Contextual data: Tanzania - Rice - All years

‹ 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 ›
Tanzania total 704,287 759,386 805,065 909,990 896,696 897,291 1,486,865 1,450,012 1,149,148 1,316,927
Arusha   8,896 31,472 52,950 55,914 57,786 48,615 136,823 117,580 203,188 103,630  
Dar-Es-Salaam   5,738 1,362 9,326 8,803 9,098 8,914 2,515 2,455 5,669 2,813  
Dodoma   3,752 2,533 2,432 2,468 2,551 2,499 8,749 7,519 6,254 8,099  
Geita   - - - - - - - - - -  
Iringa   16,055 12,770 12,259 12,442 12,859 12,598 13,982 12,016 10,035 12,944  
Kagera   6,498 32,170 26,964 31,177 32,221 31,568 8,685 7,464 5,612 8,040  
Kaskazini Pemba   - - - - - - - - - -  
Kaskazini Unguja   - - - - - - - - - -  
Katavi   - - - - - - - - - -  
Kigoma   3,141 7,501 14,492 14,115 14,587 14,292 87,895 64,296 33,520 81,365  
Kilimanjaro   17,608 27,632 33,818 36,927 38,164 37,391 17,067 14,666 13,771 15,799  
Kusini Pemba   - - - - - - - - - -  
Kusini Unguja   - - - - - - - - - -  
Lindi   9,623 2,743 11,250 11,938 12,338 14,188 19,997 17,184 11,138 18,511  
Manyara   8,896 18,188 20,377 20,681 21,374 20,941 2,857 2,455 1,846 2,645  
Mara   8,896 322 309 321 331 325 2,618 2,249 10,744 2,423  
Mbeya   125,648 167,255 131,408 128,390 132,689 130,001 132,269 113,666 138,288 122,444  
Mjini Magharibi   - - - - - - - - - -  
Morogoro   153,529 80,868 117,114 109,922 113,602 111,301 186,313 160,109 120,393 172,473  
Mtwara   22,847 20,006 19,207 19,493 20,146 19,738 76,427 65,678 28,813 70,750  
Mwanza   102,630 84,677 87,593 116,542 65,398 65,781 160,426 137,863 132,859 148,509  
Njombe   - - - - - - - - - -  
Pwani   11,989 19,889 33,674 29,890 30,891 61,436 61,436 262,005 41,714 61,436  
Rukwa   48,947 46,581 44,719 45,386 46,906 49,956 126,118 108,380 61,641 116,749  
Ruvuma   18,310 2,046 1,964 1,994 2,060 2,019 93,010 53,929 46,551 58,093  
Shinyanga   42,971 78,699 62,961 125,272 109,548 107,329 129,201 111,029 97,513 110,603  
Simiyu   - - - - - - - - - -  
Singida   17,563 6,227 5,250 12,345 11,163 10,937 4,734 4,069 8,519 4,383  
Tabora   59,841 108,497 109,368 119,000 155,781 140,405 162,673 139,794 157,861 146,090  
Tanga   10,909 7,948 7,630 6,970 7,203 7,057 53,070 45,606 13,219 49,128  

Data version 2.19.3

Contextual data

Collected data

Values obtained from primary and secondary sources

Approximated data

Values approximated based on other data for same location

–No data

Not applicable or no data available

LGB Larger Grain Borer (Prostephanus truncatus) read more ›

Storage beetle pest of maize and dried cassava

Contextual data

Contextual factors relate to local conditions and practices that may affect losses on a seasonal or annual basis, such as weather, pest incidence, grain drying conditions and the length of household-level storage.

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APHLIS network and team members collect this data from official sources (e.g. ministries of agriculture and statistics offices), published research studies and national surveys etc, or by interviewing farmers, extension workers or other key informants. This data enables APHLIS to apply the relevant loss figures from the PHL profiles depending on seasonal circumstances, and convert percentage losses into absolute losses (in tonnes) using the production figures. Based on the postharvest loss profiles and contextual factors, APHLIS estimates postharvest losses at the provincial, national and regional levels.

The contextual dataset includes production quantities (t), whether it rained at harvest, the percentage of the crop marketed during first 3 months after harvest, household-level storage duration, and presence of specific pests (for example the larger grain borer, LGB, Prostephanus truncatus) which are used to contextualise the loss profile for each year. The importance of different types of contextual factors may vary between crops, and data for the most relevant contextual factors is shown and used in the loss estimate calculations.

For specific crop, location, year combinations where contextual data is not available, approximated data has been used. This is calculated based on the contextualised data which has been provided for the same location. Approximated contextual data is shown in grey. 

Read more about how APHLIS estimates losses ›

Contextual factors

Production quantity (t) the quantity of grain produced on small and large farms for each growing season.

Rain at harvest whether or not there is damp weather at time of harvest which would make drying the grain difficult. If there is rain at harvest then the value for the harvesting link in the PHL profile is increased.

% sold during first 3 months the proportion of grain that is marketed within the first three months, i.e. that will not enter farm storage for any significant time. This proportion of the production will not be subject to farm storage losses but instead will be affected by transport and market storage losses.

Household-level storage duration (months) the length of time that grain was stored at household-level. If household-level storage is less than 3 months then household-level storage loss is reduced to 0%, if 3 to 6 months it will be only 50% of the loss profile figure, or if 6 months or more than 100% of the household-level storage loss is applied.

LGB presence in the case of maize, whether or not LGB (the larger grain borer, Prostephanus truncatus) is expected to be a significant pest. If LGB is a serious pest in that particular season then storage losses are multiplied by 2.

Season

In locations where there is more than one growing season each year, seasonal production data for each of the growing seasons is inputted. These are labelled as first, second, third season.

Farm type

In locations where there are both smallholder farms and large-scale commercial farms, the contextual data is shown for each of these types.

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