CC BY
42
Determinants of Drought Tolerant Rice Variety Adoption: Evidence from Rural Farm Household in Northern Part of Bangladesh
Razia Sultana 1, Md. Habibur Rahman 1, Mohammad Rashidul Haque Md. Mohsin Ali Sarkar 1, Syful Islam 1
1 Bangladesh Institute of Nuclear Agriculture BAU Campus, Mymensingh-2202, Bangladesh
DOI: 10.22178/pos.75-12
LCC Subject Category: SB1-1110
Received 21.09.2021 Accepted 25.10.2021 Published online 31.10.2021
Corresponding Author: Razia Sultana razia1201@gmail.com
© 2021 The Authors. This article is licensed under a Creative Commons Attribution 4.0 License W^Ll
Abstract. The drought-tolerant rice variety Binadhan-19 study was conducted in five districts: Mymensingh, Ranpur, Pabna, Rajshahi and Chapainwabganj of Bangladesh. A total of 200 farmers were randomly selected (40 from each location) to collect the data with a pre-designed questionnaire. Tabular, descriptive statistics and Probit model were used to fulfil objectives. The estimated log-likelihood value of gender, farm size, yield, agricultural extension services have a statistically and significant positive effect on the adoption of the variety. The household characteristic related variables such as age, experience, annual income, human labour, duration of the variety have no statistically significant effect on the adoption of the variety. Marginal coefficients indicate that if male farmers increased by 100%, the probability of adopting Binadhan-19 variety would increase at 38 times more likely to adopt the variety. If the farm size of Binadhan-19 increased by 100%, the probability of adopting the variety would be increased by 0.07%. A farmer who has access to agricultural extension service is about 39 times more likely to adopt the variety. Again, if the yield increased by 100%, adopting the varieties would increase by 0.08%. The marginal coefficients of locations and soil fertility are negatively significant, indicating that if these two variables increased by 100%, the probability of adopting the varieties would decrease by 0.06% and 30%, respectively.
Keywords: Drought tolerant; Rice; Adoption; Probit model; determinants; Binadhan-19; Bangladesh.
INTRODUCTION
Global food demand and supply is continuously changing due to climate change. Nowadays, floods, drought, heat and cold are seen by many people over the year in the world. Adopting improved agricultural technology and variety in drought-prone areas can be an essential alternative to eliminating food shortages and food insecurity by improving crop productivity and income. Further, promoting the adoption of improved crop varieties sustainably helps improve the welfare of households [9]. The global circulation model (GCM) predicts that the average temperature increases in Bangladesh due to climate change will be 1.0 °C by 2030 and 1.4 °C by 2050, and monsoon precipitation is likely to increase 6.8 % by 2050 [37]. Agricultural production in the face of climate change (CC) requires a change and reorientation of agricultural systems to supply adequate food for the increasing world population [13]. Rice is one of the essential staple's
foods in the developing world, accounting for about 19 % of the total crop area harvested, 20 % of the caloric intake from staples and 50% of the food expenses by poor people in low- and middle-income countries [45]. By [14], rice is consumed by more than 4.8 billion people in 176 countries. It is the essential food crop for over 2.89 billion people in Asia, over 40 million people in Africa and over 150.3 million people in America. Demand for rice is expected to grow faster than the production in most countries [42], so much so that by 2025 we will need 800 million tonnes of it annually. At present, many developing countries have become food self-sufficiency because of appropriate policies and strategies taken by their government. But several biotic and abiotic stresses may slow down this progress in future. Understanding these problems, research organisations in different countries such as China, India, Bangladesh, etc., have developed stresstolerant (submergence, salt, drought, etc.) rice
varieties. Rice production would be a significant concern in recent years due to changing climatic conditions. A significant rice yield may hamper only fluctuations of those climatic parameters [12].
The population of Bangladesh will reach about 215 million in 2050. So, food grain must be increased for the upcoming future generation. Drought is one of the vital problems for many countries. The severity of such an issue goes massive when it comes as an obstacle to ensuring optimum agricultural production for a country like Bangladesh. Droughts rank first among all natural hazards when measured in terms of the number of people affected [33, 18, 43]. Although as a natural hazard, droughts differ from other natural hazards in several ways [43]. According to the IPCC special report on the regional impacts of climate change, there would be drastic changes in rainfall patterns in a warmer climate. Bangladesh may experience a 5-6% increase in rainfall by 2030 due to glacier melting and more intense monsoon, which will create frequent, big and prolonged floods and increased droughts outside the monsoon season. As a result of climate change, Bangladesh experiences a dry period for seven months, from November to May, when rainfall is normally low. Drought mostly affects the country in the pre-monsoon (March-May) and post-monsoon periods (October-November) [11]. Droughts are becoming more frequent, intense, spatially extensive, and longer duration [24]. It is one of the major abiotic stresses which adversely affect crop growth and yield and thus a constraint for productivity worldwide [25]. Recent research findings from a field study118 revealed that, on average, a 1% increase in the drought-affected area reduces aman and aus. annual rice production per household by 1,382 and 693 kgs, respectively [29]. This problem occupied an extreme position in the northwest region of Bangladesh. Bangladesh has already shown an increased drought frequency in recent years [44]. The average drought occurrence in Bangladesh is once every 2.5 years [3, 19]. In the last decade, this region has experienced drought once every year [17]. In the northwest region. Drought can have devastating impacts on rice production. Further, every year farmers in these areas incur high costs and huge crop losses due to drought. The impact of drought leads to the shortage of water and food and has a long-term environmental, socioeconomic, and health impact on the population [40]. The phenomenon is causing enormous diffi-
culties towards maintaining livelihoods and has become a severe problem in the North Western parts of Bangladesh. Farming of Aus. crop area using unused seasonal rainwater must be increased during the Kharif-1 season as an additional and substitute cereal crop to gradually replace the cultivation of more irrigation water consuming and costly Boro farming [36]. Keeping this in mind, the Bangladesh Institute of Nuclear Agriculture developed an Aus. rice variety, namely Binadhan-19, tolerant of drought. The present attempt has been made to focus on the determinants of the improved variety in the North Western farmers in Bangladesh.
The agricultural research literature contains numerous studies on adopting improved (high yielding) crop varieties [15, 16, 22, 27]. The majority of the previous studies focused on identifying the factors affecting technology adoption [1, 2, 21, 41] while others examined the effect of adoption on efficiency and productivity [4, 7, 20, 30] or household welfare [5, 7, 8, 10]. In addition, authors [28, 16] investigated the impact of NERICA adoption on income and poverty in Uganda and Nigeria, respectively. However, empirical study on the determinant of drought rice is hard to find. Most studies on farmers' joint adoption decisions have focused on adopting improved crop varieties and inorganic fertiliser [26, 34, 35, 39]. Therefore, determinants of drought-tolerant rice varieties adoption are needed for future policy as well as sustainability. Finally, the study's objective was to determine factors affecting the adoption of drought-tolerant variety cultivation in the study areas.
METHODOLOGY
Study areas, Sampling Procedure and Sample Size. The study was conducted in five districts of Bangladesh. Purposive sampling and multistage stratified random sampling techniques were followed to collect sample farmers for this study. Five districts under each environment were selected in this study. According to the literature review, Chapainawabganj, Rajshahi and Pabna were selected as high to moderate drought-prone areas, whereas Rangpur and Mymensingh were selected as slight and no drought areas. Then one Upazila from every five districts and two blocks from each Upazila were purposively selected in consultation with DAE personnel and rice scientists. Finally, a total of 200 farmers, taking 40 from each district, were randomly selected for
the study, where 20 were adopters, and 20 were non-adopters of drought-tolerant variety. In the sampled areas, data were collected through a pre-designed interview schedule. Tabular, descriptive statistics and probit models were used to analyse the collected data. Collected data were edited, summarised, tabulated and analysed to fulfil the objectives of the study.
Table1 - Study areas and characteristics of Aus. rice
locations in Banglades h
No Areas Upazilla Characteristics
1 Mymensingh Gouripur Very Suitable
2 Ranpur Kaunia Suitable
3 Pabna Iswardi Moderate suitable
4 Rajshahi Tanore Marginally suitable
5 Chapainawabganj Nachol Not suitable
Descriptive statistics used different statistical tools like averages, percentages, and ratios to present the study results. Probit and Logit models have been used extensively by economists of
In Pakistan, [19] used the Probit model to examine the role of credit in agricultural development and identify the determinants of adoption of wheat varieties. Authors [3] also used this analysis to identify the characteristics of insecticide farmers. In the present study, the Probit regression model was used to determine the adoption and non-adoption of the variety. The probit model is a statistical probability model with two categories in the dependent variable (Liao). Probit
agricultural production and farming systems to study and analyse farmers' adoption and diffusion of agricultural interventions.
Figure 1 - Showing the suitable areas of Aus. rice cultivation in Bangladesh
analysis is based on the cumulative normal probability distribution. The binary dependent variable, y, takes on the values of zero and one. The outcomes of y are mutually exclusive and exhaustive. The dependent variable,y, depends on k observable variables xk, where k=1...K. While the values of zero and one were observed for the dependent variable in the probit model, there was a latent, unobserved continuous variable, y*.
30000,0 25000,0 20000,0 15000,0 10000,0 5000,0 0,0
Mymensingh Ranpur Pabna Rajshahi
I Chapainawabganj
Very Suitable 7253,8 4699,3 0,0 0,0 0,0
Suitable Moderatel Marginally
11745,1 4137,0 2082,8 23,5 82,3
y Suitable 3617,5 3815,7 11758,1 12359,5 4910,4
Suitable 0,0 0,0 5143,7 12578,6 21105,5
Not Suitable 300,7 0,0
837.4
993.5 32,9
Total Area
22917 12652 19822 25955 26131
Mymensingh ■ Ranpur ■ Pabna ■ Rajshahi ■ Chapainawabganj
Figure 2 - Bar diagram showing total Aus. rice area in selected areas of Bangladesh
y*=Yi=1PkXk + e (1)
where s is IN (0, a2)
The dummy variable, y was observed and was determined by y* as follows.
y=(l, if y* > 0}, 0 otherwise (2)
The point of interest relates to the probability that y equals one. From the above equations,
Prob(y=l)=ProbYlk=i P kXk+e>= =Prob(e>-YX=1 PkXk)=
=1-0(- ZLiPkXk) (3)
where 0 was the cumulative distribution function of e (Liao).
The Maximum Likelihood Estimation (MLE) technique was used to estimate probit model parameters. MLE focused on choosing parameter estimates that gave the highest probability or likelihood of obtaining the observed sample y. Thus, the main principle of MLE was to choose as an estimate of // the set of K numbers that would maximise the likelihood of having observed this particular y.
The specification of the Probit model was as follows.
yi=/o+/iXi +/2X2+/3X3+/4X45X5+/5X5++//7X7+
+/8X8+/9X9+/10X10+/11X11+/12X12+ +/i3Xi3+Ui
where Y/=Farmers adopting Binadhan-19 variety (if adopted=1; Otherwise=0); ^Intercept, Xi=Explanatory variables, /^Coefficients of respective variables, and U=Error term.
The independent variables were captured as:
X1=Age of the respondent (Year)
X2=Gender
X3= Education (Year of Schooling)
X4= Farmer's experience in farming (years)
X5= Family size
X6=Annual income
X7= Farm size (hectare)
X8= Yield
X9=Duration (days) X10=Ext contact X11=Soil fertility X12=Human labour X13= Locations
Measurement of dependent and explanatory variables were given in Table 2.
Table 2 - Measurement of dependent anc explanatory variables
Variable Type Measurement
Dependent variable Dummy 1 if farmer has adopted, otherwise 0
Explanatory Variable
X1=Age Continuous Age of the Household head (years)
X2=Gender Dummy 1 if the household respondent was male, otherwise 0
X3= Education Continuous Formal education of the respondent (years of schooling)
X4= experience in farming Continuous Farming experiences of the respondents (years)
X5= Family size Continuous Number of active (aged 15-60 yrs.) members in the family (persons)
X6=Annual income Continuous Amount of money earned by the family members in a year ('000 BDT)
X7= Farm size Continuous Amount of land under Binadhan-19 cultivation (ha)
X8=Yield Continuous Yield obtained by farmers in kg
X9=Duration (Days) Continuous No of days required for harvest
X10=Ext Contact Dummy If favourable=1; otherwise=0
X11=Soil fertility Continuous High=1, Medium=2
X12=Human labour Continuous No. of labour/ha
X13=Location Continuous Score
RESULTS AND DISCUSSION
Demographic characteristics of the Binadhan-19 cultivated farmers. The demographic characteristics of the rice farmers were presented and dis-
cussed according to their age, education, family size, income and years of farming experience. The distribution of the farmers by age showed that the mean age for Binadhan-19 cultivated farmers was 38 years. This implies that the rice
farming populations were still within their productive age and could still engage efficiently in rice production. Rice farming is a labour-intensive occupation and exerts energy for land preparation, nursery, planting, weeding and harvesting. The findings are similar to those of [5], who found out that the mean age of upland rice farmers and swamp rice farmers in Ebonyi State was 37.3 years and 39.2 years, respectively.
This is encouraging as an active age implies increased productivity and enables farmers to engage in other value-added activities like rice processing. Among the farmers, 86% was educated, categorised as illiterate, Primary, Secondary, Higher secondary, and above. In the study areas, farmers' average experience was 20 years, and income was tk. 235066 per year for adopters, and non-adopters, it was 17 years, and income was tk. 215587, respectively. The average family size was six, where 50 % was male, and 50% was female (Table 3).
Pattern of input use for rice cultivation. Farmers in the study areas used various inputs for rice cultivation. Farmers used on an average 126 person-days per hectare of total human labour for modern high yielding variety adopter where family labour was 57 person-days and hired labour was 69 person-days. On the other hand, in the case of non-adopter variety, on average 132 person-days per hectare of total human labour for modern high yielding variety adopter where fam-
ily labour was 60 person-days and hired labour was 72 person-days.
Table 3 - Socio-demographic profile of the selected rice farmer's during 2019
Variables Adopter (Average) Non-adopter (Average)
Age (years) 38 41
Educational Status (%) 86 78
Family size (no.) 6 6
Male 3 3
Female 3 3
Income (Tk/year) 235066 215587
Farming experience (years) 20 17
Farming as a single occupation 99 80
Farming+ business 27 30
Farming+ job 11 05
Farming+others 24 12
Training or extension 20 10
services (%)
For rice, the cultivation adopter used 37 kg seed per hectare of land and applied urea at the rate of 111 kg/ha, TSP 119 kg/ha, and MoP 86 kg/ha in where non-adopter used 42 kg seed per hectare of land and applied urea at the rate of 115 kg/ha, TSP 110 kg/ha, and MoP 107 kg/ha for Aus. rice Cultivation. It was observed that among the chemical fertiliser, farmers used the highest amount of TSP for the studied districts (Figure 2).
Figure 2 - Bar diagram showing input use pattern of adopter and non-adopter of improved rice cultivation in
Bangladesh
Determination of factors affecting the adoption of the variety. The estimated log-likelihood value is highly significant, indicating that the model with
predictors is to be preferred over a model without predictors. Gender, farm size, yield, and agricultural extension services have a statistically positive effect on adopting the variety. The
household characteristic related variables such as age, experience, annual income, human labour, duration of the variety have no statistically signif-
Marginal coefficients indicate that if male farmers increased by 100 %, adopting the Binadhan-19 variety would increase at 38 times more likely to adopt the variety. If the farm size of the variety Binadhan-19 increased by 100 %, the probability of adopting the variety would increase by .07%. A farmer who has access to agricultural extension service is about 39 times more likely to
Table 5 - Marginal Effect Estimates of the Probit Model
icant effect on the adoption of the variety. The study areas and soil fertility are negatively significant for adopting the variety (Table 4).
adopt the variety. Again, if the yield increased by 100%, adopting the varieties would increase by .08%. The marginal coefficients of the district and soil fertility are negatively significant. If these variables increase by 100%, the probability of adopting the varieties will decrease by .06% and 30%, respectively (Table 5).
Variable dy/dx Std. Err Z Probability
X1=Age -.000027 .0033589 0.01 0.994
X2=Gender .3870364 * .2306375 1.68 0.093
X3= Education .0311511 .0201507 1.55 . 0.122
X4= Experience in farming .0009266 .0034029 0.27 0.785
X5= Family size .0719539 .0603571 1.19 0.233
X6=Annual income 8.51e-08 1.66e-07 0.51 0.609
X7= Farm size .0007449 ** .0002441 3.05 0.002
X8=Yield .000855** .000631 1.36 0.032
X9=Duration (Days) 0000356 .0050719 0.01 0.994
X10=Ext. Contact .0850021** 0.067438 1.26 0.028
X11=Soil fertility -.3070833 *** .0728358 4.22 0.000
X12=Human labour 0005219 . .0017946 0.29 0.771
X13=Location** .0005941 .0002999 1.98 0.048
Table 4 - Maximum likelihood estimates of variable determining adoption of the variety among respondent farmers
Variable Co-efficient Std. Err Z statistic Probability
X1=Age -.0000819 .0101874 0.01 0.994
X2=Gender 1.17387* .7144071 1.64 0.100
X3= Education .0944802 .0621191 1.52 0.128
X4= Experience in farming .0028103 .0103253 0.27 . 0.785
X5= Family size -.0719539 .0603571 1.19 0.233
X6=Annual income 2.58e-07 5.06e-07 0.51 0.610
X7= Farm size .0022593** .00078 2.90 0.004
X8=Yield .003857 ** .001837 2.10 0.036
X9=Duration (Days) .0001081 .0153831 0.01 0.994
X10=Ext. contact .3857288 ** .1960491 1.97 0.049
X11=Soil fertility -.9313744*** .2483106 3.75 0.000
X12=Human labour -.0015828 .0054463 -0.29 0.771
X13=District -.0018017** .0009324 1.93 0.053
Number of observations 199
LR chi2 (12) 45.52
Prob > chi2 0.000***
Pseudo R2 0.1650
Log-likelihood -121.51639
Notes: *, **, *** represent statistically significance at 10 %, 5 % and 1 % respectively.
Notes: *, **, *** represent statistically significance at 10 %, 5 % and 1 % respectively.
CONCLUSIONS
Improving rice variety allows farmers to earn more money because of less input requirement like human, irrigation and fertiliser and shorter growth duration (100-105 days) than local rice variety. Econometrics analysis showed that gen-
der, farm size, yield, extension contact is statistically significant in adopting the variety. In addition, farmers of the study areas mention positive traits of the variety like palatability, refined rice, ease to harvest, less water and urea requirement etc.
REFERENCES
1. Abdoulaye, T., Abass, A., Maziya-Dixon, B., Tarawali, G., Okechukwu, R., Rusike, J., ... Ayedun, B.
(2014). Awareness and adoption of improved cassava varieties and processing technologies in Nigeria. Journal of Development and Agricultural Economics, 6(2), 67-75. doi: 10.5897/jdae2013.006
2. Adesina, A. A., Baidu-Forson, J. (1995). Farmers' Perceptions and Adoption of New Agricultural
Technology: Evidence from Analysis in Burkina Faso and Guinea, West Africa. Journal of Agricultural Economics, 13(1), 1-9.
3. Adnan, S. (1993). Living without floods: lessons from the Drought of1992. Dhaka: Research and
Advisory Services.
4. Adofu, I., Shaibu, S., & Yakubu, S. (2013). The economic impact of improved agricultural technology
on cassava productivity in Kogi State of Nigeria. International Journal of Food and Agricultural Economics, 1(1), 63-74.
5. Afolami, C. A., Obayelu, A. E., & Vaughan, I. I. (2015). Welfare impact of adoption of improved cassava
varieties by rural households in South Western Nigeria. Agricultural and Food Economics, 3(1). doi: 10.1186/s40100-015-0037-2
6. Amao, J., & Awoyemi, T. (2008). Adoption of Improved Cassava Varieties and Its Welfare Effect on
Producing Households in Osogbo ADP Zone of Osun State. Gene Conserve, 7(29), 1-11. Retrieved from https://www.cabdirect.org/cabdirect/abstract/20093032490
7. Asante, B. O., Villano, R. A., Battese, G. E. (2014). The effect of the adoption of yam minisett
technology on the technical efficiency of yam farmers in the forest-savanna transition zone of Ghana. African Journal of Agricultural and Resource Economics, 9(2), 75-90.
8. Asfaw, S. (2010). Estimating Welfare Effect of Modern Agricultural Technologies: A Micro- Perspective
from Tanzania and Ethiopia. Retrieved from
https://www.semanticscholar.org/paper/Estimating-Welfare-Effect-of-Modern-Agricultural-A-Asfaw/0c8fb82303b049ff29a2312c019505e249391c24
9. Asfaw, S., Kassie, M., Simtowe, F., & Lipper, L. (2012). Poverty Reduction Effects of Agricultural
Technology Adoption: A Micro-evidence from Rural Tanzania. Journal of Development Studies, 48(9), 1288-1305. doi: 10.1080/00220388.2012.671475
10. Awotide, B. A., Diagne, A., Omonona, B. T. (2012). Impact of Improved Agricultural Technology
Adoption on Sustainable Rice Productivity and Rural Farmers' Welfare in Nigeria: A Local Average Treatment Effect (LATE) Technique. Retrieved from
https://aec.afdb.org/sites/default/files/2019/12/04/impact_of_improved_agricultural_technol
ogy_adoption_on_sustainable_rice_productivity_and_rural_farmers_welfare_in_nigeria_a_local_av
erage_treatment_effect_late_technique_02.pdf
11. Banglapedia (2021. June 18). Drought in Bangladesh. Retrieved from
https://en.banglapedia.org/index.php/Drought#:~:text=Drought%20mostly%20affects%20Ba ngladesh%20in,monsoon%20and%20post%2Dmonsoon%20periods.&text=The%20percentag e%20of%20drought%20affected,of%20the%20monsoon%20crops%20only
12. Basak, J. K. (2010). Climate Change Impacts on Rice Production in Bangladesh: Results from a Model.
Retrieved from
https://www.researchgate.net/publication/346260955_Climate_Change_and_Crop_Production_
in_Bangladesh_Insights_from_the_Impacts_of_Climate_and_Groundwater_Variability_on_Rice_Pr
oduction
13. Campbell, B. M., Thornton, P., Zougmoré, R., van Asten, P., & Lipper, L. (2014). Sustainable
intensification: What is its role in climate smart agriculture? Current Opinion in Environmental Sustainability, 8, 39-43. doi: 10.1016/j.cosust.2014.07.002
14. Daromola, B. (2005). Government policies and competitiveness of Nigerian Rice Economy. Retrieved
from https://www.cabdirect.org/cabdirect/abstract/20113224337
15. Dontsop-Nguezet, P. M., Manyong, V., Abdoulaye, T., Arega, A., Amato, M. S., Ainembabazi, J. H., .... &
Okafor, C. (2016). Non-farm activities and adoption of improved cassava and beans varieties in South-Kivu, DR Congo. Tropicultura, 34(3), 262-275.
16. Dontsop-Nguezet, P., Diagne, A., Okoruwa, Y. O., & Ojehomon, V. (2011). Impact of Improved Rice
Technology (NERICA varieties) on Income and Poverty among Rice Farming Households in Nigeria: A Local Average Treatment Effect (LATE) Approach. Quarterly Journal of International Agriculture, 50(3), 267-291.
17. Habiba, U., Shaw, R., & Takeuchi, Y. (2011). Chapter 2 Socioeconomic Impact of Droughts in
Bangladesh. Community, Environment and Disaster Risk Management, 25-48. doi: 10.1108/s2040-7262(2011)0000008008
18. Hewitt, K. (1997). Regions at Risk: A Geographical Introduction to Disasters. London: Routledge.
19. Hossain, M. (1990). Natural Calamities, Instability in Production and Food Policy in Bangladesh. The
Bangladesh Development Studies, 18(4), 33-54.
20. Hossain, M., Bose, M. L., & Mustafi, B. A. A. (2006). Adoption and productivity impact of modern rice
varieties in Bangladesh. The Developing Economies, 44(2), 149-166. doi: 10.1111/j.1746-1049.2006.00011.x
21. Houssou, N., Chapoto, A. (2015). Adoption of Farm Mechanisation, Cropland Expansion, and
Intensification in Ghana. Retrieved from https://ideas.repec.org/p/ags/iaae15/211744.html
22. Huang, W., Zeng, D., & Zhou, S. (2015). Welfare impacts of modern peanut varieties in China.
Quarterly Journal of International Agriculture, 54(3), 221-238. Retrieved from
https://www.researchgate.net/publication/286865509_Welfare_impacts_of_modern_peanut_va
rieties_in_China
23. IPCC. (1997). The regional impacts of climate change: an assessment of vulnerability. Retrieved from
https://www.ipcc.ch/report/the-regional-impacts-of-climate-change-an-assessment-of-vulnerability/
24. IPCC. (2010). World climate report-global drought pattern. Retrieved from
www.worldclimaterepor.com/indexphp/2010/02/24/update-on-global-drought-pattern-sipcc-take-note
25. Jaleel, C. A., Manivannan, P., Wahid, A., Farooq, M., Somasundaram, H., Panneerselvam, R. (2009).
Drought stress in plants: a review on morphological characteristics and pigments composition. International Journal of Agricultural Biology, 11, 100-105
26. Kaliba, A. R. M., Verkuijl, H., & Mwangi, W. (2000). Factors Affecting Adoption of Improved Maize
Seeds and Use of Inorganic Fertilizer for Maize Production in the Intermediate and Lowland Zones of Tanzania. Journal of Agricultural and Applied Economics, 32(1), 35-47. doi: 10.1017/s1074070800027802
27. Khonje, M., Manda, J., Alene, A. D., & Kassie, M. (2015). Analysis of Adoption and Impacts of
Improved Maize Varieties in Eastern Zambia. World Development, 66, 695-706. doi: 10.1016/j.worlddev.2014.09.008
28. Kijima, Y., Otsuka, K., & Sserunkuuma, D. (2008). Assessing the impact of NERICA on income and
poverty in central and western Uganda. Agricultural Economics, 38(3), 327-337. doi: 10.1111/j.1574-0862.2008.00303.x
29. Mottaleb, K. A., Gumma, M. K., Mishra, A. K., & Mohanty, S. (2015). Quantifying production losses
due to drought and submergence of rainfed rice at the household level using remotely sensed MODIS data. Agricultural Systems, 137, 227-235. doi: 10.1016/j.agsy.2014.08.014
30. Nandal, D. S., Rai, K. N. (1986). Impact of Farm Mechanisation on Farm Productivity in Haryana. N. d.:
n. d.
31. Negatu, W. (1999). The impact of perception and other factors on the adoption of agricultural
technology in the Moret and Jiru Woreda (district) of Ethiopia. Agricultural Economics, 21 (2), 205-216. doi: 10.1016/s0169-5150(99)00020-1
32. Nata, J. T., Mjelde, J. W., & Boadu, F. O. (2014). Household adoption of soil-improving practices and
food insecurity in Ghana. Agriculture & Food Security, 3(1). doi: 10.1186/2048-7010-3-17
33. Obasi, G. (1994). WMO's role in the international decade for natural disaster reduction. Bulletin of
the American Meteorological Society, 75(9), 1655-1661.
34. Ogada, M. J., Mwabu, G., & Muchai, D. (2014). Farm technology adoption in Kenya: a simultaneous
estimation of inorganic fertilizer and improved maize variety adoption decisions. Agricultural and Food Economics, 2(1). doi: 10.1186/s40100-014-0012-3
35. Ouma, J., Murithi, F., Mwangi, W., Verkuijl, H., Gethi, M., De Groote, H. (2002). Adoption of Maize Seed
and Fertilizer Technologies in Embu District, Kenya. Retrieved from
https://repository.cimmyt.org/bitstream/handle/10883/904/447957.pdf?sequence=1&isAllo wed=y
36. Rahman, A. T. M. S., Kamruzzaman, M., Jahan, C. S., Mazumder, Q. H., & Hossain, A. (2016).
Evaluation of spatio-temporal dynamics of water table in NW Bangladesh: an integrated approach of GIS and Statistics. Sustainable Water Resources Management, 2(3), 297-312. doi: 10.1007/s40899-016-0057-4
37. Selvaraju, R., Subbiah, A. R., Baas, S., & Juergens, I. (2006). Livelihood adaptation to climate
variability and change in drought-prone areas of Bangladesh. Retrieved from https://www.fao.org/3/a0820e/a0820e.pdf
38. Shafie, H., Rashid, A., Halder, S. (2009). Endowed wisdom Knowledge of nature and copingwith
disasters in Bangladesh. Retrieved from
https://www.researchgate.net/publication/328969034_2009_ENDOWED_WISDOM_Knowledge _of_Nature_and_Coping_with_Disasters_in_Bangladesh
39. Shakya, P. B., & Flinn, J. C. (1985). Adoption of modern varieties and fertilizer use on rice in the
Eastern Tarai of Nepal. Journal of Agricultural Economics, 36(3), 409-419. doi: 10.1111/j.1477-9552.1985.tb00188.x
40. Sheffield, J., Andreadis, K. M., Wood, E. F., & Lettenmaier, D. P. (2009). Global and Continental
Drought in the Second Half of the Twentieth Century: Severity-Area-Duration Analysis and Temporal Variability of Large-Scale Events. Journal of Climate, 22(8), 1962-1981. doi: 10.1175/2008jcli2722.1
41. Sodjinou, E., Glin, L. C., Nicolay, G., Tovignan, S., & Hinvi, J. (2015). Socioeconomic determinants of
organic cotton adoption in Benin, West Africa. Agricultural and Food Economics, 3(1). doi: 10.1186/s40100-015-0030-9
42. Swaminathan, M. S. (1998). Issues and Challenges in Sustainable Increased Rice Production and the
Role of Rice in Human Nutrition in the World. International Rice Comission, 98(4-2), 23.
43. Wilhite, D. A. (2000). Drought: A Global Assessment. London: Routledge.
44. Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the Drought Phenomenon: The Role of
Definitions. Water International, 10(3), 111-120. doi: 10.1080/02508068508686328
45. Yorobe, J. M., Ali, J., Pede, V. O., Rejesus, R. M., Velarde, O. P., & Wang, H. (2016). Yield and income
effects of rice varieties with tolerance of multiple abiotic stresses: the case of green super rice (GSR) and flooding in the Philippines. Agricultural Economics, 47(3), 261-271. doi: 10.1111/agec.12227
