Climate change and agriculture land use changes in the form of cropping patterns are closely linked with crop water use. In this study the SDSM (statistical downscaling model) was used to downscale and simulate changes in meteorological parameters from 1961 to 2099 using HadCM3 General Circulation Model (GCM) data under two selected scenarios i.e., H3A2 and H3B2. Results indicated that Tmax, Tmin, and wind speed may increase while relative humidity and precipitation may decrease in the future under both H3A2 and H3B2 scenarios. Downscaled meteorological parameters were used as input in the CROPWAT model to simulate crop irrigation requirement (CIR) in the baseline (1961–1990) and the future (2020s, 2050s and 2080s). Data related to agriculture crop sown area of five major crops were collected from Punjab statistical reports for the period of 1981–2015 and forecasted using linear exponential smoothing based on the historical rate. Results indicated that the cropping patterns in the study area will vary with time and proportion of area of which sugarcane, wheat, and rice, may exhibit increasing trend, while decreasing trend with respect to the baseline scenario was found in maize and cotton. Crop sown area is then multiplied with CIR of individual crops derived from CROPWAT to simulate Net-CIR (m3) in three sub-scenarios S1, S2, and S3. Under the H3A2 scenario, total CIR in S1, S2, and S3 may increase by 3.26 BCM, 12.13 BCM, and 17.20 BCM in the 2080s compared to the baseline, while under the H3B2 scenario, Net-CIR in S1, S2, and S3 may increase by 2.98 BCM, 12.04 BCM, and 16.62 BCM in the 2080s with respect to the baseline. It was observed that under the S2 sub-scenario (with changing agriculture land-use), total CIR may increase by 12.13 BCM (H3A2) and 12.04 BCM (H3B2) in the 2080s with respect to the baseline (1961–1990) which is greater as compared to S1 (with changing climate). This study might be valuable in describing the negative effects of climate and agriculture land use changes on annual crop water supply in Rechna Doab.

Long-Term Perspective Changes in Crop Irrigation Requirement Caused by Climate and Agriculture Land Use Changes in Rechna Doab, Pakistan

Modeling Effects of Different Irrigation and Fertigation Strategies on Maize (Zea Mays) Response And Salinity Buildup In Root Zone Under Drip Irrigation

This paper examines agricultural policies in 18 developing countries over the period 1961-1985. We measure productivity with both a nonparametric Malmquist index and a production function, confirming previous findings of declining agricultural productivity, but with sufficident inconsistencies as to raise concern about the adequacy of the methods. We nontehless find considerable support for the hypothesis that unfavorable price policies have damaged agricultural performance in these countries.

Have Price Policies Damaged LDC Agricultural Productivity

Success of precision farming practices requires knowledge of fields such as soil type, topography, soil nutrients, spatial variability effects, yield patterns and their spatial relationships. A three year (2008-09 to 2010-11) field experimental study was conducted at Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the influencing landscape parameters and their spatial distribution, having effects on wheat yield patterns using artificial neural network (ANN) and GIS map overlay techniques. A total of 48 soil samples were collected from top 30 cm of the soil, before sowing, at center of each grid of 24 x 67 m in size along with position data using Global Positioning System receiver (GARMIN, GPS60). Landscape attributes such as elevation, %sand, %silt, %clay, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus were included in the analysis. ANN analysis revealed that urea fertilizer treatments, followed by %sand, %silt, % clay, elevation, soil nitrogen and EC were ranked as the most influencing parameters. The yield data, however, were normalized to remove fertilizer treatments effects and then were used in the subsequent analysis. The map overlay analysis showed that the areas having lower elevation, lower soil EC and higher levels of soil N produced higher yields. Whereas the areas having higher elevation, higher soil EC and moderate soil N produced lower yields, establishing the cause-effect relationships. These results indicated that ANN and GIS techniques were helpful in identifying the influencing parameters affecting wheat yield, which can be managed under precision farming practices.

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Spatial Relationships of Landscape Attributes and Wheat Yield Patterns

This study was aimed at investigating the factors influencing production of banana for Uba Debre Tsehay Woreda, Gammo Gofa zone of SNNPRS with the specific objectives of identifying and analyzing the factors influencing production of banana in the study area. In order to attain these objectives the study made use of primary and secondary data. The data were generated by individual interview schedules and focus group discussions using pre-tested semi-structured questionnaires and checklists respectively. This was supplemented by secondary data. Descriptive and the Cobb-Douglass production function were used to analyze the socio-economic factors affecting banana production in the area. The study has identified the main factors affecting banana yield. The regression results showed that level of education, household size and fertilizer were positively related output at 1% and 5% level of significance whilst experience in banana production and price were negatively related to output at 10% and 1% level of significance. The farmers were also faced other constraints as obtained from FGD’s like lack of market access, distance to market and lack of road to the main outlets. Therefore extension work should focus on encouraging farmers to use the household productive labour force efficiently, and inputs like fertilizer should be provided to increase the yield of banana. Again the findings suggest that effective market information service and infrastructure development access has to be established to overcome the fluctuating price. Keywords: Socio-Economic factors, Banana Production, Uba Debretsehay Woreda

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Analysis of Socio-Economic Factors Affecting Banana Production: Evidences from Lowlands of Uba Debretsehay Woreda, Gamo Gofa Zone, SNNPRS

The study aims to determine the effect of Agriculture Policy on wheat production in Pakistan. The effect of change in tax/subsidy on agricultural productivity depends upon their respective sensitivities, which is reflected through the value of Nominal Protection Coefficient (NPC). NPC is a ratio between border prices and prices received by the local producer. Using the data for the period 1971-2006, the study concludes that the government policy has insignificant effect on wheat production though the sign of its coefficient is positive. JEL Classification: H23, H24, Q18.

M. Z. Ranjha

Papers

Agricultural policy and wheat production: a case study of pakistan