Traditional and Improved Varieties in Uganda: food security, agriculture

productivity and biodiversity conservation.

Manuela Coromaldi – University of Rome “Niccolò Cusano”

Giacomo Pallante - University of Rome Tor Vergata

Sara Savastano - University of Rome Tor Vergata

17th ICABR Conference 18th -21th June, 2013, Ravello, Italy

Building blocks• Background and motivations

• Our aim

• Literature

• Data description

• Empirical model

• Estimation results

• Conclusions

Background and motivationsEvidence from the Green Revolution show a strong impact of

the adoption of improved varieties on agriculture productivity growth (Evenson and Gollin, 2007) , on food production and food security (Tillman, 1999).

High-yield-crop of Asian Green Revolution were bred to work better with greater applications of fertilizer than traditional varieties and to work better on irrigated land (Larson et al. 2010).

Level of adoption in Sub-Saharan Africa has been limited.

Our aimThis paper aims at testing the factors affecting productivity and

agricultural biodiversity in terms of local and improved varieties.

As the Sub-Saharan Africa countries heavily rely on traditional technologies, we study the potential of local species who are expected to perform better in marginal production environments and who can be more resistant to climatic stress.

We adapt the Steinfeld approach (2000) to empirically verify the effect of intensification of crop production on the opportunity costs for smallholders of conserving local species when there are factors market and agronomic constraints that inhibit the efficient and economic use, and availability of improved variety.

Literature (1)Evidences from Sub-Saharan Africa show low level of adoption

of improved technology and inappropriate use of inputs: 40% of fertilizer is used on maize (Morris et al., 2007; Heisey

and Norton, 2007) Sub-Saharan Africa : the average dose is about 17 kg/ha of nutrients Developing countries: 100 kg/ha Developed countries: 270 kg/ha on the same crop

Sub-Saharan Africa failing in adoption of agricultural technology is due not only to market constraints but also to low responsiveness of marginal land respect to external inputs (Sanchez et al., 1997).

Literature (2)The use of improved seeds has increased the farmers yield in Asia and Latin

America, but has also let arise diffused concerning about the inter and intra species genetic diversity erosion (Harlan, 1992).

The variability is then dramatically reduced, when single lines or F1 hybrids varieties become dominant to the detriment of diversity-rich landraces (Lipper and Cooper, 2009).

When the farmers preserve a wide range of local landraces, they conserve a genetic portfolio that minimizes a set of private and public risks (Weitzman, 2000).

To the point where smallholders allow the conservation of local public goods as the resilience of the local ecosystem to face biotic and abiotic stresses (Jarvis et al., 2007) or global public goods as the maintenance of a pool of genetic material and the option value to use it (Bellon, 2009), those farmers are “custodians” of the agricultural biodiversity conservation (Silveri and Manzi, 2009).

Literature (3)Authors and year yield Quantity of fertilizer Soil characteristics

Matsumoto et al. (2013) maize (local variety) 12.4 kg of base fertilizer plus 10 kg top-dressing fertilizer for

a quarter acre of maizeNkoya et al. (2010)

80 kg N ha-1, 5 t ha of manure and 100% retention crop residue poor soil quality

Glover Amengor and Tetteh (2008) tomato 460 and 375 g ha-1 of lindane and propoxur insecticides ferric acrisols of

Ghana

Muthomi et al. (2007) legume mixing inorganic fertilizer at a rate of 200 kg/ha with 0.7 L/ha

and 2 kg/ha of insecticide and fungicide respectivelyrainy and non rainy

area of Kenya

Morris et al. (2007)Heisey and Norton (2007)

maize 17 kg/ha compared to the developing country average of 100 and the industrialized country average of 270 kg/ha

Shankar and Thirtle (2005) cotton 1 L/ha of insecticide South Africa

Jama et al. (1997) maize 10 kg ha-1 of Phosphorousalfisols and ultisols

of humid and subhumid areas

Nyakatawa (1996) sorghum 50 kg N ha-1 entisols and

vertisols area of Zimbabwe

Hoekstra and Corbett (1995) 250 kg P ha-1 plus urea at 60 kg N ha-1 Kenyan alfisols and

oxisols

Bationo and Mokwunye (1991)

15-30 kg ha-1 of Phosphorous (P) sandy soil in semiarid regions

Mugwira and Mukurumbira (1986)

100 kg ha-1 of Nitrogen (N) Sandy soil in semiarid regions

Agricultural intensification index (1)

The agricultural intensification concept is defined as the increase of agricultural production on a fixed portion of land, as opposite to a raising in production due to expansion of land (Netting, 1993).

It involves the substitution of other inputs, as well technology, for a constant land in order to let the yielding function rise (Brookefield, 1993).

Agricultural intensification index (2)

Herzog et al. (2006) developed an intensification index with the aim to study the effect on biodiversity at the landscape level (Agricultural Intensity index, AI ).

where yi is a variable of agricultural intensification, n is the number of intensification variables and yimin and yimax are the minimum and maximum value of the agricultural intensification variables in the sample.

Agricultural intensification index (3)

The use of AI holds two interesting features:

1. The input’s application on per hectares basis is a widely comparable measure over regions.

2. It is a relative index. Since it accounts for maxima and minima administering of inputs into a community, the differences in soil qualities, climatic conditions and farm’s practices are endogeneized so that the context-specific traits are taken into consideration.

Agricultural intensification index (4)

We estimate the AI index for each households by using the following three variable of agricultural intensification:

1. Chemical fertilizers (kg/ha): sum of Nitrate, Phosphate, Potash and Mixed.

2. Pesticides (Kg/ha): sum of Insecticides, Miticides/Acaricides, Fungicides, Rodenticides, Herbicides and growth regulators.

3. Years of fallow (number of years).

Data description (1)Data are taken from the Uganda National Panel

Survey 2009/10 (UNPS).

UNPS is carried out annually over a twelve-month period on a nationally representative sample of households.

The survey includes 3,123 households that were distributed over 322 enumeration areas.

Data description (2)Traditional Variety Improved Variety

Net Crop Income/ac 4733.074 4914.85

Area Operated 2.243*** 3.144***

Age of household head 48.198*** 45.703***

Avg adult yrs of education 5.007*** 5.692***

Household has access to road 0.996 0.993

Head is female 0.296*** 0.197***

N. of HH using HYV in the district 17.369 17.481

Dummy soil good quality 72.663* 77.117*

Dummy slope steep 22.046*** 15.332***

Area Rented in irrespective of the season and the use 0.392 0.483

Household hired labor input 0.519*** 0.693***

Person Days of family labor 270.771*** 322.134***

Sum of N,P,Po,Mix per ha 0.096* 13.938*

Sum of ins,mit,fun,rod,nem per ha 0.165 0.224

Class of lenght of fallow 9.233*** 8.959***

Intens. ind. with fallow,chem,pest 30.861** 30.060**

* significant at 10%; ** significant at 5%; *** significant at 1%

Conceptual framework (1)

• We implement the conceptual framework developed by Steinfeld (2000) and adapted by Narloch et al. (2011);

• the aim is to verify as the gross profit of farmers from the use of traditional or improved variety changes under the increase of the intensification degree of the farming system.

Conceptual framework (2)

Improved gross profits

Traditional gross profits

Gross profit

Degree of intensification

The empirical results

17

020

040

060

080

0

0 20 40 60 80intens. ind. with fallow,chem,pest

improved traditionalimproved fit traditional fit

Estimation strategy (1)• To control for selection bias in the assessment agriculture

productivity in the adoption of improved variety, we make use of Heckman's two-step estimation (Heckman, 1978).

• In the first stage, we compute a Probit regression in order to estimate the probability that a given farmers will adopt a new technology.

• This regression is used to estimate the Inverse Mills Ratio (IMR) for each farmers, and this will be used as an instrument in the second regression where we will analyze the determinant farmers efficiency through an ordinary OLS

Estimation strategy (2)• Following Maddala (1983) Amemiya (1985) and Johnston and

DiNardo (1997), we will use different instruments to control for identification problems

The first stage Heckman procedure, namely the adoption equation is a standard Probit regression of the form:

(1)

where Y indicates adoption (Y=1 if farmers adopted the improved technology, 0 if they did not adopted). Z is a vector of explanatory variables such as household characteristics, asset’s endowment, credit access, and dummy for organic inputs used, fertilizers, geographical dummies).

)()|1(Pr ZXYob

Estimation strategy (3)In the second stage, we correct for self-selection by incorporating a

transformation of these predicted farmers probabilities as an additional explanatory variable.

The agriculture productivity equation may be specified as (2)

where AP* denotes an underlying level of technical efficiency, which is not observed if the farmers did not adopted.

The conditional expectation of efficiency if the farmer adopted is:

(3)

uXAP *

1,|1,|* YXuEXYXAPE

Estimation strategy (4)Under the assumption that the error terms are jointly normal, we

have:

(4)where ρ is the correlation between unobserved determinants of the

probability of adopting the technology ε and the unobserved determinants of farmers technical efficiency u;

and λ is the Inverse Mills Ratio.

)(1,|* ZXYXAPE u

legend: * p<0.05; ** p<0.01; *** p<0.001 lambda -0.3613 sigma 2.4492 rho -0.1475 N 1226 1226 1226 Statistics Constant -6.8310 Log distance to road (~) -0.0216 Number of HH using HYV~i 0.0567*** Log pers-days family l~r 0.1885*** Log h hired ag labor i~t 0.5140*** Log area rented in 0.0991 Dummy slope steep -0.0000 Dummy soil good quality 0.0000 head is female -0.2117* Household has access t~d -0.3494 Log avg adult yrs of e~n 0.0611 Log age-squared of hh -0.4162 Log age of hh 2.8182 Log operated area 0.2017** Intens. ind. with fall~t -0.0032 dimprov Constant 0.2971 -10.6277* -11.3548 Number of HH using HYV~i -0.0178 0.0566*** Log pers-days family l~r 0.6788*** 0.7373*** Log h hired ag labor i~t 0.4387* 0.4909 Log area rented in -0.0309 0.0523 Dummy slope steep 0.0074*** 0.0001 0.0049 Dummy soil good quality 0.0057** 0.0007 0.0084** head is female 0.0207 -0.2110* -0.1032 Household has access t~d 1.4577* -0.3202 2.3625 Log distance to road (~) 0.0988 -0.0091 Log avg adult yrs of e~n 0.0766 0.1347 0.1916 Log age-squared of hh -0.1600 -0.7339* -1.0004 Log age of hh 0.7987 5.2589* 6.5042 Log operated area -1.0683*** 0.2769*** -1.1029*** Intens. ind. with fall~t 0.0257* 0.0506** #1 Variable OLS PROBIT HECKMAN

Table 1: Two-step estimates of the adoption equation

ConclusionsLow level of technology adoption in Uganda, but increasing probability if

neighbor farmers’ adopt.The perception of technology profitability increases with the number of

households using improved seeds in the district.Positive impact of Intensification Index on productivity but level of input use

is still very low among adopters. Need to increase extension services for maximizing use of scarce seeds and agriculture productivity.

Evidence of the presence of an inverse farm size productivity relationship, but larger farmers have higher probability to adopt.

Adoption does not lead to substantial difference in agriculture productivity.Extensions: Estimate Heckman Model with 2 selection equations:

Probability of adopting HYV Probability that once adoption has taken place, fertilizer use will follow.