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Hard point dataClay, silt, sandHard point dataSoil texture classesSoft point dataUFFC classesArea informationAerial photographyGeographic information systemsSoft grid dataUFFC classesSequential ISMaps of clay, silt, sandSIKMaps of texture classesGeostatistical indicator interpolationFig. 3. The outline of operations that are needed to generate mapsfrom point data using sequential indicator simulation (IS) and softindicator kriging (SIK).better reproduced by simulation than kriging, and this is important for an assessmentof soil property distribution in very heterogeneous lands.Suppose that {z*(u), uεA)} is a set of kriging estimates of the soil property Z forlocations u in a region A. The local error variance Var{Z*(u) – Z(u)} is minimum andthe best estimate in the “least-square sense” if each estimate z*(u’) is consideredindependently of its neighboring estimates. These best local estimates may not bebest as a map and, instead of generating such a kriged map, simulation produces oneor a set of possible realizations of the spatial distribution of the z* values. For example,a map of z* values {z* (l) (u), uεA}, where l denotes the lth realization, can begenerated that reproduces the statistics of an area that are most important for theproblem at hand. Simulated maps honor data values at their locations and reproduceclosely the declustered sample histogram and the set of covariance models for variousindicator thresholds (Goovaerts 1997).In this study, indicator variograms were modeled for the nine deciles of quantitativesoil properties and for the classes of qualitative properties. Estimates of mean(Figs. 4A–6A), median (Figs. 4B–6B), conditional variance (Figs. 4E–6E), and conditionalCV (Figs. 4F–6F) and the 0.2- and 0.8-quantiles (Figs. 4C,D–6C,D) of theconditional cumulative distribution function were mapped. Quantile maps are oftenmore useful than maps showing mean values; for instance, overestimation of soilnutrients in regions with low soil fertility is more harmful (yield loss or crop failure)than underestimation (undue application of treatments). Conversely, underestimationof nutrient contents has negative impacts in very fertile regions. In this situation,excess fertilizer nutrients are discharged in the groundwater and have detrimentaleffects on ecosystems. Using quantile maps, one can over- or underestimate the truevalues and minimize risks of environmental pollution or crop failure (see Figs. 4–6).Perception, understanding, and mapping of soil variability . . . 87
A. Clay (%)B. 0.5 ccdfC. 0.2 ccdf C. 0.8 ccdfE. Con. Var. F. Con. CVFig. 4. Applying estimators to probabilities of occurrence (obtained using sequential indicatorsimulation) for created maps of clay (topsoil). Map A shows the mean value and was generatedusing the e-type estimator. Quantile estimators were applied to produce maps B, C, and D thatshow the values at the 0.5-, 0.2-, and 0.8-quantile of the conditional cumulative distributionfunction (ccdf), respectively. Estimates can be interpreted as the value that has 50% (B), 80%(C), or 20% (D) probability of being exceeded by the true value in the field. Maps E and F depictthe spread about the mean at each location as the conditional variance (map E; Con. Var.) andin relative terms as the conditional coefficient of variation (map F; Con. CV).88 Oberthür 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
Page 38 and 39: IRRI (International Rice Research I
Page 40 and 41: Widawsky DA, O’Toole JC. 1990. Pr
Page 42 and 43: Rice is the staple food crop for mo
Page 44 and 45: first working group to identify the
Page 46 and 47: TropicsSubtropicsTemperateSummer/wi
Page 48 and 49: ferred it for wide adoption by farm
Page 50 and 51: ing, remote sensing, and computing
Page 52 and 53: ice environments will be needed to
Page 54 and 55: Insects and diseasesInsects and dis
Page 56 and 57: The completeness of characterizatio
Page 58 and 59: Table 7. Levels and scales for agro
Page 60 and 61: Jamin JY, Andriesse W. 1993. Discus
Page 62 and 63: Tools and methodologiesfor biophysi
Page 64 and 65: Effect of climate, agrohydrology,an
Page 66 and 67: to water deficit because the crop
Page 68 and 69: for different periods of the year:
Page 70 and 71: depth scenarios (shallow, medium, a
Page 72 and 73: Water table depth (cm)500Shallow wa
Page 74 and 75: Simulated yield (kg ha -1 )6,0005,0
Page 76 and 77: Perception, understanding,and mappi
Page 78 and 79: and rice yield at regional or more
Page 80 and 81: SubareaSampling sites:GridTransectR
Page 82 and 83: was approximately 2,000 m with a to
Page 84 and 85: kg -1 in 50% of the topsoil and sub
Page 86 and 87: Table 3. Class medians were calcula
Page 90 and 91: A. Silt (%) B. 0.5 ccdfC. 0.2 ccdf
Page 92 and 93: In summary, the chosen geostatistic
Page 94 and 95: elated over slightly longer distanc
Page 96 and 97: Miura K. 1990. Genetic features of
Page 98 and 99: Rainfed lowland rice, growing with
Page 100 and 101: tively. In 1993, following dry till
Page 102 and 103: Rainfall (mm)Water table depth (cm)
Page 104 and 105: Saturated hydraulic conductivity (c
Page 106 and 107: in the uncompacted C0 plots (6.7 wk
Page 108 and 109: Clay classified≤ 2% 50 ha (2% but
Page 110 and 111: NotesAuthors’ addresses: D. Harnp
Page 112 and 113: target area of a breeding program.
Page 114 and 115: which is calculated from the estima
Page 116 and 117: hence grain yield. The three major
Page 118 and 119: ainfall data for each location. Mea
Page 120 and 121: such as 1980 and 1996, yield was hi
Page 122 and 123: Table 1 shows the results of the se
Page 124 and 125: Grain yield (t ha -1 )3UBN 19942103
Page 126 and 127: decreased with the use of old seedl
Page 128 and 129: The use of five genotypes with diff
Page 130 and 131: Mackill DJ, Coffman WR, Garrity DP.
Page 132 and 133: In rainfed agriculture, crop perfor
Page 134 and 135: GrainSite Year Code Group yield San
Page 136 and 137: 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
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Table 1. Total rice area, average y
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use of water by avoiding seedbed pr
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water often brought with it a varie
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Table 4. Distribution of farm house
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Soil types found in the villages we
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Table 10. Ownership of agricultural
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in a group) at the rate of $60 per
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Table 13. Pattern of loan use (% of
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Labor and employmentTable 16 presen
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Table 17. Incidence of child labor
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Cumulative proportion of income1.04
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Table 21. Cost and return analysis
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Table 22. Estimated rice yield and
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were often burned for disease contr
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landless population reaching 50-60%
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Integration of biophysical andsocio
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opinion regarding what socioeconomi
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1995-96 Rice production (t)TvRd6Rd1
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Farm-gate price of agricultural goo
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ming the distance of all line segme
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Description of the study areaWe com
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Table 1 continued.Variable Unit1994
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CambodiaFig. 4. Main transport rout
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Table 2. Accessibility indicators f
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ice and the model is applied to exp
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Table 3. Summary of estimates of la
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estimation of a system of multinomi
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The availability of low-saline irri
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Table 5. Simulation of effects of i
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NotesAuthors’ address: Social Sci
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that they synthesize fragmented agr
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Framework for exploratory and predi
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and quantify the spatial distributi
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Seventeen different agricultural pr
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self-sufficiency in rice production
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al warming since maintaining or cre
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eturns to purchased inputs due to u
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Francisco SR, Navarrete Jr. RL, Dim
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Wade L. 1998. Nutrient research on
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