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Table 3. Summary of estimates of land use using the random effects probit estimator.Cultivates Cultivates Farm cultivatesLeft-hand-side dependent variable more than nonrice fruit or otherone crop y –1 crop(s) treesEstimation coefficient 1994-97 a 1994-97 1994-97(estimated standard error) (n = 436) (n = 436) (n = 436)Land-labor ratio on farm 1.204** –1.058* 0.056(ha per household laborer) (0.559) (0.578) (0.591)Age of head of household 0.009* –0.050*** 0.014**(0.005) (0.014) (0.007)Farm used short-duration 1.438*** –0.131 –0.018modern variety seed(s) (0.343) (0.670) (0.452)Farm used medium- or long-duration –0.218 –2.221*** –0.334modern varieties (0.317)*** (0.775) (0.364)Farm invested in land leveling 0.664 –0.116 –0.902**or other soil improvement (0.462) (0.467) (0.450)Farm invested in construction 0.031 0.646* –0.769*of dikes or irrigation (0.297) (0.381) (0.449)Rho 0.703*** 0.787*** 0.970(0.153) (0.225) (0.081)Goodness of fit diagnostics:Pseudo R 2 : Cragg-Uhler 0.085 0.130 0.447Maddela 0.046 0.064 0.317McFadden 0.060 0.098 0.309Likelihood ratio (X 2 ) test 20.653*** 28.693*** 166.549***[degrees of freedom] [1] [1] [1]Percentage correctly predicted 0.812 0.842 0.672Actual/predicted 0 1 total 0 1 total 0 1 total0 0 82 82 342 17 359 107 126 2331 0 354 354 52 25 77 66 137 203total 0 436 436 394 42 436 173 263 436a *** = estimated coefficient is statistically significant at 99% confidence level, ** = estimated coefficient isstatistically significant at 95% confidence level, * = estimated coefficient is statistically significant at 90%confidence level.The variables considered are the land-labor ratio on the farm (area per full-timeequivalent family worker), the age of the head of household, a series of dummy variablesindicating the main rice variety cultivated (the omitted variety classes are glutinousand traditional nonglutinous rice varieties), and dummy variables indicatingfarm investment in dikes or land leveling. It is expected that households with lowerland-labor ratios will adopt more labor-intensive land use. It is argued that older farmoperators are more traditional and hesitant to adopt new technologies and changetheir agricultural practices. Their reluctance may also reflect their lower levels ofeducation.Integration of biophysical and socioeconomic constraints . . . 461
Table 4. Summary of estimates of rice-cropping intensity using the ordered probit estimator forcross-sectional data.Left-hand-side/dependent Rice-cropping Rice-cropping Rice-cropping Rice-croppingvariable intensity intensity intensity intensityEstimation coefficient in 1994 a in 1995 in 1996 in 1997(estimated standard error) (n = 60) (n = 114) (n = 114) (n = 77)Constant 7.2873 –17.884*** 16.599*** –34.870***(5.559) (4.217) (4.678) (11.088)Average distance between 0.0011 –0.131 –0.062 –0.025homestead and plot or plots (0.118) (0.138) (0.139) (0.185)Average travel time to all 0.0323 –0.041*** 0.020*** –0.060***accessible local markets (0.033) (0.011) (0.007) (0.027)Land-labor ratio on farm 0.0308 0.689 0.356 –0.100(ha per household laborer) (0.992) (0.521) (0.464) (0.834)Years since family settled in 0.0064 –0.007 –0.003 –0.012current place of residence (0.011) (0.007) (0.007) (0.014)Maximum educational attain- 0.0020 0.311 0.071 0.526ment of any family member (0.532) (0.296) (0.294) (0.461)Whether farm served by good- 2.2076 2.053*** 4.266*** 4.704***quality irrigation system (1.513) (0.466) (0.843) (1.668)Annual precipitation where –0.0091 0.013*** –0.011*** 0.025***farm is located (0.006) (0.003) (0.003) (0.008)Mu (threshold parameter for 0.3267** 2.208*** 1.801*** 4.765**first stratum) (0.149) (0.330) (0.309) (2.182)Goodness of fit diagnostics:Pseudo R 2 : Cragg-Uhler 0.432 0.633 0.521 0.768Maddela 0.361 0.555 0.460 0.654McFadden 0.248 0.386 0.288 0.556Likelihood ratio (X 2 ) test 26.862*** 92.256*** 70.189*** 81.707***[degrees of freedom] [7] [7] [7] [7]Percentage correctly predicted 0.800 0.719 0.632 0.805Actual/ 0 1 2 total 0 1 2 total 0 1 2 total 0 1 2 totalpredicted0 37 0 0 37 21 12 0 33 18 13 0 31 6 6 0 121 6 0 1 7 4 45 6 55 5 35 11 51 2 38 5 452 5 0 11 16 0 10 16 26 0 13 19 32 0 2 18 20total 48 0 12 60 25 67 30 114 23 61 30 114 8 46 23 77a *** = estimated coefficient statistically significant at 99% confidence level, ** = estimated coefficient statisticallysignificant at 95% confidence level, * = estimated coefficient statistically significant at 90% confidencelevel.The rice variety planted by farms clearly influences the number of rice crops itis possible for the farm to cultivate. The two dummy variables used in the reportedestimates define farms growing short-duration modern varieties and medium- or longdurationmodern varieties. Rice variety choice is endogenous with the choice of croppingpattern, so estimates are open to endogeneity bias under the present specification.Unfortunately, neither the data nor the estimation procedures available enable462 Edmonds and Kam
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The International Rice Research Ins
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Monitoring rainfed and irrigated ri
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ForewordRainfed rice areas are asso
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AcknowledgmentsIRRI is most gratefu
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2 Singh et al
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development, for example, provision
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subecosystems were recognized withi
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Table 3. Methods commonly used in e
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Table 4 continued.Scale of analysis
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Table 4 continued.Scale of analysis
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This included standard countrywide
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deepwater rice ecosystems. The rese
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Table 5 continued.Main MethodologyC
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Table 5 continued.Main MethodologyC
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Table 5 continued.Main Scale presen
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levels of involvement. For example,
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●●●ecosystem classifications
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Conclusionsclearly that they are di
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IRRI (International Rice Research I
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Widawsky DA, O’Toole JC. 1990. Pr
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Rice is the staple food crop for mo
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first working group to identify the
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TropicsSubtropicsTemperateSummer/wi
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ferred it for wide adoption by farm
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ing, remote sensing, and computing
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ice environments will be needed to
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Insects and diseasesInsects and dis
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The completeness of characterizatio
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Table 7. Levels and scales for agro
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Jamin JY, Andriesse W. 1993. Discus
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Tools and methodologiesfor biophysi
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Effect of climate, agrohydrology,an
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to water deficit because the crop
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for different periods of the year:
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depth scenarios (shallow, medium, a
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Water table depth (cm)500Shallow wa
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Simulated yield (kg ha -1 )6,0005,0
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Perception, understanding,and mappi
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and rice yield at regional or more
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SubareaSampling sites:GridTransectR
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was approximately 2,000 m with a to
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kg -1 in 50% of the topsoil and sub
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Table 3. Class medians were calcula
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Hard point dataClay, silt, sandHard
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A. Silt (%) B. 0.5 ccdfC. 0.2 ccdf
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In summary, the chosen geostatistic
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elated over slightly longer distanc
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Miura K. 1990. Genetic features of
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Rainfed lowland rice, growing with
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tively. In 1993, following dry till
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Rainfall (mm)Water table depth (cm)
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Saturated hydraulic conductivity (c
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in the uncompacted C0 plots (6.7 wk
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Clay classified≤ 2% 50 ha (2% but
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NotesAuthors’ addresses: D. Harnp
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target area of a breeding program.
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which is calculated from the estima
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hence grain yield. The three major
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ainfall data for each location. Mea
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such as 1980 and 1996, yield was hi
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Table 1 shows the results of the se
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Grain yield (t ha -1 )3UBN 19942103
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decreased with the use of old seedl
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The use of five genotypes with diff
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Mackill DJ, Coffman WR, Garrity DP.
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In rainfed agriculture, crop perfor
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GrainSite Year Code Group yield San
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Criteria considered for selecting r
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Table 1. Characteristics of the ref
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equired as the quality of the repro
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Cooper M, Rajatasereekul S, Immark
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144 Amien and Las
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As Indonesia’s staple food, rice
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Soil and climate of rainfed lowland
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isky when the crop is planted late.
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The decrease in harvested area only
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than in tidal swamp rice, for which
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Monitoring rainfed and irrigated ri
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and, indirectly, methane emission f
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Radar backscatter (dB)-7ERS windsca
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ERS SAR imageseries2. RSpreprocessi
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Fig. 6. Supervised classification o
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ConclusionsMonitoring of rice crops
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Characterizing soil phosphorusand p
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Table 1. Area and distribution of s
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Table 2. Lowland area (ha) classifi
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Fig. 1. Extractable soil P map (25%
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Characterizing soil phosphorus and
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Table 4. Recommendations for P appl
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Fig. 4. Extractable soil K map of t
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Fig. 5. Extractable soil K map of t
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● acidic pH (< 6)● low organic
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trials, K recommendations for uplan
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O’Brien DT, Blair G, Lefroy R. 19
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Planning and managing rice farmingt
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lowlands is submergence-prone. Ther
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situations and the allocation of re
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estimation of drought were determin
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Table 2. Effect of date of sowing,
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from October to February and a summ
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Table 3. Promising areas for ground
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1. There are possibilities of growi
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mmmm450350250150ASoil moisture useS
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Table 9. Yield a of preflood ahu (a
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Grain yield (t ha -1 )6Farmers’ l
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ReferencesIRRI (International Rice
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network (Sastry 1976). Temperature
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Temperature (°C) and solar radiati
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Table 1. Characterization of rainfa
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in eastern Uttar Pradesh under norm
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fluctuations/variations in rainfall
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the desirable rainfall period at 50
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The radiation values range from les
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not affect the growth and developme
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Agrohydrologic and drought riskanal
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Analytical procedureThe nature and
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late because of low rainfall at the
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Probability (%)10080604020Vegetativ
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15 August), which may happen twice
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texture), then once in two years (5
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Characterizing biotic stresses
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Characterizing biotic constraintsto
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included in these studies. Later st
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Average number of insects per sweep
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Table 2. Categorization of weed and
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Table 5. Properties of individual c
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ResultsRelations among pests and cr
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Start hereInterview farmers todocum
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Table 10. Information used to prior
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Interpreting the results of indepen
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level. Again, we augment our databa
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NotesAuthors’ address: Cambodia-I
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in soil nutritional status, especia
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flora. The range in elevation acros
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Table 1. Soil characteristics of th
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Table 3. Weed species recorded and
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Proportional abundance (log scale)%
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1.0Echi_colCype_iriMoll_penLind_spp
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+3.0Echi_cruMars_creEcli_albLept_ch
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indicates the need to deploy broad-
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ReferencesBangun P, Pane H, Jatmiko
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Socioeconomic characterization
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The role of characterizationin ex a
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ApproachThe following steps were ta
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PriceDS 0S 1P 0P 1dS 0S 1acbD0 Q 0Q
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Table 1. Ex ante assessment of rese
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Table 2 continued.Ranked byResearch
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Table 3. Research resource allocati
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NotesAuthors’ address: National C
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In addition to the unavailability o
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Socioeconomic constraintsFarm sizeF
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found that a higher rural labor sup
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Table 3. Rice area under HYVs from
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Rice varieties in farmers’ fields
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Table 9. Constraints for low adopti
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vehicle for realizing the untapped
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Parthasarathy G, Prasad DS. 1978. R
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Appendix. Agroclimatic zones of Bih
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such that losses are reduced during
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P = 0.5 R [a 2 C r2+ 2 a (1 - a) g
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and Itgaon, however, the plot-level
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espectively, to farmers’ income.
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Factors determining the adoption of
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mining whether or not modern variet
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irrigation schemes in the study are
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Using gender analysis in characteri
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lytical tool used to identify or di
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However, household members have cer
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Table 1. Village characteristics of
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taking care of dairy cattle. On the
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The biophysical environmentThe perf
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Table 3. Seasonal calendar and gend
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Table 4. Crops grown during the kha
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Table 7. Labor input (person-days h
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Table 8. Labor input (person-days h
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Table 10. Cost and returns of rice
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Gender roles in animal husbandryWhe
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farm activities, 25% in nonfarm act
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for rice but also for cash crops su
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Table 15. Access to agricultural in
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Hossain M. 1995. Recent development
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Agricultural commercializationand l
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Conceptual frameworkPopulation pres
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tional average of 18% 1 , and most
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The Dzao live in medium-altitude ar
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Table 1. Selected districts and com
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Table 2. General characteristics of
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markets for their food needs. With
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ther “extensification” in the u
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the productivity of lowland rice ca
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Boserup E. 1981. Population and tec
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Economics of intensive rainfed lowl
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Area (ha)50,00040,000Irrigated30,00
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The economic characterization and i
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Table 1. Rice varieties planted (19
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Table 4. Yield and input use of maj
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TFP, output and input indices200160
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Lynam JK, Herdt RW. 1989. Sense and
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406 Garcia et al
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Increased rice production had alway
Page 402 and 403: Table 1. Total rice area, average y
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Page 408 and 409: Table 4. Distribution of farm house
Page 410 and 411: Soil types found in the villages we
Page 412 and 413: Table 10. Ownership of agricultural
Page 414 and 415: in a group) at the rate of $60 per
Page 416 and 417: Table 13. Pattern of loan use (% of
Page 418 and 419: Labor and employmentTable 16 presen
Page 420 and 421: Table 17. Incidence of child labor
Page 422 and 423: Cumulative proportion of income1.04
Page 424 and 425: Table 21. Cost and return analysis
Page 426 and 427: Table 22. Estimated rice yield and
Page 428 and 429: were often burned for disease contr
Page 430 and 431: landless population reaching 50-60%
Page 432 and 433: Integration of biophysical andsocio
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Page 436 and 437: 1995-96 Rice production (t)TvRd6Rd1
Page 438 and 439: Farm-gate price of agricultural goo
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Page 442 and 443: Description of the study areaWe com
Page 444 and 445: Table 1 continued.Variable Unit1994
Page 446 and 447: CambodiaFig. 4. Main transport rout
Page 448 and 449: Table 2. Accessibility indicators f
Page 450 and 451: ice and the model is applied to exp
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Page 456 and 457: The availability of low-saline irri
Page 458 and 459: Table 5. Simulation of effects of i
Page 460 and 461: NotesAuthors’ address: Social Sci
Page 462 and 463: that they synthesize fragmented agr
Page 464 and 465: Framework for exploratory and predi
Page 466 and 467: and quantify the spatial distributi
Page 468 and 469: Seventeen different agricultural pr
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Page 472 and 473: al warming since maintaining or cre
Page 474 and 475: eturns to purchased inputs due to u
Page 476 and 477: Francisco SR, Navarrete Jr. RL, Dim
Page 478: Wade L. 1998. Nutrient research on
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