

OTA

TOS

NR

II

SW

EE

T P

OTA

TOS

NR

III

TOB

AC

CO

NR

II

TOB

AC

CO

NR

III

TOB

AC

CO

NR

IV

TOM

ATO

ES

NR

II

TOM

ATO

ES

NR

III

TOM

ATO

ES

NR

IV

TOM

ATO

ES

NR

V

WH

EA

T N

R II

WH

EA

T N

R II

I

WH

EA

T N

R IV

WH

EA

T N

R V

CROPPING REGION

YIE

LD K

G/H

AMean Yieldkg/ha

PotentialYield kg/ha

AggregateYield Gap kg/ha

Count

Yield GapaspercentagePotentialyield

49

Minimum 1,200.0 12,143.3 25,000.0 - -

Maximum 27,000.0 14,000.0 27,000.0 25,800.0 95.6

Sum 147,290.0 147,576.7 322,000.0 174,710.0 653.6

Count 12.0 12.0 12.0 12.0 12.0


4,969.6 340.5 366.8 4,844.6 18.3

CARROTS Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 41,743.9 22,743.9 34,887.7 (6,856.1) (12.9)

Standard Error 20,805.1 2,101.8 3,945.5 19,827.3 49.0

Median 21,000.0 26,000.0 41,000.0 10,007.0 26.8

Mode #N/A 26,000.0 41,000.0 #N/A #N/A

Standard Deviation

55,045.1 5,560.9 10,438.7 52,458.1 129.8

Kurtosis 6.1 (0.8) (0.8) 6.0 6.0

Skewness 2.4 (1.2) (1.2) (2.4) (2.4)

Range 156,000.0 11,396.5 21,393.0 156,000.0 380.5

Minimum 8,000.0 14,603.5 19,607.0 (123,000.0) (300.0)

Maximum 164,000.0 26,000.0 41,000.0 33,000.0 80.5

Sum 292,207.0 159,207.0 244,214.0 (47,993.0) (90.4)

Count 7.0 7.0 7.0 7.0 7.0


50,908.2 5,143.0 9,654.2 48,515.6 120.0

COTTON Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 1,356.7 1,197.8 1,540.0 183.3 8.6

Standard Error 246.1 112.7 304.4 153.6 6.2

Median 1,060.0 1,040.0 1,120.0 - -

Mode 1,000.0 1,553.3 2,500.0 - -

Standard Deviation

602.7 276.0 745.5 376.2 15.3

Kurtosis 3.1 (1.9) (1.9) 5.4 3.0

Skewness 1.8 0.9 0.9 2.3 1.8

Range 1,540.0 553.3 1,500.0 940.0 37.6

Minimum 960.0 1,000.0 1,000.0 - -

Maximum 2,500.0 1,553.3 2,500.0 940.0 37.6

Sum 8,140.0 7,186.7 9,240.0 1,100.0 51.9

Count 6.0 6.0 6.0 6.0 6.0


632.5 289.6 782.4 394.8 16.0

GROUND NUTS

Yield kg/ha Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 4,720.0 4,589.1 9,100.0 4,380.0 36.7

Standard Error 1,995.8 476.2 2,082.1 2,582.5 20.1

Median 3,500.0 5,366.7 12,500.0 500.0 12.5

Mode #N/A 5,366.7 12,500.0 - -

Standard Deviation

4,462.8 1,064.8 4,655.6 5,774.7 44.9

Kurtosis 4.0 (3.3) (3.3) (3.3) (3.2)

Skewness 2.0 (0.6) (0.6) 0.6 0.6

Range 10,900.0 1,944.0 8,500.0 10,900.0 87.2

50

Minimum 1,600.0 3,422.7 4,000.0 - -

Maximum 12,500.0 5,366.7 12,500.0 10,900.0 87.2

Sum 23,600.0 22,945.4 45,500.0 21,900.0 183.7

Count 5.0 5.0 5.0 5.0 5.0


5,541.4 1,322.1 5,780.7 7,170.2 55.8

LEAFY VEGETABLES


Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 9,456.8 9,413.7 16,764.9 7,308.0 40.2

Standard Error 1,688.7 997.6 1,464.7 1,704.7 8.2

Median 8,000.0 8,337.5 20,000.0 4,088.8 36.1

Mode 8,000.0 8,337.5 20,000.0 - -

Standard Deviation

7,164.7 4,232.4 6,214.0 7,232.6 34.6

Kurtosis (0.8) 0.9 1.8 (1.4) (1.3)

Skewness 0.6 0.2 (1.5) 0.5 0.2

Range 22,140.5 15,446.9 20,509.5 19,450.0 97.3

Minimum 550.0 1,396.6 2,181.0 - -

Maximum 22,690.5 16,843.5 22,690.5 19,450.0 97.3

Sum 170,223.2 169,446.5 301,767.4 131,544.3 723.7

Count 18.0 18.0 18.0 18.0 18.0


3,562.9 2,104.7 3,090.2 3,596.7 17.2

MAIZE Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 3,806.6 3,887.0 13,103.0 9,296.4 70.1

Standard Error 216.8 78.4 324.7 330.0 1.6

Median 3,300.0 4,343.1 14,000.0 8,900.0 76.2

Mode 1,500.0 2,758.3 8,400.0 8,000.0 64.3

Standard Deviation

2,792.7 1,009.8 4,184.0 4,251.7 21.0

Kurtosis 5.1 (0.6) (1.5) (0.5) 1.7

Skewness 1.7 0.4 0.3 0.2 (1.3)

Range 18,433.3 3,845.9 10,500.0 18,066.7 95.6

Minimum 466.7 2,758.3 8,400.0 - -

Maximum 18,900.0 6,604.2 18,900.0 18,066.7 95.6

Sum 631,900.1 645,245.9 2,175,100.0 1,543,199.9 11,639.6

Count 166.0 166.0 166.0 166.0 166.0


428.0 154.8 641.2 651.6 3.2

ONION Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 15,067.0 17,505.4 46,672.9 31,605.9 66.5

Standard Error 4,208.7 2,737.8 5,132.7 5,171.0 7.6

Median 9,000.0 7,428.7 33,613.0 30,280.0 77.1

Mode #N/A 7,428.7 33,613.0 - -

Standard Deviation

19,286.5 12,546.1 23,521.1 23,696.6 35.0

Kurtosis 5.9 (1.9) (1.0) (0.5) 0.2

Skewness 2.3 0.4 0.4 0.4 (1.3)

Range 79,400.0 28,397.1 70,000.0 74,666.7 98.2

51

Minimum 600.0 5,352.9 10,000.0 - -

Maximum 80,000.0 33,750.0 80,000.0 74,666.7 98.2

Sum 316,406.7 367,612.9 980,130.0 663,723.3 1,397.3

Count 21.0 21.0 21.0 21.0 21.0


8,779.1 5,710.9 10,706.7 10,786.6 15.9

POTATOES Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 10,955.7 10,920.6 22,052.6 11,096.9 50.5

Standard Error 2,780.7 1,480.3 2,407.9 2,663.2 9.1

Median 6,000.0 7,422.7 22,000.0 7,000.0 66.7

Mode 6,000.0 7,422.7 22,000.0 - -

Standard Deviation

12,120.8 6,452.4 10,495.9 11,608.7 39.8

Kurtosis 2.0 (0.6) 0.9 1.0 (1.8)

Skewness 1.5 0.9 0.7 1.1 (0.2)

Range 44,100.0 18,666.7 36,000.0 42,000.0 95.9

Minimum 900.0 5,000.0 9,000.0 - -

Maximum 45,000.0 23,666.7 45,000.0 42,000.0 95.9

Sum 208,159.0 207,492.3 419,000.0 210,841.0 959.4

Count 19.0 19.0 19.0 19.0 19.0


5,842.0 3,109.9 5,058.9 5,595.2 19.2

SOYA BEANS Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 1,579.3 1,911.5 3,461.5 1,882.2 48.7

Standard Error 266.1 54.0 312.5 499.5 9.3

Median 917.0 1,831.8 3,000.0 2,083.0 69.4

Mode 917.0 1,831.8 3,000.0 2,083.0 69.4

Standard Deviation

959.4 194.6 1,126.6 1,801.1 33.5

Sample Variance

920,376.0 37,874.7 1,269,230.8 3,243,929.8 1,125.4

Kurtosis (1.4) 3.2 3.2 1.1 (1.4)

Skewness 0.4 2.2 2.2 1.2 (0.3)

Range 2,750.0 518.2 3,000.0 5,750.0 95.8

Minimum 250.0 1,831.8 3,000.0 - -

Maximum 3,000.0 2,350.0 6,000.0 5,750.0 95.8

Sum 20,531.2 24,849.4 45,000.0 24,468.8 633.1

Count 13.0 13.0 13.0 13.0 13.0


579.7 117.6 680.8 1,088.4 20.3

TOBACCO Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 5,363.1 5,781.3 33,120.0 27,756.9 83.8

Standard Error 2,465.8 0.0 - 2,465.8 7.4

Median 2,200.0 5,781.3 33,120.0 30,920.0 93.4

Mode 1,000.0 5,781.3 33,120.0 32,120.0 97.0

Standard Deviation

8,890.7 0.0 - 8,890.7 26.8

Sample 79,045,056.4 0.0 - 79,045,056.4 720.6

52

Variance

Kurtosis 9.2 (2.4) #DIV/0! 9.2 9.2

Skewness 2.9 (1.1) #DIV/0! (2.9) (2.9)

Range 33,120.0 - - 33,120.0 100.0

Minimum - 5,781.3 33,120.0 - -

Maximum 33,120.0 5,781.3 33,120.0 33,120.0 100.0

Sum 69,720.0 75,157.3 430,560.0 360,840.0 1,089.5

Count 13.0 13.0 13.0 13.0 13.0


5,372.6 0.0 - 5,372.6 16.2

TOMATOES Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 18,698.5 10,393.4 63,866.7 45,168.1 72.9

Standard Error 3,835.4 768.6 2,455.6 3,611.2 5.0

Median 6,000.0 7,221.2 48,000.0 46,000.0 91.3

Mode 1,600.0 7,221.2 48,000.0 - -

Standard Deviation

25,728.5 5,155.9 16,472.6 24,224.8 33.6

Sample Variance

661,956,618.9 26,582,823.9 271,345,454.5 ######### 1,128.1

Kurtosis 1.4 1.2 (2.0) (0.5) 0.3

Skewness 1.6 1.6 0.1 (0.3) (1.3)

Range 89,333.3 14,557.4 42,000.0 79,000.0 98.8

Minimum 666.7 7,221.2 48,000.0 - -

Maximum 90,000.0 21,778.7 90,000.0 79,000.0 98.8

Sum 841,434.7 467,702.4 2,874,000.0 2,032,565.3 3,282.2

Count 45.0 45.0 45.0 45.0 45.0


7,729.7 1,549.0 4,948.9 7,277.9 10.1

WHEAT Yield kg/ha

Corrected Adjusted

Mean Yield kg/ha


kg/ha

Yield Gap kg/ha

Yield Gap as


Yield

Mean 3,211.1 3,235.2 6,011.7 2,800.6 44.1

Standard Error 315.9 113.1 348.4 391.2 4.8

Median 3,000.0 3,570.8 7,000.0 2,624.5 51.7

Mode 3,000.0 2,557.7 4,000.0 - -

Standard Deviation

1,786.7 639.9 1,971.1 2,212.8 27.2

Sample Variance

3,192,473.0 409,442.3 3,885,190.9 4,896,353.0 741.0

Kurtosis 3.3 (2.0) (1.5) (0.3) (0.6)

Skewness 1.4 (0.0) 0.3 0.6 (0.2)

Range 9,000.0 1,387.0 5,000.0 8,375.0 100.0

Minimum - 2,557.7 4,000.0 - -

Maximum 9,000.0 3,944.7 9,000.0 8,375.0 100.0

Sum 102,754.3 103,525.1 192,375.0 89,620.7 1,410.8

Count 32.0 32.0 32.0 32.0 32.0


644.2 230.7 710.7 797.8 9.8

53

Figure 17. Yield Gap Variation with Crop at Farm Level

YIELD GAP SUMMARY FOR BEANS

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA

P

Yield kg/ha Corrected Adjusted Mean Yield kg/ha Potential Yield (Max) kg/ha

Farm Yield Gap kg/ha Aggregate Yield Gap kg/ha Yield Gap as Percentage Potential Yield

YIELD GAP SUMMARY FOR CABBAGES

0

5,000

10,000

15,000

20,000

25,000

30,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA

PYield kg/ha Corrected Adjusted Mean Yield kg/ha Potential Yield (Max) kg/ha


YIELD GAP SUMMARY FOR CARROTS

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

PE

RC

EN

TA

GE

YIE

LD

GA

P



54


YIELD GAP SUMMARY FOR LEAFY VEGETABLES

0

5,000

10,000

15,000

20,000

25,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA

P



YIELD GAP SUMMARY FOR ONION

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA



YIELD GAP SUMMARY FOR MAIZE

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA

P



55


YIELD GAP SUMMARY FOR POTATOES

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA

P



YIELD GAP SUMMARY FOR TOMATOES

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA



YIELD GAP SUMMARY FOR WHEAT

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

NATURAL REGION

YIE

LD

KG

/HA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

PE

RC

EN

TA

GE

YIE

LD

GA

P



56

Table 5 reports calculated average yield gaps based on aggregate assessment of potential yields derived for each region from the maximum observed yields in the different domains and production systems. The standard deviations capture the variation in yields and yield gaps in distinctive farming systems. Evidently, the potential to experience a two to threefold yield increase among some of the farming enterprises is possible if more farmers can access and efficiently use the available stock of knowledge and technologies. According to our estimates of yield gaps, we conclude that there is a vast potential to expand agricultural production in Zimbabwe. The yield gap for most crops could be reduced to obtain yields closer to the potential achievable yield by appropriately using improved crop varieties, the recommended levels of fertilizers, and adequate management of nutrients, water, and pests and diseases. It is equally important to verify if this knowledge is really available and if there is historical evidence of technology development and availability of this technology in the country’s homogenous agro-ecological regions. If this is the case, why have these technologies not been adopted?

The difference of variance of income is assumed to be the contribution of crop diversification in reducing income risk. Similarly, the difference of variance of income and the sum of observed variance terms is assumed to be the contribution of price-yield correlation in reducing the income risk. Both farmer characteristics and system-wide constraints explain these various yield gaps and suggest how they may be closed. In general, yield gaps at the lower end are explained more by farmers’ access to information and technical skills, while higher order yield gaps reflect opportunities for research as well as broader policy and institutional constraints.

Figure 20. Variance of Crop Yield with natural regions

0.E+00

2.E+08

4.E+08

6.E+08

8.E+08

1.E+09

1.E+09

VA

RIA

NC

E I

N Y

IEL

D

BE

AN

S

CA

BB

AG

E

CA

RR

OT

S

CO

TT

ON

GR

OU

ND

NU

TS

LE

AF

Y V

EG

ET

AB

LE

S

MA

IZE

ON

ION

S

OT

HE

RS

PO

TA

TO

SO

YA

BE

AN

TO

BA

CC

O

TO

MA

TO

ES

WH

EA

T

I

III

V

CROPPING

NATU

RAL...

VARIANCE CROP YIELD VARIATION WITH NATURAL REGION

I

II

III

IV

V

57

Figure 21. Number of Farmers in study by Natural Region

Figure 22. Number of Farmers in study by Farm Category

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

NU

MB

ER

OF

FA

RM

ER

S

BEA

NS

CABB

AGE

CARROTS

COTTO

N

GROUNDNUTS

LEAFY

VEG

ETAB

LES

MAIZ

E

ONIO

NS

OTH

ERS

POTA

TO

SOYAB

EAN

TOBA

CCO

TOM

ATO

ES

WHEA

T

I

III

V

NUMBER OF FARMERS IN NATURAL REGION

I

II

III

IV

V

0.0

20.0

40.0

60.0

80.0

100.0

120.0

NU

MB

ER

OF

FA

RM

ER

S

BEA

NS

CABBA

GE

CARROTS

CO

TTO

N

GROUNDNUTS

LEAFY V

EGETA

BLES

MAIZ

E

ONIO

NS

OTH

ERS

POTATO

SOYAB

EAN

TOBAC

CO

TOM

ATO

ES

WHEA

T

Garden

A1

NUMBER OF FARMERS WITH FARM CATEGORY

Garden

Communal

A1

A2

58

Table 6. Statistical Description of Yield Variation by Farm Category

WHEAT

COMMUNAL

WHEAT A2 TOMATOS

GARDEN

TOMATOS COMMUNA

L

TOMATOS A1

TOMATOS A2

TOBACCO COMMUNA

L

TOBACCO A1

TOBACCO A2

SOYABEAN

COMMUNAL

SOYABEAN A1

SOYABEAN A2

Mean 3,110 3,838 19,938 14,391 24,950 54,000 8,000 950 7,692 525 2,524 2,188

Standard Error 307 1,131 9,263 4,077 11,687 36,000 3,606 132 3,245 275 828 274

Median 3,000 4,000 7,750 4,500 13,300 54,000 6,000 900 2,800 525 917 2,222

Mode 3,000 #N/A #N/A 2,000 1,600 #N/A #N/A #N/A 1,000 #N/A 917 2,000

Standard Deviation 1,624 2,529 26,199 22,331 28,626 50,912 6,245 265 10,263 389 2,190 725

Sample Variance 2.64E+

06 6.40E+

06 6.86E+

08 4.99E+

08 8.19E+

08 2.59E+

09 3.90E+

07 7.00E+

04 1.05E+

08 1.51E+

05 4.79E+

06 5.26E+

05

Kurtosis 7 2 5 4 -2 0 4 -1 2

Skewness 2 -1 2 2 1 1 1 2 1 -1

Range 8,375 7,000 77,600 79,333 58,500 72,000 12,000 600 33,120 550 5,083 2,209

Minimum 625 0 2,400 667 1,500 18,000 3,000 700 0 250 917 791

Maximum 9,000 7,000 80,000 80,000 60,000 90,000 15,000 1,300 33,120 800 6,000 3,000

Sum 87,067 19,188 159,50

0 431,73

5 149,70

0 108,00

0 24,000 3,800 76,920 1,050 17,668 15,313

Count 28 5 8 30 6 2 3 4 10 2 7 7

Largest(1) 9,000 7,000 80,000 80,000 60,000 90,000 15,000 1,300 33,120 800 6,000 3,000

Smallest(1) 625 0 2,400 667 1,500 18,000 3,000 700 0 250 917 791

Confidence Level(95.0%) 630 3,141 21,903 8,339 30,042 457,42

3 15,513 421 7,342 3,494 2,025 671

POTATO

COMMUNAL

POTATO A1

POTATO A2

OTHERS

GARDEN

OTHERS

COMMUNAL

OTHERS A1

OTHERS A2

ONIONS GARDE

N

ONION COMMU

NAL

MAIZE GARDE

N

MAIZE COMMU

NAL

MAIZE A1

MAIZE A2

Mean 6,740 18,473 13,600 18,057 7,298 4,143 8,371 23,418 9,483 3,662 3,514 4,177 5,211

Standard Error 2,279 10,026 4,067 7,198 1,722 998 1,665 9,054 3,571 897 270 617 608

Median 5,667 13,000 15,000 15,000 2,950 5,000 7,000 12,000 5,333 3,667 2,900 3,500 5,000

Mode 6,000 #N/A 25,000 #N/A 3,000 1,500 #N/A #N/A #N/A 5,000 1,000 1,500 6,000

Standard Deviation 7,206 20,053 10,760 16,096 9,111 2,641 4,405 25,609 12,874 2,692 2,752 3,208 3,161


07 4.02E+

08 1.16E+

08 2.59E+

08 8.30E+

07 6.98E+

06 1.94E+

07 6.56E+

08 1.66E+

08 7.25E+

06 7.57E+

06 1.03E+

07 9.99E+

06

Kurtosis 7 -1 -2 3 1 -2 2 4 7 0 9 4 1

Skewness 2 1 0 2 1 0 1 2 3 0 2 2 1

Range 25,100 42,107 23,800 41,667 29,559 6,000 12,800 77,167 47,400 8,105 18,400 14,500 13,388

Minimum 900 2,893 1,200 3,333 442 1,500 4,000 2,833 600 467 500 500 612

Maximum 26,000 45,000 25,000 45,000 30,000 7,500 16,800 80,000 48,000 8,571 18,900 15,000 14,000

Sum 67,399 73,893 95,200 90,285 204,35 29,000 58,600 187,34 123,27 32,960 365,41 112,78 140,69

59

7 6 8 0 9 1

Count 10 4 7 5 28 7 7 8 13 9 104 27 27

Largest(1) 26,000 45,000 25,000 45,000 30,000 7,500 16,800 80,000 48,000 8,571 18,900 15,000 14,000

Smallest(1) 900 2,893 1,200 3,333 442 1,500 4,000 2,833 600 467 500 500 612

Confidence Level(95.0%) 5,155 31,908 9,952 19,985 3,533 2,443 4,074 21,410 7,779 2,069 535 1,269 1,250

LEAFY VEGETABLES

GARDEN

LEAFY VEGETABLES COOMUNAL

GROUNDNUT

S COMMUNAL

COTTON

COMMUNAL

COTTON

A1/A2

CARROTS

GARDEN

CARROTS

COMMUNAL

CABBAGES

COMMUNAL

CABBAGES

A1

CABBAGES

A2

BEANS GARDE

N

BEANS COMMUNAL

BEANS A1

BEANS A2

Mean 7,657 9,962 4,395 1,020 1,553 14,604 23,200 10,327 23,500 13,250 8,333 3,381 1,767 3,589

Standard Error 1,534 2,680 1,662 35 548 5,004 5,704 2,541 3,500 4,626 3,005 351 385 1,109

Median 8,445 8,000 3,134 1,000 1,560 14,604 21,000 10,000 23,500 12,500 10,000 2,400 1,750 2,500

Mode 10,667 550 #N/A 1,000 #N/A #N/A #N/A 10,000 #N/A #N/A #N/A 1,000 2,800 #N/A

Standard Deviation 4,340 8,889 4,070 69 950 7,076 12,755 6,724 4,950 9,251 5,204 3,099 944 2,934


07 7.90E+

07 1.66E+

07 4.80E+

03 9.03E+

05 5.01E+

07 1.63E+

08 4.52E+

07 2.45E+

07 8.56E+

07 2.71E+

07 9.61E+

06 8.91E+

05 8.61E+

06

Kurtosis -1 -2 5 3 -1 -1 0 4 -2 1

Skewness 0 0 2 2 0 0 0 0 -1 2 0 1

Range 12,478 22,140 10,900 160 1,900 10,007 33,000 16,290 7,000 22,000 10,000 14,800 2,300 8,250

Minimum 1,389 550 1,600 960 600 9,600 8,000 1,200 20,000 3,000 2,500 200 500 750

Maximum 13,867 22,690 12,500 1,120 2,500 19,607 41,000 17,490 27,000 25,000 12,500 15,000 2,800 9,000

Sum 61,259 109,58

4 26,368 4,080 4,660 29,207

116,000

72,290 47,000 53,000 25,000 263,75

4 10,600 25,121

Count 8 11 6 4 3 2 5 7 2 4 3 78 6 7

Largest(1) 13,867 22,690 12,500 1,120 2,500 19,607 41,000 17,490 27,000 25,000 12,500 15,000 2,800 9,000

Smallest(1) 1,389 550 1,600 960 600 9,600 8,000 1,200 20,000 3,000 2,500 200 500 750

Confidence Level(95.0%) 3,628 5,972 4,272 110 2,360 63,575 15,838 6,219 44,472 14,721 12,928 699 990 2,713

Table 7. Statistical Description of Yield Variation by Natural Region Category

BEANS NR I BEANS NR II BEANS NR

III BEANS NR

IV BEANS NR

V CARROTS NR

III CARROTS NR

IV COTTON NR II

COTTON NR IV

COTTON NR V

Mean 1,250 3,311 3,233 3,382 4,101 26,000 14,604 1,553 1,040 1,000 Standard Error 567 510 868 676 5,434 5,004 548 80 0 Median 2,500 2,500 1,700 3,000 25,500 14,604 1,560 1,040 1,000 Mode 2,000 2,500 1,000 2,000 #N/A #N/A #N/A #N/A 1,000 Standard Deviation 2,196 2,696 4,070 3,448 13,312 7,076 950 113 0 Sample 5.E+06 7.E+06 2.E+07 1.E+07 2.E+08 5.E+07 9.E+05 1.E+04 0.E+00

60

Variance Kurtosis 2 5 3 1 -2 Skewness 1 2 2 1 0 0 Range 8,250 11,700 14,800 11,625 33,000 10,007 1,900 160 0 Minimum 1,250 750 800 200 875 8,000 9,600 600 960 1,000 Maximum 1,250 9,000 12,500 15,000 12,500 41,000 19,607 2,500 1,120 1,000 Sum 1,250 49,671 90,524 74,400 106,630 156,000 29,207 4,660 2,080 2,000 Count 1 15 28 22 26 6 2 3 2 2 Largest(1) 1,250 9,000 12,500 15,000 12,500 41,000 19,607 2,500 1,120 1,000 Smallest(1) 1,250 750 800 200 875 8,000 9,600 600 960 1,000 Confidence Level(95.0%) 1,216 1,046 1,805 1,393 13,970 63,575 2,360 1,016 0

GROUNDNUTS

NR III GROUNDNUTS

NR IV CABBAGE

NR II CABBAGE

NR III CABBGE

NR IV

LEAFY VEGETABLES

NR II

LEAFY VEGETABLES

NR III

LEAFY VEGETABLES

NR IV

LEAFY VEGETABLES

NR V

Mean 3,423 5,367 14,000 12,143 17,500 1,397 16,843 8,338 9,885 Standard Error 358 3,569 11,000 2,687 2,500 451 4,498 2,738 1,393 Median 3,500 2,000 14,000 10,000 17,500 1,389 19,840 6,800 10,667 Mode #N/A #N/A #N/A 10,000 #N/A #N/A #N/A 550 10,667 Standard Deviation 620 6,181 15,556 8,060 3,536 781 7,790 7,745 3,114 Sample Variance 4.E+05 4.E+07 2.E+08 6.E+07 1.E+07 6.E+05 6.E+07 6.E+07 1.E+07 Kurtosis 0 -1 1 Skewness -1 2 0 0 -1 1 0 Range 1,232 10,900 22,000 25,800 5,000 1,561 14,690 19,450 8,533 Minimum 2,768 1,600 3,000 1,200 15,000 620 8,000 550 5,333 Maximum 4,000 12,500 25,000 27,000 20,000 2,181 22,690 20,000 13,867 Sum 10,268 16,100 28,000 109,290 35,000 4,190 50,530 66,700 49,423 Count 3 3 2 9 2 3 3 8 5 Largest(1) 4,000 12,500 25,000 27,000 20,000 2,181 22,690 20,000 13,867 Smallest(1) 2,768 1,600 3,000 1,200 15,000 620 8,000 550 5,333 Confidence Level(95.0%) 1,539 15,354 139,768 6,195 31,766 1,939 19,352 6,475 3,866

MAIZE NR I MAIZE NR II MAIZE NR

III MAIZE NR

IV MAIZE NR V ONION NR II ONION NR III ONION NR IV ONION NR V

Mean 6,604 4,343 4,857 2,758 3,043 5,353 26,707 7,429 33,750 Standard Error 1,949 389 517 274 539 2,326 11,040 3,106 16,188 Median 5,688 3,839 4,000 2,125 2,000 3,226 21,120 4,317 23,000 Mode #N/A 3,500 1,500 1,000 2,000 #N/A #N/A #N/A #N/A Standard Deviation 4,775 2,518 3,470 1,919 2,693 4,029 19,122 9,822 32,377 Sample Variance 2.E+07 6.E+06 1.E+07 4.E+06 7.E+06 2.E+07 4.E+08 1.E+08 1.E+09 Kurtosis 2 4 5 0 1 7 2 Skewness 1 1 2 1 1 2 1 2 1 Range 13,500 13,250 18,067 7,933 9,500 7,167 37,000 33,013 71,000

61

Minimum 1,500 750 833 467 500 2,833 11,000 600 9,000 Maximum 15,000 14,000 18,900 8,400 10,000 10,000 48,000 33,613 80,000 Sum 39,625 182,412 218,582 135,157 76,075 16,059 80,120 74,287 135,000 Count 6 42 45 49 25 3 3 10 4 Largest(1) 15,000 14,000 18,900 8,400 10,000 10,000 48,000 33,613 80,000 Smallest(1) 1,500 750 833 467 500 2,833 11,000 600 9,000 Confidence Level(95.0%) 5,011 785 1,042 551 1,111 10,009 47,502 7,026 51,519

POTATO NR I POTATO NR II POTATO

NR III POTATO NR

IV POTATO NR

V SOYA BEAN

NR II SOYA BEAN

NR III

SWEET POTATOS NR

II

SWEET POTATOS NR

III

Mean 23,667 7,423 16,640 5,000 5,333 1,832 2,350 24,000 1,200 Standard Error 12,129 3,003 5,380 1,378 0 258 1,832 0 0 Median 23,000 4,166 25,000 6,000 5,333 2,000 800 24,000 1,200 Mode #N/A #N/A 25,000 6,000 #N/A 917 #N/A #N/A #N/A Standard Deviation 21,008 7,946 12,031 3,082 892 3,173 Sample Variance 4.E+08 6.E+07 1.E+08 1.E+07 8.E+05 1.E+07 Kurtosis 1 -3 -1 -2 Skewness 0 1 -1 0 0 2 Range 42,000 21,100 24,800 8,000 0 2,209 5,750 0 0 Minimum 3,000 900 1,200 1,000 5,333 791 250 24,000 1,200 Maximum 45,000 22,000 26,000 9,000 5,333 3,000 6,000 24,000 1,200 Sum 71,000 51,959 83,200 25,000 5,333 21,981 7,050 24,000 1,200 Count 3 7 5 5 1 12 3 1 1 Largest(1) 45,000 22,000 26,000 9,000 5,333 3,000 6,000 24,000 1,200 Smallest(1) 3,000 900 1,200 1,000 5,333 791 250 24,000 1,200 Confidence Level(95.0%) 52,187 7,349 14,939 3,827 #NUM! 567 7,882 #NUM! #NUM!

TOBACCO NR

II TOBACCO NR

III TOBACCO

NR IV TOMATOES

NR II TOMATOES

NR III TOMATOES

NR IV TOMATOES

NR V WHEAT NR II WHEAT NR III

WHEAT NR IV

WHEAT NR V

Mean 5,781 38,513 21,779 21,779 7,221 10,625 7,221 2,729 3,945 2,558 3,571 Standard Error 2,266 11,789 7,776 7,776 2,200 4,636 2,200 1,407 482 233 1,158 Median 2,200 23,900 3,000 3,000 4,000 3,200 4,000 3,500 3,263 3,000 2,900 Mode 1,000 #N/A 1,600 1,600 2,000 2,000 2,000 #N/A 7,000 3,000 #N/A Standard Deviation 8,775 33,343 30,116 30,116 7,622 16,058 7,622 2,437 1,597 841 2,836 Sample Variance 8.E+07 1.E+09 9.E+08 9.E+08 6.E+07 3.E+08 6.E+07 6.E+06 3.E+06 7.E+05 8.E+06 Kurtosis 7 -1 0 0 0 2 0 1 -1 4 Skewness 3 1 1 1 1 2 1 -1 1 0 2 Range 33,120 85,200 79,000 79,000 22,666 48,000 22,666 4,688 4,500 2,650 8,375 Minimum 0 4,800 1,000 1,000 667 0 667 0 2,500 1,350 625 Maximum 33,120 90,000 80,000 80,000 23,333 48,000 23,333 4,688 7,000 4,000 9,000 Sum 86,720 308,100 326,680 326,680 86,655 127,500 86,655 8,188 43,392 33,250 21,425 Count 15 8 15 15 12 12 12 3 11 13 6

62

Largest(1) 33,120 90,000 80,000 80,000 23,333 48,000 23,333 4,688 7,000 4,000 9,000 Smallest(1) 0 4,800 1,000 1,000 667 0 667 0 2,500 1,350 625 Confidence Level(95.0%) 4,860 27,876 16,677 16,677 4,843 10,203 4,843 6,054 1,073 508 2,977

63

Table 8. Correlations of Yield to Field Size for Selected Crops

CORRELATIONS OF FIELD SIZE (HA) TO YIELD (KG/HA)

Beans

Cabbage

Carrots

Cotton

Groundnuts

Leafy Vegetables

Maize

Onion

Potatoes

Soya Bean

Tobacco

Tomato

Wheat

Natural Region

NR1

(0.04) 0.01

(0.16)

NR2

(0.17)

0.28 0.01

(0.91)

NR3

(0.05)

0.23

(0.10)

0.65

NR4

(0.07)

(0.08)

(0.55)

(0.02)

NR5

0.04

(0.24) (0.27)

(0.34)

ALL REGIONS

(0.10) (0.28) 0.22

0.29 (0.35) (0.22)

0.17 (0.21) 0.27 0.05 0.23 0.41

(0.02)

Figure 23. Correlation of Yield with Fertiliser Application

The average coefficients of variation of selected crop prices observed at the farm level and at the aggregated level have been calculated for all natural regions. As for crop yield variability, the average variability of output price across farm is observed to be higher at the farm level than at the aggregated level for all natural regions. However, the difference found is much smaller than in the case of yield. The spatial integration of output markets equalizes output prices across locations, making the price variability less location specific than yield variability. The aggregation bias may mislead one to underestimate the yield variability when observing the aggregated level. This bias has to be properly taken into consideration in order to assess the producer’s exposure to risk.

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1


WHEAT REGION 5

WHEAT REGION 4

WHEAT REGION 3

WHEAT REGION 2

TOMATOES REGION 5

TOMATOES REGION 4

TOMATOES REGION 3

TOMATOES REGION 2

MAIZE REGION 5

MAIZE REGION 4

MAIZE REGION 3

MAIZE REGION 3

MAIZE REGION 2

MAIZE REGION 1

BEANS REGION 5

BEANS REGION 4

BEANS REGION 3

BEANS CROP REGION 2

WHEAT ALL REGIONS


TOBACCO ALL REGIONS


POTATOES ALL REGINS

OTHR ALL REGIONS

ONION ALL REGIONS

MAIZE ALL REGIONS


COTTON ALL REGIONS


CARROTS ALL REGIONS

CABBAGE ALL REGIONS

BEANS ALL REGIONS


DETERMINANTS OF PRODUCTIVITY IN IRRIGATION FOR FERTILISER

TOTAL FERTILISER (KG)

D Quantity (kg)

AN Quantity (kg)

64

Figure 24 Correlation of Yield with Cropped Area

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1


WHEAT REGION 5

WHEAT REGION 4

WHEAT REGION 3

WHEAT REGION 2

TOMATOES REGION 5

TOMATOES REGION 4

TOMATOES REGION 3

TOMATOES REGION 2

MAIZE REGION 5

MAIZE REGION 4

MAIZE REGION 3

MAIZE REGION 3

MAIZE REGION 2

MAIZE REGION 1

BEANS REGION 5

BEANS REGION 4

BEANS REGION 3

BEANS CROP REGION 2

WHEAT ALL REGIONS


TOBACCO ALL REGIONS


POTATOES ALL REGINS

OTHR ALL REGIONS

ONION ALL REGIONS

MAIZE ALL REGIONS


COTTON ALL REGIONS


CARROTS ALL REGIONS

CABBAGE ALL REGIONS

BEANS ALL REGIONS


DETERMINANTS OF PRODUCTIVITY IN IRRIGATION BY CROPPING AREA

65

Figure 25. Correlation of Yield with Labour Application

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8


WHEAT REGION 5

WHEAT REGION 4

WHEAT REGION 3

WHEAT REGION 2

TOMATOES REGION 5

TOMATOES REGION 4

TOMATOES REGION 3

TOMATOES REGION 2

MAIZE REGION 5

MAIZE REGION 4

MAIZE REGION 3

MAIZE REGION 3

MAIZE REGION 2

MAIZE REGION 1

BEANS REGION 5

BEANS REGION 4

BEANS REGION 3

BEANS CROP REGION 2

WHEAT ALL REGIONS


TOBACCO ALL REGIONS


POTATOES ALL REGINS

OTHR ALL REGIONS

ONION ALL REGIONS

MAIZE ALL REGIONS


COTTON ALL REGIONS


CARROTS ALL REGIONS

CABBAGE ALL REGIONS

BEANS ALL REGIONS


DETERMINANTS OF PRODUCTIVITY IN IRRIGATION FOR LABOUR

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7


WHEAT REGION 5

WHEAT REGION 4

WHEAT REGION 3

WHEAT REGION 2

TOMATOES REGION 5

TOMATOES REGION 4

TOMATOES REGION 3

TOMATOES REGION 2

MAIZE REGION 5

MAIZE REGION 4

MAIZE REGION 3

MAIZE REGION 3

MAIZE REGION 2

MAIZE REGION 1

BEANS REGION 5

BEANS REGION 4

BEANS REGION 3

BEANS CROP REGION 2

WHEAT ALL REGIONS


TOBACCO ALL REGIONS


POTATOES ALL REGINS

OTHR ALL REGIONS

ONION ALL REGIONS

MAIZE ALL REGIONS


COTTON ALL REGIONS


CARROTS ALL REGIONS

CABBAGE ALL REGIONS

BEANS ALL REGIONS


DETERMINANTS OF PRODUCTIVITY IN IRRIGATION WITH PRODUCE PRICE

66

Figure 26. Correlation of Yield with Produce Price

Figure 27. Correlation of Yield with Farm Category

In establishing the premise for analysis to determine the production function analysis: The positive relationship exists between total output and age, education, labour, and non-labour input cost. This implies that as more of these variable are employed, there will be an increase in total output of crops. On the other hand, when results show inverse relationship. An inverse or negative relationship is a mathematical relationship in which one variable decreases as another increases. The relationship between output and farm size, years of experience and sex of respondents is investigated. An inverse relationship between output and farm size is unexpected. This could be due to poor farm management and poor soil fertility resulting from lack of land improvement. Also the negative relationship between output and education is unexpected but could be due to the generally small number of years of formal education observed throughout the sample. This has probably hindered the adoption of new techniques of production. This is probably due to the fact that farmers with long years of experience are used to obsolete methods of farming, traditional tools and species which do not encourage high output.

Table 9. Correlation Results of Yield and Fertiliser, Produce Price and Scheme Category


WHEAT REGION

5

WHEA

T REGION 4

WHEAT REGIO

N 3

WHEA

T REGIO

N 2

TOMAT

OES REGIO

N 5

TOMAT

OES REGIO

N 4

TOMAT

OES REGIO

N 3

TOMAT

OES REGIO

N 2

Area (ha) (0.449)

(0.125 0.516

(0.235)

(0.118)

(0.308)

(0.121) 0.675

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8


WHEAT REGION 5

WHEAT REGION 4

WHEAT REGION 3

WHEAT REGION 2

TOMATOES REGION 5

TOMATOES REGION 4

TOMATOES REGION 3

TOMATOES REGION 2

MAIZE REGION 5

MAIZE REGION 4

MAIZE REGION 3

MAIZE REGION 3

MAIZE REGION 2

MAIZE REGION 1

BEANS REGION 5

BEANS REGION 4

BEANS REGION 3

BEANS CROP REGION 2

WHEAT ALL REGIONS


TOBACCO ALL REGIONS


POTATOES ALL REGINS

OTHR ALL REGIONS

ONION ALL REGIONS

MAIZE ALL REGIONS


COTTON ALL REGIONS


CARROTS ALL REGIONS

CABBAGE ALL REGIONS

BEANS ALL REGIONS


DETERMINANTS OF PRODUCTIVITY IN IRRIGATION BY FARM CATEGORY

NATURALREGION

Irirgation Scheme

Category

67

)

AN Quantity (kg)

0.849

0.118 (0.220)

0.113

0.620 0.500 (0.201) 0.366

D Quantity (kg)

0.625

0.259 (0.133) 0.322 0.585 0.801 (0.267) 0.423


0.751

0.196 (0.189) 0.229 0.607 0.601 (0.259) 0.473

TOTAL LABOUR

(0.351)

0.075 (0.300)

(0.061)

(0.001) (0.335) (0.146) 0.623

Produce Price ($/kg)

(0.106)

(0.292) (0.382) (0.137) 0.607

Irirgation Scheme Category

0.063 0.520 0.337 (0.495) 0.473

NATURALREGION


MAIZE REGION

5

MAIZE

REGION 4

MAIZE REGIO

N 3

MAIZE REGIO

N 3

MAIZE REGIO

N 2

MAIZE REGIO

N 1

Area (ha) (0.250)

(0.009)

0.241

0.241 0.280 (0.455)

AN Quantity (kg)

0.005

0.264 0.165

0.165

0.126 0.965

D Quantity (kg)

0.040

0.477 0.182

0.182

0.135 (0.507)


0.028

0.392 0.237 0.237 0.135 0.585

TOTAL LABOUR

(0.182)

0.071 0.226 0.226 (0.220) (0.450)


(0.141)

0.173 0.039 0.039 (0.164)


0.133 0.027 0.027 0.167 0.406

NATURALREGION


BEANS REGION

5

BEAN

S REGION 4

BEANS REGIO

N 3

BEAN

S CROP

REGION 2

WHEAT

ALL REGIO

NS

TOMAT

OES ALL

REGIONS

Area (ha) 0.121

(0.365)

(0.044)

(0.034) (0.133) (0.041)

AN Quantity (kg)

0.279

0.134 (0.123) 0.337 0.008 0.042

D Quantity (kg)

(0.165)

0.112

(0.191)

0.421

0.112 0.080


0.023

0.139

(0.157) 0.450 0.060 0.075

TOTAL 0.139 (0.009) 0.052 (0.036) 0.128

68

LABOUR (0.105)


(0.019)

(0.059)

(0.245)

0.194 (0.313) (0.058)


0.070 (0.486) 0.430 (0.090) (0.006)

NATURALREGION

0.229 (0.176)


TOBACC

O ALL REGION

S


POTAT

OES ALL

REGINS

OTHR ALL

REGIONS

ONION ALL

REGIONS

MAIZE ALL

REGIONS

Area (ha) 0.859

0.276 0.220 0.295 (0.122) 0.199

AN Quantity (kg)

(0.146)

(0.016)

(0.180)

(0.051) 0.138 0.168

D Quantity (kg)

(0.104)

0.167 0.007 0.229 0.205 0.164


(0.126)

0.169 (0.042) 0.094 0.180 0.186

TOTAL LABOUR

(0.085)

0.617 (0.017) 0.038 0.329 0.107


0.187

(0.259)

0.419

(0.155) (0.254) (0.015)


0.214

0.516 0.280

0.077

(0.187) 0.202

NATURALREGION

0.031

(0.084)

(0.434)

(0.325) 0.184 (0.299)


LEAFY VEGETA

BLES ALL

REGIONS

COTT

ON ALL

REGIONS

GROUND NUTS

ALL REGIO

NS

CARR

OTS ALL

REGIONS

CABBAGE ALL REGIO

NS

BEANS ALL

REGIONS

ALL CROPS

ALL REGIO

NS

Area (ha) 0.187

0.314 (0.096)

(0.142)

0.455 (0.053) 0.045

AN Quantity (kg)

0.058

0.280 0.221 0.502 0.221 0.033 0.002

D Quantity (kg)

(0.058)

(0.102)

0.987 0.543 0.341 0.036 0.030


(0.007)

0.145 0.987 0.569 0.365 0.038 0.021

TOTAL LABOUR

0.537

(0.024)

0.740

(0.081) (0.344) (0.003) 0.006


(0.336)

(0.000

0.118

0.429 (0.043) (0.023)

69

)


0.230

0.615 0.076 0.669 (0.026) 0.025

NATURALREGION

0.396

(0.827)

0.269 (0.108)

(0.584) 0.135 (0.073)

GENERAL OBSERVATIONS

Yield-yield correlation

The correlation of yield across crops significantly affects a farmer’s crop diversification strategy. The less the yield of one crop is correlated with another crop, the more benefits it generates to diversify production between these crops. The farm level data shows that the crop yields are not perfectly correlated. In all the cases, yield is less correlated at the farm level than at the aggregate level. This is partly the result of a farmer’s crop diversification strategy. Among the farmers, yield correlation is higher, implying that the failure of one crop is more likely associated with the failure of another crop. This may be revealing of the systemic nature of risk in Zimbabwe, where drought affects the yield of all crops simultaneously.

Price-price correlation

The correlation between prices of different crops is also an important factor to determine the farmer’s crop diversification strategy. Price risk tends to be more systemic so that higher coefficients of correlations are found between prices than between yields. In addition, the descriptive analysis shows that the difference between the farm level and aggregated level correlation of price across crops is smaller than is the correlation of yield across crops.

Summary General Observations A wide range of yield gaps are observed around the country, with average yields ranging from roughly 20% to 35% of yield potential. Many irrigated cropping systems should target achieving about 80% of yield potential. This implies that yield gains in the country will be significant in the near future. Generally most crop yields may even decline in the long term if yield potential is reduced because of climate change. Raising average yields above 80% of yield potential appears possible but only with technologies that either substantially reduce the uncertainties farmers face in assessing soil and climatic conditions or that dynamically respond to changes in these conditions (e.g., sensor-based nutrient and water management). Although these tools are more often discussed because of their ability to reduce costs and environmental impacts, their role in improving future crop yields may be just as important. A risk is said to be systemic if it affects many farms at the same time. If this is the case, the risk variable should be correlated across farms. This will have an impact on the size of the aggregation bias: for those crops with risk that has a weak correlation across farms, the difference of the observed variability between the farm and the aggregated level is larger, leading to higher aggregation bias. Statistics show that the yield risk is much less correlated across farms, meaning that yield risk is more farm specific However, price risk is highly correlated across farms. If a farmer suffers from low prices, it is highly likely that other farmers experience similar adversity at the same time. In regions 4 and 5 the farmers in the sample suffer from more systemic yield risk – probably linked to droughts as much as they do from price risk. The type of weather risk determines the systemic nature of yield risk.

The analysis of farm level data has shown several important characteristics of the risk environment that farmers are exposed to. Not all farmers are exposed to the same characteristics, but it can be shown that there are similarities for a large share of the farmers

70

in the samples under study. For instance, it has been shown that yield risk at the farm level is greater than at the aggregated level This is true across the natural agro-ecological regions and commodities in the samples.

It has been shown that the average yield risk at the farm level is significant and comparable with price risk. Although the significance of the negative correlation between price and yield in stabilizing income is analyzed, any stabilization should take into consideration the degree of price-yield correlations. The data indicates that the correlation of yields and prices of different crops are far from perfect (less than one) and that yields are less correlated with each other than prices for most of the farms. Moreover, the correlation of risk across farms is also an important dimension of risk at the farm level. In general, the farmer is exposed to similar price shocks as other farms, which is indicated by high correlation of prices across farms.

Many statistical factors beyond the variance of each income component determine income risk: output-cost correlation, price-yield correlation and crop diversification. A simple methodology has been developed to determine the relative importance of these factors in stabilizing income.

When we estimated the Cobb-Douglas production function, we found that for agro-ecological natural regions in Zimbabwe for maize, wheat, beans, and tomatoes.

71

References used for data analysis Pingali PL, Pandey S. 2001. World maize needs meeting: technological opportunities and priorities for the public sector. In CIMMYT 1999–2000 World Maize Facts and Trends. MeetingWorld Maize Needs: Technological Opportunities and Priorities for the Public Sector, ed. PL Pingali, pp. 1–24. Mexico, DF: CIMMYT Tittonell P, Vanlauwe B, Corbeels M, Giller KE. 2008. Yield gaps, nutrient use efficiencies and response to fertilisers by maize across heterogeneous smallholder farms of western Kenya. Plant Soil 313:19–37 Tittonell P, Shepherd KD, Vanlauwe B, Giller KE. 2008. Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya—an application of classification and regression tree analysis. Agric. Ecosyst. Environ. 123:137–50 Pingali PL, Heisey PW. 1999. Cereal productivity in developing countries: past trends and future prospects. Econ. Work. Pap. 7682, Int. Maize Wheat Improv. Cent., CIMMYT, Mexico, DF Lobell, David B.; Cassman, Kenneth G.; and Field, Christopher B., "Crop Yield Gaps: Their Importance, Magnitudes, and Causes" (2009). NCESR Publications and Research. Paper 3. http://digitalcommons.unl.edu/ncesrpub/3 J. of Sustainable Development in Agriculture & Environment Vol. 3(2):20-33 April. 2008 ISSN 0794-8867 ©Paraclete Publishers. Resource-Productivity, Allocative Efficiency and Determinants of Technical Efficiency of Rainfed Rice Farmers: A Guide for Food Security Policy in Nigeria. Ogundari, K. Department für Agrarökonomie und Rurale EntwicklungGeorge August Universitat, Platz der Göttinger Sieben 5,D-37073 Göttingen, Germany Bindraban, P. S., A. Verhagen, P. W. J. Uithol, and P. Henstra. 1999. A land quality indicator for sustainable land management: The yield gap. Report 106. Wageningen, Netherlands: Research Institute for Agrobiology and Soil Fertility. Capalbo, S. M., and T. T. Vo. 1988. A review of the evidence on agricultural productivity and aggregate technology. In Agricultural productivity: Measurement and explanation, ed. S. M. Capalbo and J. M. Antle. Washington, D.C.: Resources for the Future. Molden, David J., R. Sakthivadivel, Christopher J. Perry, Charlotte de Fraiture, and Wim H. Kloezen. 1998. Indicators for comparing performance of irrigated agricultural systems. Research Report 20. Colombo, Sri Lanka: International Water Management Institute. Molden, David J., R. Sakthivadivel, Christopher J. Perry, Charlotte de Fraiture, and Wim H. Kloezen. 1998. Indicators for comparing performance of irrigated agricultural systems. Research Report 20. Colombo, Sri Lanka: International Water Management Institute.

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