Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

damen, m. c. j.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856641294

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: IX Seiten, Seiten 551-956

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A,. A. Balkema

Identifier (digital):: 856641294

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,2)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 8 Geo-information systems. Chairman: J. J. Nossin

Write comment:: Wegen zu enger Bindung kommt es teilweise im Original zu Textverlust.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The CRIES Resource Information System: Computer-aided spatial analysis of resource development potential and development policy alternatives. Gerhardus Schultink

Document type:: Multivolume work

Structure type:: Chapter

937 Table 2. SUMMARY DESCRIPTION OF THE CRIES-AGRO- ECONOMIC INFORMATION SYSTEM - MULBUD ty 23 DEPARTMENT Atr Val Atr Val 4 5 precipitation, relative humidity, wind velocity, solar radiation, sowing dates, crop selection, length of growing stages, soil texture, relevant water table characteristics on the root zone, and root growth over various growing stages. 98481 { 1394 J ; : loo.oo : loo.oo ¡ 22.16 : 0.32 : 22.16 ; 0.32 : 51512 I 494 : 52.31 ! 35.44 ! 57.42 1 0.56 ! 11.59 1 0.12 ! 36381 1 900 ! 36.95 I 64.57 J 43.69 S 1.09 ! 8.19 i 0.21 : 8419 ! 0 8.55 ! 0.00 4.56 ¡ 0.00 1.90 ! 0.00 2169 i 0 2.21 S 0.00 2.51 J 0.00 0.49 ! 0.00 If the data base is not established, the user is provided with a data management option facilitating data input and editing for selected locations. Data requirements include: location identification, temperature, precipitation, relative humidity, wind velocity, solar radiation, sowing dates, crop selection, length of growing stages, soil texture, relevant water table characteristics on the root zone, and root growth over various growing stages. In combination with other CRIES-RIS modules and the established data base, the YIELD model provides the user with the analytical framework to evaluate physical and socio economic attributes by location, and determine the comparative advantage of sites for cropping alternatives. PORTION OF TWO- RAINFALL AND The CRIES-AIS-YIELD module was developed to provide agronomists, land use planners, resource managers and policy analysts with a low cost, micro computer-based analysis tool in resource assessment studies. A summary description of the model is provided belok (Table 1): Table 1. SUMMARY DESCRIPTION OF THE CRIES-AGRO- ECONOMIC INFORMATION SYSTEM - YIELD rease the grid e cells in user- es raster files m e n t s (under ut of the CRIES оVided below: C INFORMATION sleeted modules 'ormation Systen T or AT micro- jerating systei point processor, lULBUD modu 1 es, d from "Yield bos and Kassai . was developed ws (1985) and >del. ity to predict nb er of food and d locations and mode 1, can be >ase containing yield response ica1 zones or tes. If the data iser is provided n facilitating for selected ents include: temperature. (CRIES-AIS-YIELD) • MODIFIED AFTER D00RENB0S ET AL. "YIELD RESPONSE TO WATER" FAO • INCORPORATES THE "WAGENINGEN METHOD" (BASED ON SLABBERS AND THE WIT) FOR ALFALFA, MAIZE, SORGHUM AND WHEAT. • INCORPORATES THE AGR0-ECOLOGICAL ZONE METHOD FOR LARGE AREA YIELD PREDICTIONS OF 26 CROPS. « COMPUTER MODEL PREDICTS YIELD RESPONSE TO WATER AVAILABILITY UNDER RAIN-FED OR IRRIGATED CONDITIONS. • PROVIDES QUANTIFICATION OF VARIOUS CROP YIELD FOR AGRO-ECOLOGICAL ZONES • MODEL HAS FIVE PHASES (FOUR CURRENTLY IMPLEMENTED) -PHASE 1: DETERMINATION OF MAXIMUM POTENTIAL YIELD (Ym) OF ADAPTED CROP VARIETY ASSUMING NO LIMITING GROWTH FACTORS (E.G. WATER, FERTILIZER, PESTS, AND DISEASES) -PHASE 2: CALCULATION OF MAXIMUM EVAPOTRANSPIRATION (ETm-PENMAN) WHEN CROP WATER REQUIREMENTS ARE FULLY MET BY AVAILABLE WATER SUPPLY. -PHASE 3: COMPUTATION OF ACTUAL EVAPOTRANSPIRATION (ETa) BASED ON FACTORS AFFECTING CROP WATER AVAILABILITY. -PHASE 4: CALCULATION OF ESTIMATED YIELD (Ye) BY EVALUATING CROP WATER REQUIREMENTS, WATER SUPPLY AND RESPONSE FACTORS AT VARIOUS STAGES OF PLANT DEVELOPMENT. •INCLUDES THE FOLLOWING CROPS: ALFALFA, BANANA, BEAN, CABBAGE, CITRUS, COTTON, GRAPE, GROUNDNUT, MAIZE, OLIVE, ONION, PEA, PEPPER, PINEAPPLE, POTATO, RICE, SAFFLOWER, SORGHUM, SOYBEAN, SUGARBEET, SUGARCANE, SUNFLOWER, TOBACCO, TOMATO, WATERMELON, AND WHEAT. 5.2 The CRIES - AIS - MULBUD Model. Farming systems, representing land utilization options and regional or national aggregates, need to be evaluated with regards to their optimum performance characteristics and resulting socio-economic benefits derived under alternative land use and development policy scenarios. Typical production options in developing countries can be characterized as variations of mixed cropping systems, representing multiple enterprises and their (CRIES-AIS-MULBUD) * ADAPTED FROM ETHERINGTON AND MATTHEWS, 1984 * PERMITS ECONOMIC APPRAISAL OF A SINGLE FARMING SYSTEM CHARACTERIZED BY MULTIPLE AND INTERCROPPING PRACTICES AS TYPICALLY FOUND IN THE (SUB)TROPICS. * PERMIT THE EVALUATION OF LAND USE/PRODUCTION OPTIONS OVER A PERIOD OF TIME BASED OF ANNUAL AND PERENNIAL CROPS, AGRO-FORESTRY SYSTEMS AND LIVESTOCK PRODUCTION OPERATED AS A MULTIPLE ENTERPRISE ON A SINGLE LOCATION (FARMING SYSTEM UNIT). • MODEL INPUTS INCLUDE: LABOR OPERATIONS AND COSTS, WAGE RATES AND LABOR AVAILABILITY, DISCOUNT RATES, TERMINAL VALUES, FERTILIZER, PLANT MATERIAL, CHEMICALS, PRODUCT TYPES AND PRICES, LAND USE INDICES. • MODEL OUTPUTS: GRAPHICAL DISPLAY OF LABOR REQUIREMENTS AND COSTS, MATERIAL REQUIREMENTS AND VARIABLE COSTS, GROSS AND NET REVENUES, NET REVENUE PER LABOR UNIT, SUM OF NET PRESENT VALUES, SENSITIVITY ANALYSIS FOR VARIABLE DISCOUNT RATES, INTERNAL RATE OF RETURNS, PROFILE OF LAND USAGE WITH TIME. Table 3. CRIES-AIS-YIELD model, Example of Phase I Output for Bananas. OUCS - MKKCONOHIC INFORMATION SYSTEM YIELD GENERATOR (CRIES - AIS - YIELD) MICHIGRN STATE UNl^RSITY, EAST LANSING, Ml 48623 COUNTRY NATO JAMAICA CROP TYPE: BANANA TROPICAL LATITUDE: 18.01(4*1) POL./ACM. DISTRICT: ST. CADOIIC TEAR OF ORIGIN: 1*2 HEMISPHERE: NORTTCRN ICATTCR STATION NAfC: WORTHY PARK GROWTH PERK»: 2/15/1*2 TO 12/30/1*2 AWRAGE ALT I TUBE: 59.06U) RESOURCE PROO UNIT OR PROO POTENTIAL (BUT: 1.12 TINE INCREMENTS (<T): 3 (*ys> AGRO-ECOLOGICAL KBC: 11 PHASE t: DETERMINATION OF MAXIMUM POTENTIAL YIELD (Yu). T HTH/DAY GROSS DRY MATTER PRODUCTION STANDARD CROP - (k?/lu) GROSS DRY NATTER PRODUCTION POTENTIAL YIELD (DAYS) ни t*Ul «« « *v*rc**t <Uy « *« cl**r 4*y w « ******** cMT«ctl«fls §***««*»**** ******* (kg/Ht) h*w <*bs«l (cm* Ubsol (cum (tbs*I < cum (pr<xi (cleud (1«tf (iwt (lurvt (*bs*lut< <cum cb»**) clung«) dung*) dung«) dung«) dung«) r*t*) g*rc) *f** f) f) U4«x) dung«) dung*) 5 2/15 2341. 10 2/20 2383. 15 2/25 2426. 20 3/2 2469. 25 3/7 2512. 30 3/12 2556. 35 3/17 2599. 40 3/22 2641. 45 3/27 2684. 50 4/ 1 2728. 55 4/6 2771. 60 4/11 2815. 65 4/16 2824. 70 4/21 2810. 75 4/26 2795. 80 5/ 1 2779. 15 5/6 2763. 90 5/11 2746. 95 5/16 2770. 100 5/21 2821. 105 5/26 2873. 110 V31 2925. 115 6/ 5 2977. 120 6/10 3030. 125 6/15 эоео. 130 6/20 3129. 135 6/25 3179. 140 6/30 3221. 2341. 994. 4724. 1011. 7150. 1028. 9619. 1044. 12132. 1061. 14687. 1078. 17286. 1094. 19927. 1109. 22611. 1124. 25339. 1138. 281 И). 11S3. 30925. 1168. 33750. 1179. Э6560. 1186. 39354. 1194. 421Э4. 1202. 44896. 1209. 47643. 1217. 50413. 1222. 53234. 1224. 56108. 1227. 59032. 1229. 62009. 1232. 65039. 1234. 68119. 1235. 71248. 1234. 74426. 1234. 77647. 1233. 994. 1901. 2005. 1929. 3032. 1956. 4077. 1984. 5138. 2011. 6216. 2039. 7310. 2064. 8419. 2088. 9542. 2112. 10681. 2136. 11834. 2160. 13002. 2183. 14181. 2202. 15367. 2216. 16561. 2231. 17763. 2245. 18972. 2260. 20189. 2274. 21411. 2283. 22635. 2289. 23861. 2294. 25090. 2299. 26322. 2305. 27556. 2310. 28791. 2311. 30025. 2309. 31259. 2307. 32492. 2Э05. 1901. 50.11 3830. 50.78 5786. 51.44 7770. 52.11 9782. 32.78 11820. Я. 44 13885. 54.11 15973. 54.78 18085. 55.44 20220. 56.11 22390. 56.78 24563. 57.44 26765. 57.93 28*1. 58.41 31212. 58.89 33457. 59.37 35717. 59.85 37992. 60.33 40275. 60.70 42564. 61.07 44858. 61.44 47157. 61.81 49462. 62.19 51772. 62.56 54083. 63.30 56392. 64.04 58699. 64.78 61004. 65.00 .48 .48 .50 .49 .48 .50 .Я .48 .50 .50 .48 .50 .51 .48 .50 .51 .48 .50 .32 . 48 .50 .52 .48 .50 •Я .48 .50 .Я .48 .30 .Я .48 .50 .54 .48 .50 .Я .48 .50 .Я .48 .50 .59 . 48 .50 .61 .48 .50 .62 .48 .50 .64 . 48 .50 .63 .48 .50 .62 .48 .50 .61 .48 .Я .59 . 48 . 50 .58 .48 .50 •Я .48 .50 .56 . 48 .Я .Я .48 .Я .54 . 48 .Я .Я .48 .Я Я. Я. Я. 113. 61. 353. 62. 415. 63. 478. 64. 543. 65. 606. 67. 675. 68. 742. 68. 810. 67, 877. 67. 945. 67. 1011. 66. 1078. 66. 1143. 66. 1210. 68. 1278. 69. 1347. 70. 1417. 71. 1488. 73. 1Я1. 74. 1635. 75. 1710. 76. 1786. 77. 1864. products produced over a period of time. The MULBUD model (Etherington and Matthews, 1985) permits appraisal of land use options over a period of time and their derived socio economic benefits on a sustained basis. A summary description of the MULBUD model is provided (Table 2). Important features of MULBUD include the ability to: specify variable cost <>f inputs (labor and materials) and total projet value by time period/production cycle (stason and year) for selected enterprises, produce graphical displays of seasonal labor requirements versus net revenues generated, conduct sensitivity analysis on discount rates and changes in material costs and gross revenue, and provide summaries of variable costs, returns and net present values at user-defined discount rates.

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