Figure 1.
The geographic distribution of household survey sites within the study districts of Soroti and Pallisa in eastern Uganda and the Singida district in central Tanzania. (A) The location of household survey sites in the Singida district. The survey sites are marked with orange dots, indicating the areas where household surveys were conducted. (B) The distribution of household survey sites within the Pallisa district. Similarly, the orange dots indicate the households surveyed (C) The survey sites within the Soroti district, with orange dots marking the specific survey locations. The districts represent the agricultural landscape of cereal–legume–livestock integration, where soil fertilizer resources are generated and managed within the farm as a sustainable practice for land productivity enhancement among smallholder farmers in the semi-arid regions of Eastern Africa.
Figure 1.
The geographic distribution of household survey sites within the study districts of Soroti and Pallisa in eastern Uganda and the Singida district in central Tanzania. (A) The location of household survey sites in the Singida district. The survey sites are marked with orange dots, indicating the areas where household surveys were conducted. (B) The distribution of household survey sites within the Pallisa district. Similarly, the orange dots indicate the households surveyed (C) The survey sites within the Soroti district, with orange dots marking the specific survey locations. The districts represent the agricultural landscape of cereal–legume–livestock integration, where soil fertilizer resources are generated and managed within the farm as a sustainable practice for land productivity enhancement among smallholder farmers in the semi-arid regions of Eastern Africa.
Figure 2.
Flow diagram showing nutrient resource flows in sorghum production in the Soroti district, where inflows (IN1,2,3 etc., denoted by solid arrows into the sorghum field) represent the different nutrient inputs farmers add to their fields and the outflows (OUT1,2, 3 etc.), denoted by solid arrows out of the sorghum field) show the different nutrient resource export avenues from the field. The arrows out of the homestead indicate the different outflows and fates of sorghum harvests. Dotted arrows mean that harvests taken to the homestead are subject to multiple uses or fates, including being taken out of the farm.
Figure 2.
Flow diagram showing nutrient resource flows in sorghum production in the Soroti district, where inflows (IN1,2,3 etc., denoted by solid arrows into the sorghum field) represent the different nutrient inputs farmers add to their fields and the outflows (OUT1,2, 3 etc.), denoted by solid arrows out of the sorghum field) show the different nutrient resource export avenues from the field. The arrows out of the homestead indicate the different outflows and fates of sorghum harvests. Dotted arrows mean that harvests taken to the homestead are subject to multiple uses or fates, including being taken out of the farm.
Figure 3.
Flow diagram showing nutrient resource flows in sorghum production in the Pallisa district, where inflows (IN1,2,3 etc., denoted by solid arrows into the sorghum field) represent the different nutrient inputs farmers add to their fields and the outflows (OUT 1,2,3 etc., denoted by solid arrows out of the sorghum field) show the different nutrient resource export avenues from the field. The arrows out of the homestead indicate the different outflows and fates of sorghum harvests. Dotted arrows mean that harvests taken to the homestead are subject to multiple uses or fates, including being taken out of the farm.
Figure 3.
Flow diagram showing nutrient resource flows in sorghum production in the Pallisa district, where inflows (IN1,2,3 etc., denoted by solid arrows into the sorghum field) represent the different nutrient inputs farmers add to their fields and the outflows (OUT 1,2,3 etc., denoted by solid arrows out of the sorghum field) show the different nutrient resource export avenues from the field. The arrows out of the homestead indicate the different outflows and fates of sorghum harvests. Dotted arrows mean that harvests taken to the homestead are subject to multiple uses or fates, including being taken out of the farm.
Figure 4.
Flow diagram showing nutrient resource flows in sorghum production in the Singida district, where inflows (IN1,2,3 etc., denoted by solid arrows into the sorghum field) represent the different nutrient inputs farmers add to their fields and the outflows (OUT1,2,3, denoted by solid arrows out of the sorghum field) show the different nutrient resource export avenues from the field. The arrows out of the homestead indicate the different outflows and fates of sorghum harvests. Dotted arrows mean that harvests taken to the homestead are subject to multiple uses or fates, including being taken out of the farm.
Figure 4.
Flow diagram showing nutrient resource flows in sorghum production in the Singida district, where inflows (IN1,2,3 etc., denoted by solid arrows into the sorghum field) represent the different nutrient inputs farmers add to their fields and the outflows (OUT1,2,3, denoted by solid arrows out of the sorghum field) show the different nutrient resource export avenues from the field. The arrows out of the homestead indicate the different outflows and fates of sorghum harvests. Dotted arrows mean that harvests taken to the homestead are subject to multiple uses or fates, including being taken out of the farm.
Figure 5.
TLU animals owned by different classes of manure applications per season (separated by district).
Figure 5.
TLU animals owned by different classes of manure applications per season (separated by district).
Figure 6.
The different organic nutrient fertilizer resources used by smallholder farmers in the districts of Soroti, Pallisa, and Singida to improve the fertility of their farms.
Figure 6.
The different organic nutrient fertilizer resources used by smallholder farmers in the districts of Soroti, Pallisa, and Singida to improve the fertility of their farms.
Figure 7.
The variation in the production and use of bioinoculants by farmers across three study districts. The bioinoculants are farm-made bio-inputs produced by farmers by fermenting existing organic nutrient resources such as cow dung (cow dung microbes) and forest litter (forest native microbes), composting farm residues using earthworms (vermicompost), and trapping effective soil microbes (IMOs) by burying carbohydrate-rich materials such as boiled sweet potatoes and maize meal in the soil, preferably under big trees in the farm.
Figure 7.
The variation in the production and use of bioinoculants by farmers across three study districts. The bioinoculants are farm-made bio-inputs produced by farmers by fermenting existing organic nutrient resources such as cow dung (cow dung microbes) and forest litter (forest native microbes), composting farm residues using earthworms (vermicompost), and trapping effective soil microbes (IMOs) by burying carbohydrate-rich materials such as boiled sweet potatoes and maize meal in the soil, preferably under big trees in the farm.
Figure 8.
The prevalence of different field insect pests in sorghum across the three districts of Pallisa and Soroti in Uganda and Singida in Tanzania.
Figure 8.
The prevalence of different field insect pests in sorghum across the three districts of Pallisa and Soroti in Uganda and Singida in Tanzania.
Table 1.
Social, resource, and management characterization of respondents. The upper part of the table shows the frequency (number) of respondents categorized by sex, marital status, level of education, and age across three districts (Pallisa, Singida, and Soroti). The lower part of the table gives information on animal ownership among respondents. TLU is a measure of livestock ownership based on metabolic weight, determined by multiplying numbers of each livestock category by a factor: 0.7 for cattle, 0.1 for sheep and goats, 0.2 for pigs, and 0.01 for poultry.
Table 1.
Social, resource, and management characterization of respondents. The upper part of the table shows the frequency (number) of respondents categorized by sex, marital status, level of education, and age across three districts (Pallisa, Singida, and Soroti). The lower part of the table gives information on animal ownership among respondents. TLU is a measure of livestock ownership based on metabolic weight, determined by multiplying numbers of each livestock category by a factor: 0.7 for cattle, 0.1 for sheep and goats, 0.2 for pigs, and 0.01 for poultry.
Variable | Category | Pallisa Frequency | Singida Frequency | Soroti Frequency | χ2 | p-Value |
---|
Sex | Female | 81 | 79 | 92 | 3.62, df = 2 | 0.1636 |
| Male | 63 | 53 | 45 | | |
Marital Status | Cohabiting | 1 | 0 | 1 | 10.75, df = 8 | 0.2161 |
| Divorced/Separated | 4 | 5 | 9 | | |
| Married | 133 | 111 | 113 | | |
| Single | 0 | 3 | 2 | | |
| Widowed | 6 | 13 | 12 | | |
Level of Education | A-level | 2 | 0 | 0 | 62.46, df = 10 | <0.001 |
| No education | 24 | 2 | 35 | | |
| O-level | 34 | 11 | 15 | | |
| Primary | 81 | 114 | 83 | | |
| Tertiary/University | 3 | 0 | 1 | | |
| Vocational | 0 | 5 | 3 | | |
Age | 19–30 | 20 | 13 | 16 | 7.25, df = 8 | 0.5098 |
| 31–40 | 32 | 33 | 24 | | |
| 41–50 | 48 | 41 | 43 | | |
| 51–60 | 23 | 29 | 37 | | |
| >60 | 21 | 16 | 17 | | |
| | Animal ownership | | |
Animals owned (TLU) | | 4.01 | 8.12 | 4.23 | | <0.001 |
Table 2.
Summary of farmer statements that relate to themes of crop residue management and utilization of organic resources, as well as gender aspects in the management of organic inputs for nutrient cycling in the Soroti and Pallisa districts in Uganda and the Singida district in Tanzania.
Table 2.
Summary of farmer statements that relate to themes of crop residue management and utilization of organic resources, as well as gender aspects in the management of organic inputs for nutrient cycling in the Soroti and Pallisa districts in Uganda and the Singida district in Tanzania.
Theme | Farmer Insights on the Themes |
---|
Fate of crop residues | We commonly graze cattle in the fields of cereal crops such as finger millet, sorghum, and maize by either free-range grazing or tethering. This practice also applies to residues of groundnuts, cowpeas, and green grams.
|
| - 2.
Cattle grazing in the fields is not restricted, even neighbors are free to graze because this helps to add manure to our fields.
|
| - 3.
After cattle grazing, the remaining residues, together with the deposited manure, are incorporated into the soil through plowing.
|
| - 4.
In our communities, some cereal residues are cut and carried home for cattle feeding, fuel wood, and the construction of local hats (houses). After the threshing of harvests, the residues are either used for compost-making or burnt.
|
Grazing practices for nutrient resources management | - 5.
We perform free-grazing of cattle in crop fields after harvest. “During cropping season, cattle grazing is done in the existing open non-cropped land, including wetlands”.
|
| - 6.
For farms that have shown a serious decline in soil fertility, we designate them for intensive cattle grazing for a given period—Soroti district.
|
| - 7.
Where cattle are tethered in the field, they have to be rotated at least twice in a day.
|
| - 8.
Maize stalks are very precious cattle feed and we cut them and carry them home as additional fodder during the dry season—Singida district.
|
Nutrient fertilizer resources and utilization | - 9.
Farmyard manure is what we rely on to improve soil fertility. This can be collected at homesteads and carried to fields or grazing animals deposit directly in fields.
|
| - 10.
The amount of farmyard manure applied in fields depends on quantities generated on the farm, ease of transportation to fields, and the perception of the soil fertility status of the fields.
|
| - 11.
Compost manure is mostly used by those who have the knowledge and time to make it.
|
| - 12.
Replowing crop residues is the major practice for nutrient cycling in crop fields.
|
Gender issues in soil fertility management | - 13.
Men, women, and children participate in collecting cattle manure.
|
| - 14.
Women make the decision on the burning of crop residues from cereals or any other crop after threshing.
|
| - 15.
Usually, women dispose of residues after processing in the vicinity of their homesteads and these spots are used as vegetable home gardens.
|
| - 16.
Transportation of cattle manure and application in the field is the work of men and male children.
|
Table 3.
Gamma regression analysis examining various factors that influence the amount of manure produced by farmers. The variables include district location, involvement in farmer research networks, the proportion of crop residues fed to livestock, the method of feeding crop residues to livestock, and the number of tropical livestock units. The coefficients, standard errors, t-values, and p-values indicate the significance and direction of each factor’s impact on manure production. Significant results *** (p < 0.001), ** (p < 0.01), are highlighted, showing how significantly these factors contribute to variations in manure production among farmers.
Table 3.
Gamma regression analysis examining various factors that influence the amount of manure produced by farmers. The variables include district location, involvement in farmer research networks, the proportion of crop residues fed to livestock, the method of feeding crop residues to livestock, and the number of tropical livestock units. The coefficients, standard errors, t-values, and p-values indicate the significance and direction of each factor’s impact on manure production. Significant results *** (p < 0.001), ** (p < 0.01), are highlighted, showing how significantly these factors contribute to variations in manure production among farmers.
Variable | Estimate | Std. Error | t-Value | Pr(>|t|) |
---|
(Intercept) | 2.0246 | 0.2502 | 8.0940 | <0.001 *** |
Pallisa reference group | | | | |
District Singida | 4.5131 | 0.1606 | 28.0950 | <0.001 *** |
District Soroti | −1.2895 | 0.1318 | −9.7820 | <0.001 *** |
Farmer not in research network is reference group | | | | |
Farmer organization Farmer research network | 0.4649 | 0.1184 | 3.9260 | <0.001 *** |
Proportion of crop residues fed to livestock 10–39 reference group | | | |
Proportion of crop residues fed to livestock 40–69 | 0.0388 | 0.1206 | 0.3210 | 0.7481 |
Proportion of crop residues fed to livestock 70–99 | 0.1440 | 0.1899 | 0.7580 | 0.4488 |
Proportion of crop residues fed to livestock 100 | −0.1635 | 1.0457 | −0.1560 | 0.8758 |
Cut and carry home reference group | | | | |
Crop residues fed to livestock cut and carried home grazed in the field | 0.8446 | 0.2223 | 3.8000 | <0.001 *** |
Crop residues fed to livestock cut and carried home grazed in the field Cut and processed into hay or silage | 1.1396 | 1.0780 | 1.0570 | 0.2911 |
Crop residues fed to livestock grazed in the field | 0.6639 | 0.1977 | 3.3590 | <0.001 *** |
Crop residues fed to livestock grazed in the field cut and carried home | 0.6427 | 0.2049 | 3.1360 | 0.0018 ** |
Crop residues fed to livestock grazed | −0.9368 | 1.0558 | −0.8870 | 0.3755 |
Crop residues fed to livestock grazed in the field cut and processed into hay or silage | 0.1239 | 1.0573 | 0.1170 | 0.9068 |
TLU | 0.1737 | 0.0149 | 11.6510 | <0.001 *** |
Table 4.
Differences in manure applied by farmers (in kg/season) across study districts and farmer categories (FRN vs. non-FRN).
Table 4.
Differences in manure applied by farmers (in kg/season) across study districts and farmer categories (FRN vs. non-FRN).
| | Total Manure Applied Across the Farm (kg, Categorized) |
---|
District | Farmer Organization Type | 50–300 | 301–500 | 501–700 | 701–900 | 900–1000 | Above a Ton |
---|
Pallisa | FRN *** | 22 | 19 | 12 | 12 | 7 | 3 |
| non-FRN | 55 | 9 | 4 | 1 | 0 | 0 |
| total Pallisa | 77 | 28 | 16 | 13 | 7 | 3 |
Soroti | FRN *** | 44 | 14 | 4 | 2 | 1 | 2 |
| non-FRN | 70 | 0 | 0 | 0 | 0 | 0 |
| total Soroti | 114 | 14 | 4 | 2 | 1 | 2 |
Singida | FRN (ns) | 1 | 0 | 1 | 4 | 0 | 61 |
| non-FRN | 0 | 0 | 1 | 1 | 0 | 63 |
| total Singida | 1 | 0 | 2 | 5 | 0 | 124 |
Table 5.
Gamma regression analysis examining various factors that influenced the amount of manure used by farmers. The variables include district location, involvement in FRN, responsibility in manure application, crop types grown, receipt of extension services, and the number of tropical livestock units.
Table 5.
Gamma regression analysis examining various factors that influenced the amount of manure used by farmers. The variables include district location, involvement in FRN, responsibility in manure application, crop types grown, receipt of extension services, and the number of tropical livestock units.
Variable | Estimate | Std. Error | t-Value | Pr(>|t|) |
---|
(Intercept) | 0.7317 | 0.3169 | 2.3090 | 0.0215 * |
Pallisa reference group | | | | |
District Singida | 4.5522 | 0.2338 | 19.4680 | <0.001 *** |
District Soroti | 0.2550 | 0.1909 | 1.3360 | 0.1823 |
Farmer not in research network is reference group | | | | |
Farmer category Farmer network research | −1.3336 | 0.1845 | −7.2280 | <0.001 *** |
Male children reference group | | | | |
Responsible for application Everyone in the farm | 0.1006 | 0.2343 | 0.4290 | 0.6680 |
Responsible for application farm Workers | 0.2231 | 0.5492 | 0.4060 | 0.6848 |
Responsible for application Husband | −0.0199 | 0.2482 | −0.0800 | 0.9360 |
Responsible for application Wife | −0.1729 | 0.2598 | −0.6650 | 0.5061 |
Cereals | 0.7985 | 0.2012 | 3.9690 | <0.001 *** |
Vegetables | 0.6674 | 0.1663 | 4.0140 | <0.001 *** |
Legumes | 0.9496 | 0.1961 | 4.8430 | <0.001 *** |
Roots and tubers | −0.3599 | 0.2051 | −1.7550 | 0.0800 |
Received extension services | 1.0086 | 0.2195 | 4.5940 | <0.001 *** |
Tropical livestock unit | 0.1543 | 0.0158 | 9.7960 | <0.001 *** |
Table 6.
Frequency of various barriers to using different bio-inputs as reported by farmers in different districts (Singida, Pallisa, and Soroti) and farmer organization categories (FRN and Non-FRN).
Table 6.
Frequency of various barriers to using different bio-inputs as reported by farmers in different districts (Singida, Pallisa, and Soroti) and farmer organization categories (FRN and Non-FRN).
District | Lack of Knowledge | Not Easily Accessible | Difficult to Get Materials | Expensive to Buy | Requires Many Applications | Cultural Restrictions |
---|
Singida | 49 | 7 | 49 | 5 | 0 | 0 |
Pallisa | 26 | 12 | 15 | 4 | 1 | 0 |
Soroti | 35 | 35 | 35 | 32 | 32 | 0 |
Farmer Organization |
FRN | 95 | 41 | 78 | 36 | 32 | 0 |
Non-FRN | 50 | 31 | 20 | 5 | 1 | 0 |
Table 7.
Number of farmers who applied different amounts of bioinoculants in relation to categories of belonging to a farmer research network (FRN), number of applications during a season, receiving training on soil fertility management and access to extension services, in Pallisa and Soroti districts, Uganda and Singida district, Tanzania.
Table 7.
Number of farmers who applied different amounts of bioinoculants in relation to categories of belonging to a farmer research network (FRN), number of applications during a season, receiving training on soil fertility management and access to extension services, in Pallisa and Soroti districts, Uganda and Singida district, Tanzania.
District | Farmer Category | Number of Times Bioinoculants Applied | Total Amounts of Bioinoculants Applied per Season |
---|
1–20 kg | 21–50 kg | 51–75 kg | 76–100 kg | 101–150 kg | Above 150 kg |
---|
Pallisa | FRN | Once | 3 | | | | | |
| | Twice | 30 | 16 | 10 | 8 | 3 | 4 |
| | Three times | | 1 | | | | |
| Non-FRN | Once | 67 | 1 | | | | |
| Three times | | | | 1 | | |
| Received training on soil fertility management | Yes | 28 | 17 | 9 | 9 | 3 | 3 |
| No | 72 | 1 | 1 | | | 1 |
| Received extension services | Yes | 28 | 16 | 9 | 9 | 3 | 3 |
| No | | 1 | | | | |
Soroti | FRN | Once | 47 | 4 | | | | 1 |
| | Twice | 7 | 1 | | | | 1 |
| | Four times | 2 | | | | | |
| Non-FRN | Once | 70 | | | | | |
| | Three times | 1 | 2 | | 1 | | |
| | Four times | 2 | | | | | |
| Received training on soil fertility management | Yes | 75 | 4 | | 1 | | 1 |
| No | 52 | 3 | | | | |
| Received Extension services | Yes | 54 | 3 | | 1 | | 1 |
| No | 73 | 4 | | | | 1 |
Singida | FRN | Once | 13 | | | | | 50 |
| Non-FRN | Once | 31 | | | | | 34 |
| Received Training on soil fertility mgt | Yes | 15 | | | 4 | | 50 |
| No | 29 | | | | | 34 |
| Received extension services | Yes | 15 | | | 4 | | 50 |
| No | 29 | | | | | 34 |
Table 8.
Number of respondents reporting the presence and absence of three types of pests (stem borers, sorghum midges, and shoot flies) across districts and farmer organization categories.
Table 8.
Number of respondents reporting the presence and absence of three types of pests (stem borers, sorghum midges, and shoot flies) across districts and farmer organization categories.
Farmer Organization | Pest | Yes | No |
---|
FRN | Stem borers | 172 | 37 |
Sorghum midge | 122 | 87 |
Shoot fly | 95 | 114 |
Head bugs | 37 | 172 |
Cutworms | 33 | 172 |
Armyworm | 08 | 201 |
Non-FRN | Stem borers | 154 | 50 |
Sorghum midge | 134 | 70 |
Shoot fly | 136 | 68 |
Head bugs | 121 | 83 |
Cutworms | 101 | 103 |
Armyworm | 69 | 135 |
Table 9.
Frequency of various agronomic and environmental factors that contribute to the prevalence of sorghum pests in the Singida, Pallisa, and Soroti districts, as well as across different farmer organization categories (FRN and Non-FRN).
Table 9.
Frequency of various agronomic and environmental factors that contribute to the prevalence of sorghum pests in the Singida, Pallisa, and Soroti districts, as well as across different farmer organization categories (FRN and Non-FRN).
District | Delayed Planting | Low Soil Fertility | Drought Conditions | Mono cropping | Too Much Rain | Repeated Sorghum Planting | Destroyed Environment | Repeated Use of Seed | Weedy Fields | Poor Pesticide Use |
---|
Singida | 124 | 66 | 35 | 70 | 121 | 56 | 22 | 2 | 0 | 0 |
Pallisa | 135 | 104 | 101 | 22 | 49 | 11 | 48 | 0 | 2 | 1 |
Soroti | 110 | 108 | 120 | 12 | 83 | 94 | 85 | 5 | 1 | 0 |
Farmer organization |
FRN | 178 | 125 | 105 | 61 | 77 | 58 | 37 | 7 | 1 | 1 |
Non-FRN | 191 | 153 | 151 | 43 | 176 | 103 | 118 | 0 | 2 | 0 |
Table 10.
The different sorghum pest management measures employed by farmers across the districts of Singida, Pallisa, and Soroti, including the variation in pest management measures undertaken by farmers who belong to farmer research networks and those in non-farmer research network organizations.
Table 10.
The different sorghum pest management measures employed by farmers across the districts of Singida, Pallisa, and Soroti, including the variation in pest management measures undertaken by farmers who belong to farmer research networks and those in non-farmer research network organizations.
District | Do Timely Planting | Spray with Chemical Pesticides | Spray with Botanical Pesticides | Do Nothing | Planting Border Plants to Destroy Pests | Mixed Planting of Sorghum and Other Crops | Preserving Field Borders/Margins |
---|
Singida | 101 | 74 | 69 | 27 | 31 | 50 | 5 |
Pallisa | 131 | 69 | 08 | 41 | 01 | 16 | 1 |
Soroti | 110 | 104 | 08 | 21 | 04 | 20 | 82 |
Farmer organization |
FRN | 165 | 81 | 73 | 35 | 24 | 53 | 17 |
Non-FRN | 177 | 166 | 12 | 54 | 12 | 33 | 71 |