Startseite Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
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Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda

  • Gerald Kyalo , Srinivasulu Rajendran EMAIL logo , Simon Alibu , Simon Zziwa , Margaret McEwan , Moses Ekobu , Stella E. A. Okello , Sam Namanda , Michael H. Otim , Jimmy Lamo , Robert O. M. Mwanga und Jan W. Low
Veröffentlicht/Copyright: 18. November 2024
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Abstract

A crop rotation study was conducted in the Agoro Rice scheme from mid-2015 to 2017 to determine the effect of sweetpotato–rice rotation in the lowlands on financial returns and sweetpotato root, sweetpotato vine, and rice yields compared to monocropping. Treatments included crop rotations of sweetpotato–rice–sweetpotato, rice–sweetpotato–rice, rice–rice–rice (control), and sweetpotato–sweetpotato–sweetpotato (control). The study used the sweetpotato varieties NASPOT 11 (cream-fleshed), NASPOT 10 O, and Ejumula (both orange-fleshed) and the rice varieties Wita 9, Agoro, and Komboka. The results showed that mean sweetpotato root yields in the rotation treatment were significantly higher (28 t ha−1) than the control (19.8 t ha−1), representing a 47% gain in yield. Generally, the percentage gain in yield across years due to rotation ranged from 3 to 132%, depending on the variety. The total number of vine cuttings was significantly different between treatments and seasons (P < 0.001). Mean rice paddy yields in rotation were 8–35% higher than the control. The higher yields of sweetpotato in the rotation can be attributed to the rotation crop benefitting from residual fertilizers applied in rice in the previous season, while rice in the rotation crop could have benefited from the land preparation and establishment of the sweetpotato fields. The benefit of rotation for both crops varied by variety while the revenue-to-cost ratio varied by season and crop variety. Revenue-to-cost ratios for rotation and control treatments were greater than 1, indicating net profits were positive for both. The rotation generated 0.43 times more revenue than rice monocropping. Both rotation and monocropping systems generated profits, but rotation was 43% more profitable. In other words, if monocropping generates 1 dollar, rotation generates 1.43 dollars. The study concludes that rotation of sweetpotato with rice led to (1) increased yields of both rice and sweetpotatoes, (2) more profitable utilization of land, (3) enhanced availability of sweetpotato planting material at the beginning of the upland growing season, and (4) reduced the cost of land preparation for the main rice crop. Findings from this study show that there is great potential for diversification of rice-based cropping systems in Uganda, which will contribute to building sustainable food systems.

Keywords: productivity; profitability; rice; rotation; sweetpotato; vine multiplication

1 Introduction

Sweetpotato (Ipomoea batatas L.) is a primary staple in the East and Central African region, with per capita consumption ranging from 35 kg per year to more than 80 kg per year [1]. In Uganda, sweetpotato is grown country-wide for its storage roots, which are used for household consumption and small-scale trading [2]. Sweetpotato is widely grown in Uganda because it is highly adaptable and tolerates high temperatures, low soil fertility, and drought [3]. Annual sweetpotato production in 2022 stood at 1,543,000 MT produced on 309,000 ha of land [4]. The Eastern region leads in annual sweetpotato production with 847,140 metric tons (MT) produced in 2018, followed by the western, central, and northern regions, with 366,295, 312,402, and 292,932 MT, respectively. The average sweetpotato production area per household in Uganda ranges from 0.53 to 0.81 acres [5,6].

Increased production and productivity of sweetpotato in SSA faces a number of constraints, both abiotic and biotic. Abiotic constraints include drought or weather extremes and low soil fertility, while serious biotic constraints include viruses, mainly sweetpotato virus disease (SPVD), fungal diseases, mainly Alternaria bataticola blight, and sweetpotato weevils [7]. Other constraints to sweetpotato production include poor access to quality seed-vines of suitable varieties and poor agronomic management practices by farmers that, in turn, affect yield, food availability, and income for households. Improving sweetpotato production requires a supply of and access to good quality seed-vines, accompanied by improvements in plant nutrition and disease and post-harvest management [8].

Despite the lower overall production of sweetpotato in northern Uganda where the rainfall pattern is unimodal, it is a key food-security crop in this region. The key limitation for increasing sweetpotato production in the region is the lack of sufficient quality planting material at the beginning of the first season’s rain in March, caused by the long dry season from December through February, during which vines (the planting material for this vegetatively propagated crop) have desiccated [9]. As a result, farmers are left with no option but to borrow from neighbours, who have planting materials or buy vine cuttings from commercial vine multipliers/suppliers at a higher price. For example, vine suppliers in the Gulu district are reported to provide vine cuttings to smallholders in the surrounding districts of Nwoya, Amuru, Lamwo, Pader, Lira, and Oyam.

Rice (Oryza sativa L.), on the other hand, has emerged as a critical food staple and a major source of income for many Ugandans [10]. About 80% of the rice farmers in Uganda are small-scale, farming less than 2 ha of land using basic labour-intensive tools and techniques, with limited access to irrigation, struggling with poor water management [10], and hardly using any fertilizers. Domestic paddy production is expected to rise from 320,000 tons in 2020 to 700,000 tons in 2026. It is now the second most-produced cereal after maize and is increasingly favoured over traditional staples such as bananas and millet by Ugandans, particularly among the youth, who make up 78% of the population because it is easier to cook and eat. More than 90% of the national rice output of Uganda is produced by smallholder farmers in Eastern and Northern Uganda under rainfed and irrigated rice systems [11]. In Northern Uganda, irrigated and rainfed lowland rice is planted only once a year during the first season, from March to June. The lowland is then left to fallow until the next year’s planting season (Figure 1), as there is insufficient water to irrigate a high-yielding, water-intensive rice crop.

Figure 1 Typical lowland rice field develops hard pans under fallow after harvesting first season rice crop. Source: G. Kyalo.
Figure 1

Typical lowland rice field develops hard pans under fallow after harvesting first season rice crop. Source: G. Kyalo.

During the fallow period, the fields are used for grazing animals. The resultant hardpans make land preparation for the next rice-crop an arduous task. The fallow period in the lowland rice fields coincides, in part, with the long dry season when sweetpotato vines desiccate in upland fields.

In this study, we postulate that the use of lowland rice fields could be optimized by utilizing the fallow period following the main rice season to grow sweetpotato. Sweetpotato is known for being able to produce a good crop with residual moisture that is insufficient for rice, pulses, and oil seed crops [12]. Moreover, vines from the post-rotation between rice and sweetpotato crops could potentially serve as a source of planting material for farmers in Northern Uganda, other regions of the country, and even southern Sudan, hence mitigating the typical shortage of planting material after the long dry season [13], with vine sales potentially providing a source of income. In addition, sweetpotato root and leaf production from a dry season crop could also serve as much-needed nutritional food sources at the end of the dry season when household food stocks are typically depleted.

Although crop rotations of sweetpotato with crops other than rice are common in upland farming systems of Uganda, rotation of sweetpotato in lowland rice systems is not frequently practiced by farmers. However, results from Vietnam indicated that rotation of lowland rice with sweetpotato increased rice yield and nitrogen-use efficiency of rice [14]. When rice followed sweetpotato in the rotation, the nutrient-use efficiency (NUE) of rice increased from 19% (NUE of rice following rice) to 29%. Moreover, the cultivation of sweetpotato improved the physical, chemical, and biological properties of soil, which benefited the rice crop that followed.

The potential advantages of rotating sweetpotato and rice have not been adequately explored and documented in Uganda. The major objective of this study was to explore the potential of growing sweetpotato as an alternative to leaving lowland rice fields fallow. The study was specifically designed to (a) assess the influence of crop rotation on yields of paddy rice and sweetpotato vines and roots; (b) evaluate the revenue-to-cost ratio for the rotation (sweetpotato–rice) versus monocropping options (rice–rice and sweetpotato–sweetpotato), and (c) explore whether rotation would enable the timely supply of basic sweetpotato “seed” (cuttings) to decentralized multipliers, root producers, and improve access of farmers in or near the rice scheme to the seed of newly released rice varieties.

2 Materials and methods

2.1 Study sites

The experiment was set up in the Agoro irrigation scheme located in Pobar Parish, Agoro Subcounty, Lamwo district, northern Uganda, in collaboration with the Tutte Laco Laco (TULA) Youth group participating in the scheme (Figure 2).

Figure 2 Experimental plot of rice–sweetpotato rotation. Source: S. Rajendran.
Figure 2

Experimental plot of rice–sweetpotato rotation. Source: S. Rajendran.

The scheme is located at latitude 3°48′0.7415″N3.800206, longitude 33°1′3.7488″E.017708, and 1,130 m above sea level [15]. Covering a total area of 745 ha, the soils in the scheme are dominated by gleysols. Gleysols are wetland soils known for being continuously water‐saturated for long periods, with the water table being 50 cm below the soil surface. This leads to reduced iron and manganese content, causing the predominantly greyish hues in the profile below the water table. Agoro receives unimodal rains, averaging 1,300 mm annually, stretching from mid-March to October, with a peak in April. Average temperatures range from 17 to 30°C. Soils from the experimental area were sampled and analysed at the National Agricultural Research Organization (NARO)-National Agriculture Research Laboratory, Kawanda, Uganda, for acidity (pH), total carbon, total nitrogen, available phosphorus (P), and exchangeable calcium (Ca), magnesium (Mg), and potassium (K) according to Okalebo et al. [16]. Soil pH was measured in a soil–water solution in a ratio of 1:2.5; the organic matter was determined via potassium dichromate wet acid oxidation (Walkley and Black method). Total N was determined by Kjeldahl digestion; extractable P by the Bray P1 method; K+ and Na+ from an ammonium acetate extract by flame photometry and Ca2+ and Mg2+ by atomic absorption spectrophotometry. Particle size distribution (texture) was determined using the Bouyoucos (hydrometer) method [16].

2.2 Crop materials

The study used three released sweetpotato varieties, two being orange-fleshed (Ejumula and NASPOT 10 O (Kabode)) and NASPOT 11 (cream-fleshed); and three rice varieties, viz., Wita 9, Komboka, and Agoro (Table 1).

Table 1

Plant materials used in the study

Crop Variety Attributes
Sweetpotato Ejumula (orange-fleshed sweetpotato (OFSP))

  • Vigorous vine growth with dense foliage that spread

  • Low field resistance to SPVD

  • Moderate field resistance to ASB

  • Potential yield: 19 t ha−1 [17]
NASPOT 10 O/Kabode (OFSP)

  • Semi-erect growth type

  • Green vine pigmentation

  • Skin of the roots is purplish-red

  • Medium resistance to SPVD and ASB

  • Potential yield: 25 t ha−1 [18]
NASPOT 11 (cream-fleshed sweetpotato)

  • Green vine pigmentation

  • Purplish-red skinned roots

  • Potential yield: 26.5 t ha−1

  • Moderate field resistance to SPVD

  • High field resistance to ASB

  • Susceptible to weevil [19]
Rice Wita 9

  • Short stature variety

  • Highly tolerant to rice yellow mottle virus, bacterial leaf blight, and rice blast

  • Matures in 145 days

  • Potential yield: 6.4 t ha−1 [20]
Komboka

  • An aromatic rice variety

  • Plant height: 90–100 cm

  • Matures in 120 days

  • Potential yield: 5 t ha−1

  • Tolerant to RYMV, BLB, and rice blast [20]
Agoro

  • Plant height: <90 cm

  • Matures in 125 days

  • Potential yield: 7 t ha−1

  • Resistant to yellow mottle virus, bacterial leaf blight, and rice blast [20]

O refers to orange fleshed.

The initial pre-basic sweetpotato planting material was sourced from the BioCrops private tissue culture laboratory located in Uganda, whereas rice seed was provided by the rice breeder at the National Crops Resource Research Institute, Uganda. Sweetpotato seed, cuttings (pieces of vine 20–30 cm long), and planting material are used interchangeably in this article. There are four categories of seed recognized internationally, viz., breeder’s seed, pre-basic, basic, and quality declared seed (QDS). Both pre-basic (also referred to as foundation) and basic seed are part of early generation seed. Pre-basic seed is grown in screen houses and comprises cuttings from plants sourced from tissue culture plantlets tested for pathogens, and basic seed is sourced from pre-basic cuttings and produced in open multiplication fields [21]. However, in Uganda, pre-basic seed is designated as basic seed, and basic seed is referred to as certified 1 (C1), and further multiplication of C1 produces certified 2 (C2) seed. Both C1 and C2 are sold or provided to root producers for root production. They can also be further multiplied as QDS and then sold or provided to other root producers.

2.3 Treatments, trial design, and field layout

The field experiment was initiated at the onset of the long dry season in December 2015, and rotation trials were conducted for the next three seasons: 2016A, 2016B, and 2017A (A refers to first and B refers to the second rainy season). The experiment was set-up as a split-plot design, and cropping sequences were assigned randomly to main plots and crop varieties to sub-plots within the main plots. The treatments were replicated four times (Table 2).

Table 2

Experimental layout of the rotation treatments in each season, 2015–2017

V: sweetpotato, R: rice.

Data source: experiment, 2015–2017.

The size of the main plots was 20 m × 23 m, separated by 2 m alleyways, whereas the sub-plots measured 6 m × 5 m, which were separated by 1 m alleyways. The land was prepared using a tractor, and fields levelled manually using hand hoes. Disease-free sweetpotato cuttings, each 30-cm long, were planted on mounds spaced 1 m × 1 m and three vine cuttings per mound. Rice seedlings were raised in a nursery and transplanted after 3 weeks into the plots. Three seedlings were transplanted per hill at a spacing of 25 cm × 25 cm. The experiment was irrigated every 2 weeks for 30–60 min during dry spells using water from the irrigation scheme. Efforts were made to ensure that each plot received water during the time of irrigation. The rotation crop was planted each time after the harvest of the previous crop. Weeds were controlled manually by weeding 3–4 times at 3-week intervals during the growing season. Fertilizer was applied to the rice crop both in the rotation and control sub-plots at a rate of 30:30:30 kg nitrogen–phosphorus–potassium (NPK) ha−1 at planting and then 30:0:0 kg NPK ha−1 at panicle initiation. No fertilizer was applied in the sweetpotato crop. Two persons were hired to scare away birds from the rice plots from the time of heading (the panicle is fully visible) until harvest.

2.4 Data collection and analysis

Data were collected on the incidence and severity of SPVD (Figure 3a) and Alternaria stem blight (ASB) (Figure 3b), weevil infestation, and root and vine weight at harvest in sweetpotato. Data on SPVD and ASB were collected 1 month after planting and 1 month before harvesting. Disease incidence was recorded using a scale of 1–9, where 1 = no disease symptoms and 9 = severe symptoms on all plants. Weevil infestation was also recorded at harvest on a scale of 1–9, where 1 = no infestation and 9 = most severe infestation [18]. Root and vine yields were assessed from a 10 m2 net sub-plot, which was randomly determined and expressed as tons per hectare. The number of 30 cm vine cuttings harvested per plot was also recorded.

Figure 3 (a) Sweetpotato plant showing symptoms of SPVD. (b) Sweetpotato plant showing symptoms of ASB. Source: G. Kyalo.
Figure 3

(a) Sweetpotato plant showing symptoms of SPVD. (b) Sweetpotato plant showing symptoms of ASB. Source: G. Kyalo.

Data were collected on rice yield at harvest and the percent grain moisture content was determined with a moisture meter. Rice grain obtained from a central 5 m2 harvested area in each plot was then adjusted to a 14% moisture content. Data were checked for normality and homoscedasticity and transformed as needed to meet the assumptions of analysis of variance (ANOVA). Data were analysed as a split-plot design in a randomized complete block design using a general linear model for ANOVA in the Genstat 12th edition software. During analysis, the rotations were randomly assigned to the main/whole plots, while the varieties were assigned to the small/sub-plots. Separate randomization of subplot levels (varieties) was done within each main plot.

For economic analysis, STATA version 17.0 was used to determine predictive margins for revenue-to-cost ratios through a linear regression function. Factors considered in the model were treatment (rotation or control), crop variety, and season. Interactions between treatment and variety, treatment and season, season and variety, and season × variety × treatment were included in the analysis. Mean values were separated by use of Fisher’s least significant difference (LSD) at α = 0.05. LSD values are used in statistical analysis to determine whether the difference between two mean values is statistically significant. In the context of our tables, an LSD value provides a threshold: if the difference between the mean values of two treatments exceeds this threshold, the difference is considered statistically significant at the specified confidence level (usually 95%).

The economic benefits of crop rotation between rice and sweetpotato were determined using financial cost–benefit analysis based on data collected from the field and local market prices during the two crop rotations. The cost of production was estimated for each rotation and control block. Further, the study measured the revenue-to-cost ratio by variety, block, and season. The revenue-to-cost ratio was estimated by dividing the total revenue by the total production costs.

In addition to the experimental data, the economic analysis was carried out with the following field data; it was assumed that the potential vine yield per block (i.e., area size: 360 m2) was 18 bags. The weight per bag of vine cuttings was, on average, 35 kg and, hence, total production per block would be 630 kg of vine cuttings per block. A bag of sweetpotato roots weighing 120 kg was assumed to cost 55,000 UGX (USD 16; exchange rate 1 USD = 3437.5 Ugandan Shillings (UGX)) for the year 2016. For estimating revenue, we valued the production of both vine cuttings and roots based on current market prices. For example, a 35 kg bag of vine cuttings was valued at 15,000 UGX (USD 4.4) in 2016. The market price for roots increased in the second season of 2016 to 70,000 UGX (USD 20.4) per 120 kg bag of roots and increased further to 80,000 UGX (USD 23.3) in the first season of 2017 (third rotation). Similarly, the price of vine cuttings also increased to 20,000 UGX (USD 5.5) per bag. The price of planting material used in the calculations was slightly higher than the market price, as these materials were free from SPVD because they were sourced from Biocrops, a Ugandan private tissue-culture company that employs quality control measures. The revenue-to-cost ratio was estimated in two contexts: (1) assuming that the unit of the outcome of sweetpotato was estimated in kilograms for both roots and vines and (2) assuming that the outcome of vine production was a number of vine cuttings (30 cm cuttings) and roots in kilograms. Rice seed was estimated in kg using a market price of 5,000 UGX (USD 1.4) across three seasons. The market price for rice seed remained fairly constant during the experimental period. Data on the cost of labour for all field activities and inputs were recorded during the experiment on a real-time basis. This was combined with data on sweetpotato root and vine yield as well as rice yield per plot, which was used to compute the revenue-to-cost ratio. Paddy was harvested from the net plot and converted to tons ha−1, adjusted to a 14% moisture content.

3 Results

3.1 Soil chemical properties of the experimental site

The soils at the experimental site were loamy, with an average pH of 6.0 (range: 5.3–6.8). They were high in organic matter (4%) and available nitrogen (0.25%) but overall low in phosphorus (average: 3.2 mg/kg) and moderate in potassium (average: 55.1 mg/kg). Levels of calcium and magnesium were both high. The average levels of calcium and magnesium were 2234.6 and 567.0 ppm, respectively. A detailed analysis report, including the typical soil nutrient requirements of sweetpotato, is presented in Table A1.

3.2 Effect of rotation on rice yields

In the treatment plot, the paddy yield of rice grown after sweetpotato was significantly higher than that for the control (P = 0.001), where rice followed rice. There was also a significant difference (P < 0.001) in yield performance of the different rice varieties (Table 3).

Table 3

Mean value of paddy yield of three rice varieties rotated with sweetpotato after three seasons

Treatment Variety Paddy yield (t ha−1)
2016A 2016B 2017A Overall mean per variety Standard deviation
Control Agoro 2.60 3.73 4.80 3.71 1.10
Komboka 3.68 4.90 4.50 4.36 0.62
Wita-9 2.68 4.20 4.73 3.87 1.06
Rotation Agoro 2.83 4.00 5.20 4.01 1.19
Komboka 4.88 6.13 6.35 5.79 0.79
Wita-9 3.60 5.78 5.57 4.98 1.20
Mean 3.38 4.79 5.19
CV (%) 15.33 15.89 19.40
LSD0.05 rotation (R) 0.45 0.66 0.88
LSD0.05 variety (V) 0.55 0.81 n.s.
LSD0.05 rotation × variety n.s. n.s. n.s.

n.s. - not significant.

The rotation produced yield gains in the three rice varieties tested when compared to the average yield during the baseline season of 2015 (average yield in control plots = 2.66 t ha−1, average yield in rotation plots = 2.63 t ha−1). The highest percent yield gain (35%) was attributable to the rotation compared to the control for the variety Wita 9, followed by Komboka (29%), whereas the Agoro variety had the lowest yield gain (8%).

3.3 Effect of rotation on sweetpotato vine and root yields

Rotating sweetpotato and rice across three seasons also had a significant effect on sweetpotato root yield (P = 0.03) but not on vine yield (Tables 3 and 4). The average root yield in the rotation treatment was significantly higher (mean yield = 28.0 t ha−1) than in the control (mean yield = 19.8 t ha−1) when compared to the baseline average yield in 2015 (mean control yield = 14.6 t ha−1, mean rotation yield = 20.2 t ha−1) (baseline yields in 2015 is presented in Table A2). The root yields of the three varieties were significantly different (P = 0.021), and the interaction between treatment and variety was also significant (P = 0.034). The variety Ejumula had the highest mean yield (29 t ha−1), followed by NASPOT 10 O and NASPOT 11. All varieties performed better in the rotation than the control (Table 4).

Table 4

Overall mean root yield for three sweetpotato varieties planted during three seasons in rotation with rice in the Agoro rice scheme, northern Uganda

Treatment Variety Yield (t ha−1)
2016A 2016B 2017A Overall mean per variety Standard deviation
Control Ejumula 23.7 6.6 31.2 20.5 5.2
NASPOT 10 O 25.2 4.2 37.1 22.2 10.6
NASPOT11 19.0 8.6 20.2 15.9 4.0
Rotation Ejumula 32.4 9.8 72.6 38.3 2.6
NASPOT 10 O 24.5 6.0 38.3 22.9 5.2
NASPOT11 31.0 7.8 36.5 25.1 4.3
Mean 26.0 7.1 39.3
CV (%) 14.7
LSD0.05 rotation 7.6
LSD0.05 variety 6.1
LSD0.05 season 6.7
LSD0.05 rotation × variety 8.7
LSD0.05 rotation × season 9.2
LSD0.05 season × variety NS
LSD0.05 rotation × variety × season NS

NS – not significant.

Table 5 shows that vine yields were not significantly different across treatments and varieties but were significantly different across seasons (P = 0.001). The highest vine yield was observed in 2017A (27.4 t ha−1), whereas the lowest vine yield was in 2016B (14.6 4 t ha−1). The low vine and root yields in 2016B can be attributed to the prolonged dry season experienced in 2016B. In that season, the water supply in the scheme was not adequate for occasional irrigation. Even though total vine yields were not significantly different across treatments, the total number of 30 cm vine cuttings was significantly different between treatments and seasons (P < 0.001) when measured, and the interaction between season and variety was significant (P < 0.001) (Tables 4 and 5). It is important to note that data on the number of cuttings per plot were not taken in 2016A. Irrespective of season, the rotation treatment produced more vine cuttings than the control – mean of 1,017 and 3,510 cuttings per plot in 2016B and 2017A, respectively, compared to the 679 and 1,253 cuttings produced in the same years in the control plots. This could be the effect of residual fertilizers applied to rice in the preceding season of the rotation.

Table 5

Overall mean vine yield and number of 30 cm vine cuttings for three sweetpotato varieties planted during three seasons in rotation with rice in Agoro rice scheme, northern Uganda

Treatment Variety Vines yield (tons ha−1) No. vine cuttings (30 cm) (per 30 m2)
2016A 2016B 2017A Overall mean per variety Standard deviation 2016B 2017A
Control Ejumula 17.4 10.0 33.1 20.2 9.7 867.0 1219.0
NASPOT 10 O 27.8 11.0 24.2 21.0 7.8 595.0 1304.0
NASPOT11 24.0 24.8 24.6 24.5 1.5 577.0 1238.0
Mean 23.1 15.3 27.3 21.9 6.1 679.0 1253.0
Rotation Ejumula 20.0 11.2 22.6 17.9 5.9 1041.0 3653.0
NASPOT 10 O 19.8 11.2 28.7 19.9 8.8 794.0 3413.0
NASPOT11 27.6 19.7 31.2 26.2 5.9 1218.0 3466.0
Mean 22.5 14.0 27.5 21.3 6.8 1017.0 3510.0
Overall mean 22.8 14.6 27.4 849.0 2382.0
CV (%) 8.4 32.3
LSD0.05 rotation NS 205.0
LSD0.05 variety NS NS
LSD0.05 season 6.4 355.1
LSD0.05 rotation × variety NS NS
LSD0.05 rotation × season NS NA
LSD0.05 season × variety NS NA
LSD0.05 rotation × variety × season NS NA

Average vine cuttings (tons ha−1): control = 21.9, treatment = 21.3.

NS – not significant, NA – not available.

3.4 Cost and benefit analysis of the rotation

The breakdown costs of producing sweetpotato and rice in rotation and control treatments are presented in Table 6.

Table 6

Actual cost of producing sweetpotato roots (US$), vine cuttings, and paddy rice in rotation vs control plots in the Agoro rice scheme 2016 to 2017

Cost category Rotation Control
Total cost (2016A) Total cost (2016B) Total cost (2017A) Total cost (2016A) Total cost (2016B) Total cost (2017A)
Input cost 55.4 47.7 48.2 55.4 53.0 51.2
Labour cost 147.6 132.8 138.6 137.2 127.0 128.1
Fixed cost 28.7 30.4 31.6 28.7 32.1 31.6
Wastage cost 9.2 9.9 10.4 10.5 12.3 16.0
Total cost 240.9 220.8 228.9 231.8 224.5 227.0
Total revenue 482.2 446.4 553.5 395.6 326.3 450.5
Net profit 241.3 225.6 324.6 163.9 101.9 223.5
Share of input costs to the total costs (%)
Input cost 23 22 21 24 24 23
Labour cost 61 60 61 59 57 56
Fixed cost 12 14 14 12 14 14
Wastage cost 3.8 4.5 4.6 5 5 7

Exchange rate 1 USD = 3437.5 Ugandan Shillings (UGX) as of 2016.

The total cost of production was broken down into four categories: inputs used, labour, and fixed and wastage costs. The input costs consisted of costs of seed and sweetpotato planting materials, fertilizer, pesticides, and irrigation (i.e. paid user fee to the cooperative). The labour costs focused on wages paid to the labourers for various input applications such as land preparation, planting, weeding, irrigation, fertilizer applications, bird management, threshing, drying, winnowing, packing, and harvesting costs. The fixed costs included the cost of equipment such as irrigation materials (i.e. water tank, water can, and bucket), planting rakes that are used for making rows or lines, sickles, knapsack, protective gears, spade, hand trowel, spraying suite, special nose masks, seed trays, bird deterrent net, weighing scale, fencing, cutlers, slashers, rice weeders, and hand hoes. The fixed cost depreciated based on the life cycle of the product and usage. Costs were also estimated per block and per crop. Labour costs constituted the major share of the total costs in both rotation and control plots, but in the rotation plots, the share of input cost declined slightly as the number of rotations increased (Table 6). This trend was not observed for the control plots. Furthermore, Table 6 presents data on net profit according to crop rotation and season. The results show that rotation generates more net profit compared to monocropping, emphasizing the opportunity cost of foregoing a rice crop to grow sweetpotato in a rotational system. The descriptive statistics for the revenue-to-cost ratio by variety and rotation in all seasons are presented in Table 7.

Table 7

Descriptive statistics for mean revenue-to-cost ratio by variety and rotation in all seasons

Variety Treatment Mean Std deviation N
All varieties Rotation 2.15 0.33 18
Control 1.72 0.44 18
NASPOT 10 O Rotation 2.00 0.07 3
Control 1.56 0.20 3
NASPOT 11 Rotation 1.96 0.45 3
Control 1.62 0.12 3
EJUMULA Rotation 2.12 0.21 3
Control 1.64 0.17 3
WITA 9 Rotation 2.29 0.48 3
Control 1.76 0.53 3
KOMBOKA Rotation 2.68 0.35 3
Control 2.01 0.26 3
AGORO Rotation 1.85 0.59 3
Control 1.69 0.57 3

Though the total cost of production was almost the same for both rotation and control plots, the revenue generated from the production was higher for the rotation compared with the control, resulting in the higher revenue-to-cost ratio in rotation between rice and sweetpotato compared with the control. ANOVA results showed that there was a statistically significant difference in the net income between rotation and control (Table 8).

Table 8

ANOVA summary for revenue to cost ratio for all seasons by interventions (rotation vs control)

Source of variation df MS F P Effect size (partial SS)
Overall significance test across rotation, variety, and seasons for both sweetpotato and rice
Overall model 8 0.59 7.45 0.0000* 4.70
Rotation 1 1.68 21.34 0.0001* 1.68
Variety 5 0.30 3.81 0.0097* 1.50
Seasons 2 0.76 9.62 0.0007* 1.52
Residual 27 0.08
Total 35 0.20 6.83
Interaction effect between rotation and seasons for both sweetpotato and rice: All varieties
Overall model 5 0.67 5.63 0.000* 3.31
Rotation 1 1.68 14.33 0.000* 1.68
Seasons 2 0.76 6.46 0.004* 1.52
Rotation*seasons 2 0.05 0.46 0.635 0.11
Residual 30 0.12 3.52
Total 35 0.20 6.83

MS  =  mean squares, effect size  =  η 2 or partial η 2.

p (significance level): *1% level; **5% level; ***10% level; and ****15% level.

The revenue-to-cost ratio for both rotation and control was greater than 1 for rotation (2.15) and the control (1.72) (Figure 4), demonstrating that both approaches generated revenues exceeding the costs of production.

Figure 4 Revenue-to-cost ratio for rotation and control of sweetpotato and rice in Agoro rice scheme, northern Uganda.
Figure 4

Revenue-to-cost ratio for rotation and control of sweetpotato and rice in Agoro rice scheme, northern Uganda.

However, the rotation generated a revenue-to-cost ratio that was 0.43 times higher than the control (monocropping), indicating that both rotation and monocropping systems generated profits, but the rotation was more profitable than monocropping, where rotation generated 43% more profit compared to monocropping. Alternatively, the ratio indicates that if monocropping generates 1 dollar, the rotation can generate 1.43 dollars. The revenue-to-cost ratio for the two crops varied by season and variety, as shown in Figure 5.

Figure 5 Revenue-to-cost ratio of three sweetpotato varieties (Ejumula, NASPOT 10 O, and NASPOT 11) and three rice varieties (Agoro, Komboka, and Wita) and the overall revenue-to-cost ratio of both crops over three seasons in rotation (treatment) and monocropping (control) in the Agoro Rice scheme. Note: R1: 2016A, R2: 2016B, and R3: 2017.
Figure 5

Revenue-to-cost ratio of three sweetpotato varieties (Ejumula, NASPOT 10 O, and NASPOT 11) and three rice varieties (Agoro, Komboka, and Wita) and the overall revenue-to-cost ratio of both crops over three seasons in rotation (treatment) and monocropping (control) in the Agoro Rice scheme. Note: R1: 2016A, R2: 2016B, and R3: 2017.

In addition, ANOVA results, including predictive margins (Table A3), showed that the ratio varied in significance by season and variety. In summary, ANOVA and predictive margin results showed that the overall impact of crop rotation on the ratio was significant and positive for both sweetpotato and rice compared with monocropping alternatives, indicating that a rotation of both crops improved the revenue-to-cost ratio.

4 Discussion

Considering the baseline yield in season B 2015, the study showed that rice fields in northern Uganda could be used for producing sweetpotato planting materials during the December to March period when plots would otherwise be under fallow. The yield of rice can be enhanced by rotating rice and sweetpotato vis-à-vis continuous rice monocropping under Northern Uganda conditions. These results are consistent with Hung et al. [22], who found the paddy yield of rice following rice to be 3.4 t ha−1 compared to 4.6 t ha−1 when rice followed sweetpotato, representing a yield gain of 35% due to the benefits of rotation. They attributed the yield gain to the higher nitrogen (N) fertilizer-use efficiency of rice (29%), following sweetpotato, compared with lower N-fertilizer-use efficiency of 19% for rice following another rice crop. Similarly, Ning et al. [23] found that in a rotation of sweetpotato with wheat where potassium fertilizer was applied, the root yield and total rotation yield increased by 20.7‒24.5%. The yield increase in this experiment was attributed to the application of fertilizers and the return of residues back to the gardens. The results from this study showed yield increases for sweetpotato ranging from 3 to 132% across the three seasons, which is higher than that reported by Hung et al. [22] and Ning et al. [23].

NASPOT 11 performed poorly each season in the control treatment, with its mean yield (15.9 t ha−1) being lower than that reported by Mwanga et al. [19] (26 t ha−1) at variety release. This, however, is consistent with its performance in national on-farm trials. In those trials, NASPOT 11 had much lower yields in the Northern region site than in the Central, Eastern, and Western Ugandan sites [19,24].

According to Thierfelder and Wall [25], rotations may improve soil quality, and deep-rooting crops can lead to better soil structure, aggregation, and pore continuity, with positive effects on infiltration and soil moisture in rainfed agricultural settings. Therefore, the higher yield of rice in this rotation may be attributed to the rooting properties of sweetpotato, potentially improving soil structure and, consequently, water infiltration and soil moisture. However, our results also showed that rice genotypes responded differently to the rotation with sweetpotato. Hence, rice varieties with different growth patterns and maturity periods must be tested when establishing new rice–sweetpotato rotations.

Throughout the study, rice plots received fertilizer in the form of NPK, and the subsequent sweetpotato crop could have benefited from residual fertilizers applied to rice the previous season, thereby contributing to the improved root yields observed. Young et al. [26] indicated that given the current low market value of sweetpotato, investment in soil fertility improvements might only be financially viable in an integrated soil and crop management system, which includes rotational cropping, whereby fertilizers used for other higher-value crops can provide residual benefits to the sweetpotato and rotational cropping with legumes to provide additional nitrogen. Residual fertilizers could have contributed to increases in sweetpotato yields in this study.

Nedunchezhiyan et al. [12] observed that soil bulk density significantly decreased from 1.41 to 1.37 g cm−3 when sweetpotato was planted after rice in India. Low soil bulk density can enhance rooting depth and available soil water, which influence productivity [27]. This is possibly another reason for the increase in sweetpotato root yield.

Lukonge et al. [28] reported a good response to the direct application of inorganic and organic fertilizers in Tanzania for vine production. In their study, application of 50 kg of nitrogen per hectare resulted in 196 cuttings (each 30 cm in length) per 1.2-m2 plot, with an incremental yield of 765,490 cuttings per hectare compared with 123 cuttings per plot recorded in the control plot. Namanda and Gibson [13] found that farmers could double the number of cuttings produced through the judicious use of NPK (25:5:5) fertilizer. Although no direct fertilizers were applied to sweetpotato in our study, we suggest that the higher number of vine cuttings recorded in the rotation treatment was a likely response to the residual fertilizer applied to rice during the preceding season. This practice also addresses the deficit in available planting material at the beginning of the rainy season created by the long drought period that farmers in rainfed systems face. In addition, roots harvested early in the season can address food shortages during the well-documented “hunger season” [29].

In the case of NASPOT 11, the overall impact of rotation was not significant, but significance was observed to increase with season. This implies that we might have to rotate more times with NASPOT 11 to realize financial benefits. In the case of NASPOT 10 O, the overall results showed the impact of rotation was significant in improving economic benefits. The results indicated that the Komboka rice variety was superior to the other two and best in rotation with sweetpotato, NASPOT 10 O, or Ejumula.

The study on rice–sweetpotato crop rotation was conducted in the irrigation scheme, Agoro rice scheme, where the crop was irrigated during the dry season, which was a drawback due to a lack of data under rainfed conditions. There is a need to conduct a study in a pure rainfed condition to be able to validate the assertion that sweetpotato can grow with minimal moisture during the dry season. Furthermore, whereas soil data for the initial period during the start of the study was collected and presented, soil data for successive seasons were not collected. As such, important soil parameters like NUE, as reported by Nedunchezhiyan et al. [30], Yu et al. [22], and Hung et al. [14], could not be calculated. Future related studies should be set up in both rainfed and irrigated systems, collect and present data on soil properties like accumulation of nitrogen, organic matter, and NUE. This will help explain parameters like rice and sweetpotato yield, number of tillers, panicles, leaf area index, and harvest indices better. These will improve future recommendations on the rice–sweetpotato rotation systems.

5 Conclusion

This study showed the agronomic and economic benefits of rotating sweetpotato with irrigated rice in the lowland rice-production systems. Farmers in the rice schemes can easily rotate sweetpotato with rice. Overall, sweetpotato roots and rice in the rotation yielded better than in the monocropping systems. The higher number of vines recorded in the rotation compared to control signifies a potential income advantage for farmers selling planting materials. This also enables the availability of quality planting materials after the long dry season. In addition, this new practice would allow farmers in upland areas to access vines when they need them at the time of planting, a remedy for late planting on account of scarcity of planting materials. The revenue-to-cost ratio was higher in rotation than control, indicating that rotating sweetpotato with rice was more profitable than monocropping in northern Uganda when there is a unimodal rainfall season. However, a deeper study is required to understand whether sweetpotato would yield well in the rotation under rainfed conditions. There is a need to replicate the current study in other regions growing rice in Uganda under the monocropping system. Rice–sweetpotato rotation should be demonstrated in other rice-growing regions of Uganda for farmers to appreciate and adopt this innovation. While sweetpotato–rice rotation has been shown to increase yields for rice and sweetpotato, the increase in yield for sweetpotato has been heavily linked to residual fertilizers from the previous rice crop. However, there are challenges to increasing fertilizer use in Uganda, particularly the high cost of fertilizers. Strategies to overcome these challenges may require different policies to incentivize fertilizer adoption as well as strategies to educate farmers on the benefits of crop rotations.

# Present address: Uganda Coffee Development Authority (UCDA) P.O. Box 7267, Kampala, Uganda.

Acknowledgements

We thank the leaders of Tutte Laco Laco (TULA) Youth group and the farmers in the study area for their support in mobilizing the resources. The authors wish to express their appreciation to Elke Vandamme for her review. We are grateful to the referees for their constructive inputs.

  1. Funding information: This research was coordinated by the International Potato Center and National Agricultural Research Organisation (NARO) Uganda as part of the CGIAR Research Program on Roots, Tubers, and Bananas (RTB), Sweetpotato Action for Security and Health in Africa, and CGIAR Seed Equal Initiative. It is supported by CGIAR Trust Fund contributors (http://www.cgiar.org/funders/).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. GK: conceptualization, data curation, formal analysis, investigation, methodology, writing – original draft, writing – review and editing, validation, visualization, and supervision. SR: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, validation, writing – original draft, writing – review and editing, and visualization. SA: conceptualization, data curation, formal analysis, methodology, validation, visualization, writing – original draft, and writing – review and editing. SZ: conceptualization, data curation, formal analysis, methodology, validation, visualization, writing – original draft, and writing – review and editing. MM: conceptualization, funding acquisition, investigation, project administration, resources, supervision, validation, writing – original draft, writing – review and editing, and visualization. ME: conceptualization, methodology, validation, visualization, and writing – review and editing. SO: conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, and writing – review and editing. SN: conceptualization, data curation, investigation, methodology, validation, visualization, and writing – review and editing. MHO: conceptualization, data curation, investigation, methodology, validation, visualization, and writing – review and editing. JL: conceptualization, data curation, formal analysis, investigation, methodology, writing – original draft, writing – review and editing, validation, visualization, and supervision. ROM: conceptualization, methodology, writing – original draft, writing – review and editing, validation, and visualization. JL: conceptualization, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, writing – original draft, writing – review and editing, and visualization.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The data that support the findings of this study are available from the International Potato Center (CIP) but restrictions may apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of CIP.

Appendix
Table A1

Results of soil test

Lab no. Client’s reference pH OM N P Ca Mg K
% mg/kg
S/15/7747 KA 6.6 4.61 0.24 Trace 2870.0 930.3 77.6
S/15/7748 KB 6.8 4.37 0.23 3.43 4391.4 1012.9 245.5
S/15/7749 KC 5.8 4.65 0.24 Trace 1640.7 545.7 55.1
S/15/7750 NA 5.8 4.52 0.24 4.96 1896.6 330.0 47.7
S/15/7751 NB 5.6 5.83 0.29 0.54 1560.6 304.1 46.3
S/15/7752 SA 6.2 5.19 0.26 3.26 2030.1 512.6 65.5
S/15/7753 SB 5.3 4.61 0.24 3.77 1252.9 333.7 42.0
Classification of mehlich-3 extractable nutrients
Category P K Ca Mg OM N
mg/kg %
Very low 0–12 0–20 <330 <17 0.7–1.0 <0.05
Low 12.5–22.5 20.5–40.5 330–655 17–46 1.0–1.7 0.05–0.15
Medium 23–35.5 41–72.5 655–1,640 46–87 1.7–3.0 0.15–0.25
High 36–68.5 73–138.5 1,640–3,280 87–145 3.0–5.15 0.25–0.5
Very high >69 >139 >3,280 >145 >5.15 >0.5

Source: National Agricultural Research Organisation (NARO) – National Agriculture Research Laboratory, Kawanda, Uganda.

Note: critical values for pH: 5.2; sufficient levels: 5.27.0.

Table A2

Mean root and vine yield (t ha−1) for sweetpotato varieties during the 2015 (baseline season) growing season in Agoro rice scheme, Northern Uganda

Treatment Variety Root yield (t ha−1) Vine yield (t ha−1)
Mean Std Dev Mean Std Dev
Control Ejumula 22.3 15.6
NASPOT 10 O 20.5 14.6 9.19 24.1 20.17 7.04
NASPOT11 16.1 20.8
Rotation Ejumula 27.4 16.8
NASPOT 10 O 20.2 20.2 6.04 17.5 18.9 8.62
NASPOT11 26.1 22.4
Mean paddy yield (t ha−1) in 2015B season
Treatment Variety Yield (t ha−1 ) Mean
Control Agoro 2.3 2.66
Komboka 3.3
Wita-9 2.4
Rotation Agoro 2.3 2.63
Komboka 3.1
Wita-9 2.5
Mean 2.7
CV (%) 15
LSD0.05 variety (V) 0.05
LSD0.05 rotation × variety n.s
Table A3

ANOVA for revenue to cost ratio for all seasons by crop and varieties (rotation vs control)

Source of variation df MS F p Effect size (partial SS)
Significance test across rotation, variety, and seasons for sweetpotato
Overall model 13 0.10 2.53 0.19 1.33
Rotation 1 0.77 19.13 0.01* 0.77
Variety 2 0.02 0.44 0.67 0.04
Seasons 2 0.10 2.57 0.19 0.21
Rotation*Variety 2 0.01 0.19 0.84 0.02
Rotation*Seasons 2 0.10 2.37 0.21 0.19
Variety*Seasons 4 0.03 0.67 0.64 0.11
Residual 4 0.04 0.16
Total 17 0.09 1.49
Significance test across rotation, variety, and seasons for rice
Overall model 13 0.37 23.98 0.00* 4.80
Rotation 1 0.91 59.24 0.00* 0.91
Variety 2 0.50 32.18 0.00* 0.10
Seasons 2 1.22 79.16 0.00* 2.44
Rotation*Variety 2 0.10 6.65 0.05** 0.20
Rotation*Seasons 2 0.002 0.11 0.90 0.003
Variety*Seasons 4 0.63 4.08 0.10*** 0.25
Residual 4 0.02 0.06
Total 17 0.29 4.86
Significance test between rotation and control for sweetpotato variety NASPOT 10 O
Overall model 3 0.11 8.95 0.10*** 0.34
Rotation 1 0.28 22.01 0.04** 0.28
Season 2 0.03 2.42 0.29 0.06
Residual 2 0.01 0.03
Total 5 0.07 0.36
Significance test between rotation and control for sweetpotato variety NASPOT 11
Overall model 3 0.10 0.60 0.67 0.29
Rotation 1 0.17 1.06 0.41 0.17
Season 2 0.06 0.37 0.73 0.12
Residual 2 0.16 0.32
Total 5 0.12 0.61
Significance test between rotation and control for sweetpotato variety Ejumula
Overall model 3 0.16 39.62 0.02** 0.48
Rotation 1 0.34 84.85 0.01** 0.34
Season 2 0.07 17.01 0.06*** 0.14
Residual 2 0.004 0.008
Total 5 0.10 0.49
Significance test between rotation and control for rice variety WITA 9
Overall model 3 0.47 47.49 0.02** 1.41
Rotation 1 0.41 41.64 0.02** 0.41
Season 2 0.50 50.41 0.02** 0.10
Residual 2 0.01 0.02
Total 5 0.29 1.43
Significance test between rotation and control for rice variety Komboka
Overall model 3 0.33 15.04 0.06*** 1.00
Rotation 1 0.67 30.14 0.03** 0.67
Season 2 0.17 7.48 0.12**** 0.33
Residual 2 0.2 0.44
Total 5 0.21 0.04
Significance test between rotation and control for rice variety Agoro
Overall model 3 0.47 1036.07 0.00* 1.40
Rotation 1 0.04 85.33 0.01* 0.04
Season 2 0.68 1511.44 0.00* 1.36
Residual 2 0.001 0.001
Total 5 0.28 1.40
Predictive margins for revenue-to-cost ratio for all seasons by interventions (rotation vs control)
Variables Margin Std. Err. t P > |t|
Margins for overall significance test across rotation, variety, and seasons for both sweetpotato and rice
Rotation vs control (overall)
Control 1.72 0.080 21.24 0.000*
Rotation 2.15 0.080 26.59 0.000*
Rotation vs control (sweetpotato)
Control 1.61 0.067 24.01 0.000*
Rotation 2.02 0.067 30.20 0.000*
Rotation vs control (rice)
Control 1.82 0.041 44.05 0.000*
Rotation 2.27 0.041 54.93 0.000*
Varieties for sweetpotato
NASPOT 10 1.78 0.114 15.52 0.000*
NASPOT 11 1.79 0.114 15.64 0.000*
Ejumula 1.88 0.114 16.39 0.000*
Varieties for rice
Wita 9 2.03 0.114 17.67 0.000*
Komboka 2.34 0.114 20.45 0.000*
AGORO 1.77 0.114 15.45 0.000*
Rotation*Variety (sweetpotato)
Control*NASPOT 10 O 1.56 0.116 13.47 0.000*
Control*NASPOT 11 1.62 0.116 13.99 0.000*
Control*Ejumula 1.64 0.116 14.13 0.000*
Rotation*NASPOT 10 O 1.99 0.116 17.18 0.000*
Rotation*NASPOT 11 1.96 0.116 16.89 0.000*
Rotation*EJUMULA 2.12 0.116 18.24 0.000*
Rotation*Variety (rice)
Control*Wita9 1.56 0.116 13.47 0.000*
Control*Komboka 1.62 0.116 13.99 0.000*
Control*Agoro 1.64 0.116 14.13 0.000*
Rotation*Wita 9 1.99 0.116 17.18 0.000*
Rotation*Komboka 1.96 0.116 16.89 0.000*
Rotation*Agoro 2.12 0.116 18.24 0.000*
Seasons (both sweetpotato and rice)
2016A season 1.85 0.099 18.66 0.000*
2016B season 1.73 0.099 17.54 0.000*
2017A season 2.21 0.099 22.39 0.000*
Seasons (sweetpotato)
2016A season 1.67 0.082 39.99 0.000*
2016B season 1.86 0.082 46.28 0.000*
2017A season 1.92 0.082 34.96 0.000*
Seasons (rice)
2016A season 2.02 0.051 20.33 0.000*
2016B season 1.61 0.051 22.67 0.000*
2017A season 2.50 0.051 23.40 0.000*
Variety*Seasons (sweetpotato)
NASPOT 10 O*2016A season 1.75 0.142 12.28 0.000*
NASPOT 10 O*2016b season 1.68 0.142 11.78 0.000*
NASPOT 10 O*2017A season 1.92 0.142 13.47 0.000*
NASPOT 11*2016A season 1.60 0.142 11.22 0.000*
NASPOT 11*2016B season 1.92 0.142 13.47 0.000*
NASPOT 11*2017A season 1.87 0.142 13.12 0.000*
Ejumula*2016A season 1.67 0.142 11.71 0.000*
Ejumula*2016B season 1.99 0.142 14.00 0.000*
Ejumula*2017A season 1.98 0.142 13.93 0.000*
Variety*seasons (rice)
Wita 9*2016A season 2.10 0.088 23.89 0.000*
Wita 9*2016B season 1.50 0.088 17.05 0.000*
Wita 9*2017A season 2.49 0.088 28.33 0.000*
Komboka*2016A season 2.36 0.088 26.85 0.000*
Komboka*2016B season 2.05 0.088 23.37 0.000*
Komboka*2017A season 2.63 0.088 29.93 0.000*
Agoro*2016A season 1.62 0.088 18.41 0.000*
Agoro*2016B season 1.28 0.088 14.59 0.000*
Agoro*2017A season 2.42 0.088 27.54 0.000*
Rotation*seasons (both sweetpotato and rice)
Control*2016A season 1.70 0.139 12.13 0.000*
Control*2016B season 1.45 0.139 10.38 0.000*
Control*2017A season 2.00 0.139 14.28 0.000*
Rotation*2016A season 2.00 0.139 14.25 0.000*
Rotation*2016B season 2.02 0.139 14.42 0.000*
Rotation*2017A season 2.43 0.139 17.39 0.000*
Rotation*seasons (sweetpotato)
Control*2016A season 1.58 0.116 13.61 0.000*
Control*2016B season 1.52 0.116 13.10 0.000*
Control*2017A season 1.73 0.116 14.88 0.000*
Rotation*2016A season 1.76 0.116 15.14 0.000*
Rotation*2016B season 2.20 0.116 18.96 0.000*
Rotation*2017A season 2.11 0.116 18.21 0.000*
Rotation*seasons (rice)
Control*2016A season 1.81 0.072 25.32 0.000*
Control*2016B season 1.38 0.072 19.32 0.000*
Control*2017A season 2.27 0.072 31.65 0.000*
Rotation*2016A season 2.23 0.072 31.14 0.000*
Rotation*2016B season 1.83 0.072 25.60 0.000*
Rotation*2017A season 2.75 0.072 38.40 0.000*

Note: MS  =  mean squares, effect size  =  η 2 or partial η 2 .

p (significance level) *1% level; **5% level; ***10% level; and ****15% level.

Note: p (significance level) *1% level; **5% level; ***10% level; and ****15% level.

Remarks: The soils are mainly low in phosphorus and moderate in potassium in a few of the highlighted blocks. This indicates that the use of phosphates and potash fertilizers is a must for successful plant growth.

Crop requirements (sweetpotato): Sweetpotatoes grow in a wide variety of soils, from swamps to eroded areas, but they perform best on well-drained sandy loam soils and poorly on clay soils. They are sensitive to alkaline and saline soils, and their preferred soil pH is 5.5–6.5. They show a good response to farmyard manure, which increases the yield and quality of the storage roots. Mineral fertilizers, especially potassium (K), which promotes swelling of the storage roots, are also important. A common recommendation in most countries that grow sweetpotatoes is 35–65 kg/ha N, 50–100 kg/ha P2O5, and 85–170 kg/ha K. This combination of fertilizers is usually applied as NPK compound fertilizer with a high K content.

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Received: 2023-12-27
Revised: 2024-09-09
Accepted: 2024-09-25
Published Online: 2024-11-18

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Regular Articles
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  3. Yield gap variation in rice cultivation in Indonesia
  4. Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters
  5. Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach
  6. Morphological diversity, correlation studies, and multiple-traits selection for yield and yield components of local cowpea varieties
  7. Participatory on-farm evaluation of new orange-fleshed sweetpotato varieties in Southern Ethiopia
  8. Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments
  9. Biology of Spodoptera frugiperda (Lepidoptera: Noctuidae) on different types of plants feeds: Potency as a pest on various agricultural plants
  10. Antidiabetic activity of methanolic extract of Hibiscus sabdariffa Linn. fruit in alloxan-induced Swiss albino diabetic mice
  11. Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance
  12. Nicotinamide as a biostimulant improves soybean growth and yield
  13. Farmer’s willingness to accept the sustainable zoning-based organic farming development plan: A lesson from Sleman District, Indonesia
  14. Uncovering hidden determinants of millennial farmers’ intentions in running conservation agriculture: An application of the Norm Activation Model
  15. Mediating role of leadership and group capital between human capital component and sustainability of horticultural agribusiness institutions in Indonesia
  16. Biochar technology to increase cassava crop productivity: A study of sustainable agriculture on degraded land
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  22. Effects of selected biostimulants on qualitative and quantitative parameters of nine cultivars of the genus Capsicum spp.
  23. Growth, yield, and secondary metabolite responses of three shallot cultivars at different watering intervals
  24. Design of drainage channel for effective use of land on fully mechanized sugarcane plantations: A case study at Bone Sugarcane Plantation
  25. Technical feasibility and economic benefit of combined shallot seedlings techniques in Indonesia
  26. Control of Meloidogyne javanica in banana by endophytic bacteria
  27. Comparison of important quality components of red-flesh kiwifruit (Actinidia chinensis) in different locations
  28. Efficiency of rice farming in flood-prone areas of East Java, Indonesia
  29. Comparative analysis of alpine agritourism in Trentino, Tyrol, and South Tyrol: Regional variations and prospects
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  35. Exploring innovative food in a developing country: Edible insects as a sustainable option
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  49. The synergistic application of sucrose and various LED light exposures to enhance the in vitro growth of Stevia rebaudiana (Bertoni)
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  55. Evaluating salt tolerance in fodder crops: A field experiment in the dry land
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  57. Cropping systems and production assessment in southern Myanmar: Informing strategic interventions
  58. The effect of biostimulants and red mud on the growth and yield of shallots in post-unlicensed gold mining soil
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https://doi.org/10.1515/opag-2022-0369
Schlagwörter für diesen Artikel
productivity; profitability; rice; rotation; sweetpotato; vine multiplication
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. Supplementation of P-solubilizing purple nonsulfur bacteria, Rhodopseudomonas palustris improved soil fertility, P nutrient, growth, and yield of Cucumis melo L.
  3. Yield gap variation in rice cultivation in Indonesia
  4. Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters
  5. Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach
  6. Morphological diversity, correlation studies, and multiple-traits selection for yield and yield components of local cowpea varieties
  7. Participatory on-farm evaluation of new orange-fleshed sweetpotato varieties in Southern Ethiopia
  8. Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments
  9. Biology of Spodoptera frugiperda (Lepidoptera: Noctuidae) on different types of plants feeds: Potency as a pest on various agricultural plants
  10. Antidiabetic activity of methanolic extract of Hibiscus sabdariffa Linn. fruit in alloxan-induced Swiss albino diabetic mice
  11. Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance
  12. Nicotinamide as a biostimulant improves soybean growth and yield
  13. Farmer’s willingness to accept the sustainable zoning-based organic farming development plan: A lesson from Sleman District, Indonesia
  14. Uncovering hidden determinants of millennial farmers’ intentions in running conservation agriculture: An application of the Norm Activation Model
  15. Mediating role of leadership and group capital between human capital component and sustainability of horticultural agribusiness institutions in Indonesia
  16. Biochar technology to increase cassava crop productivity: A study of sustainable agriculture on degraded land
  17. Effect of struvite on the growth of green beans on Mars and Moon regolith simulants
  18. UrbanAgriKG: A knowledge graph on urban agriculture and its embeddings
  19. Provision of loans and credit by cocoa buyers under non-price competition: Cocoa beans market in Ghana
  20. Effectiveness of micro-dosing of lime on selected chemical properties of soil in Banja District, North West, Ethiopia
  21. Effect of weather, nitrogen fertilizer, and biostimulators on the root size and yield components of Hordeum vulgare
  22. Effects of selected biostimulants on qualitative and quantitative parameters of nine cultivars of the genus Capsicum spp.
  23. Growth, yield, and secondary metabolite responses of three shallot cultivars at different watering intervals
  24. Design of drainage channel for effective use of land on fully mechanized sugarcane plantations: A case study at Bone Sugarcane Plantation
  25. Technical feasibility and economic benefit of combined shallot seedlings techniques in Indonesia
  26. Control of Meloidogyne javanica in banana by endophytic bacteria
  27. Comparison of important quality components of red-flesh kiwifruit (Actinidia chinensis) in different locations
  28. Efficiency of rice farming in flood-prone areas of East Java, Indonesia
  29. Comparative analysis of alpine agritourism in Trentino, Tyrol, and South Tyrol: Regional variations and prospects
  30. Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible–near-infrared and shortwave–near-infrared reflectance spectroscopy
  31. Forage yield, seed, and forage qualitative traits evaluation by determining the optimal forage harvesting stage in dual-purpose cultivation in safflower varieties (Carthamus tinctorius L.)
  32. The influence of tourism on the development of urban space: Comparison in Hanoi, Danang, and Ho Chi Minh City
  33. Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia
  34. The role of organic rice farm income on farmer household welfare: Evidence from Yogyakarta, Indonesia
  35. Exploring innovative food in a developing country: Edible insects as a sustainable option
  36. Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia
  37. Factors influencing green, environmentally-friendly consumer behaviour
  38. Factors affecting coffee farmers’ access to financial institutions: The case of Bandung Regency, Indonesia
  39. Morphological and yield trait-based evaluation and selection of chili (Capsicum annuum L.) genotypes suitable for both summer and winter seasons
  40. Sustainability analysis and decision-making strategy for swamp buffalo (Bubalus bubalis carabauesis) conservation in Jambi Province, Indonesia
  41. Understanding factors affecting rice purchasing decisions in Indonesia: Does rice brand matter?
  42. An implementation of an extended theory of planned behavior to investigate consumer behavior on hygiene sanitation-certified livestock food products
  43. Information technology adoption in Indonesia’s small-scale dairy farms
  44. Draft genome of a biological control agent against Bipolaris sorokiniana, the causal phytopathogen of spot blotch in wheat (Triticum turgidum L. subsp. durum): Bacillus inaquosorum TSO22
  45. Assessment of the recurrent mutagenesis efficacy of sesame crosses followed by isolation and evaluation of promising genetic resources for use in future breeding programs
  46. Fostering cocoa industry resilience: A collaborative approach to managing farm gate price fluctuations in West Sulawesi, Indonesia
  47. Field investigation of component failures for selected farm machinery used in small rice farming operations
  48. Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans
  49. The synergistic application of sucrose and various LED light exposures to enhance the in vitro growth of Stevia rebaudiana (Bertoni)
  50. Weather index-based agricultural insurance for flower farmers: Willingness to pay, sales, and profitability perspectives
  51. Meta-analysis of dietary Bacillus spp. on serum biochemical and antioxidant status and egg quality of laying hens
  52. Biochemical characterization of trypsin from Indonesian skipjack tuna (Katsuwonus pelamis) viscera
  53. Determination of C-factor for conventional cultivation and soil conservation technique used in hop gardens
  54. Empowering farmers: Unveiling the economic impacts of contract farming on red chilli farmers’ income in Magelang District, Indonesia
  55. Evaluating salt tolerance in fodder crops: A field experiment in the dry land
  56. Labor productivity of lowland rice (Oryza sativa L.) farmers in Central Java Province, Indonesia
  57. Cropping systems and production assessment in southern Myanmar: Informing strategic interventions
  58. The effect of biostimulants and red mud on the growth and yield of shallots in post-unlicensed gold mining soil
  59. Effects of dietary Adansonia digitata L. (baobab) seed meal on growth performance and carcass characteristics of broiler chickens: A systematic review and meta-analysis
  60. Analysis and structural characterization of the vid-pisco market
  61. Pseudomonas fluorescens SP007s enhances defense responses against the soybean bacterial pustule caused by Xanthomonas axonopodis pv. glycines
  62. A brief investigation on the prospective of co-composted biochar as a fertilizer for Zucchini plants cultivated in arid sandy soil
  63. Supply chain efficiency of red chilies in the production center of Sleman Indonesia based on performance measurement system
  64. Investment development path for developed economies: Is agriculture different?
  65. Power relations among actors in laying hen business in Indonesia: A MACTOR analysis
  66. High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought
  67. Converting compression ignition engine to dual-fuel (diesel + CNG) engine and experimentally investigating its performance and emissions
  68. Structuration, risk management, and institutional dynamics in resolving palm oil conflicts
  69. Spacing strategies for enhancing drought resilience and yield in maize agriculture
  70. Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types
  71. Investigating Spodoptera spp. diversity, percentage of attack, and control strategies in the West Java, Indonesia, corn cultivation
  72. Yield stability of biofertilizer treatments to soybean in the rainy season based on the GGE biplot
  73. Evaluating agricultural yield and economic implications of varied irrigation depths on maize yield in semi-arid environments, at Birfarm, Upper Blue Nile, Ethiopia
  74. Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses
  75. Pomegranate peel ethanolic extract: A promising natural antioxidant, antimicrobial agent, and novel approach to mitigate rancidity in used edible oils
  76. Transformative learning and engagement with organic farming: Lessons learned from Indonesia
  77. Tourism in rural areas as a broader concept: Some insights from the Portuguese reality
  78. Assessment enhancing drought tolerance in henna (Lawsonia inermis L.) ecotypes through sodium nitroprusside foliar application
  79. Edible insects: A survey about perceptions regarding possible beneficial health effects and safety concerns among adult citizens from Portugal and Romania
  80. Phenological stages analysis in peach trees using electronic nose
  81. Harvest date and salicylic acid impact on peanut (Arachis hypogaea L.) properties under different humidity conditions
  82. Hibiscus sabdariffa L. petal biomass: A green source of nanoparticles of multifarious potential
  83. Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV
  84. First evidence of microplastic pollution in mangrove sediments and its ingestion by coral reef fish: Case study in Biawak Island, Indonesia
  85. Physical and textural properties and sensory acceptability of wheat bread partially incorporated with unripe non-commercial banana cultivars
  86. Cereibacter sphaeroides ST16 and ST26 were used to solubilize insoluble P forms to improve P uptake, growth, and yield of rice in acidic and extreme saline soil
  87. Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality
  88. Optimizing inorganic blended fertilizer application for the maximum grain yield and profitability of bread wheat and food barley in Dawuro Zone, Southwest Ethiopia
  89. The acceptance of social media as a channel of communication and livestock information for sheep farmers
  90. Adaptation of rice farmers to aging in Thailand
  91. Combined use of improved maize hybrids and nitrogen application increases grain yield of maize, under natural Striga hermonthica infestation
  92. From aquatic to terrestrial: An examination of plant diversity and ecological shifts
  93. Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol
  94. Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo
  95. Assessment and multi-scenario simulation of ecosystem service values in Southwest China’s mountainous and hilly region
  96. Analysis of agricultural emissions and economic growth in Europe in search of ecological balance
  97. Bacillus thuringiensis strains with high insecticidal activity against insect larvae of the orders Coleoptera and Lepidoptera
  98. Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis
  99. Comparison between mycobiota diversity and fungi and mycotoxin contamination of maize and wheat
  100. Evaluation of cultivation technology package and corn variety based on agronomy characters and leaf green indices
  101. Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
  102. Phytochemical composition and insecticidal activity of Acokanthera oblongifolia (Hochst.) Benth & Hook.f. ex B.D.Jacks. extract on life span and biological aspects of Spodoptera littoralis (Biosd.)
  103. Land use management solutions in response to climate change: Case study in the central coastal areas of Vietnam
  104. Evaluation of coffee pulp as a feed ingredient for ruminants: A meta-analysis
  105. Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area
  106. Harnessing synthetic microbial communities with nitrogen-fixing activity to promote rice growth
  107. Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
  108. Response of potato tuber as an effect of the N-fertilizer and paclobutrazol application in medium altitude
  109. Bridging the gap: The role of geographic proximity in enhancing seed sustainability in Bandung District
  110. Evaluation of Abrams curve in agricultural sector using the NARDL approach
  111. Challenges and opportunities for young farmers in the implementation of the Rural Development Program 2014–2020 of the Republic of Croatia
  112. Yield stability of ten common bean (Phaseolus vulgaris L.) genotypes at different sowing dates in Lubumbashi, South-East of DR Congo
  113. Effects of encapsulation and combining probiotics with different nitrate forms on methane emission and in vitro rumen fermentation characteristics
  114. Phytochemical analysis of Bienertia sinuspersici extract and its antioxidant and antimicrobial activities
  115. Evaluation of relative drought tolerance of grapevines by leaf fluorescence parameters
  116. Yield assessment of new streak-resistant topcross maize hybrids in Benin
  117. Improvement of cocoa powder properties through ultrasonic- and microwave-assisted alkalization
  118. Potential of ecoenzymes made from nutmeg (Myristica fragrans) leaf and pulp waste as bioinsecticides for Periplaneta americana
  119. Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
  120. Revealing the influences of organic amendment-derived dissolved organic matter on growth and nutrient accumulation in lettuce seedlings (Lactuca sativa L.)
  121. Identification of viruses infecting sweetpotato (Ipomoea batatas Lam.) in Benin
  122. Assessing the soil physical and chemical properties of long-term pomelo orchard based on tree growth
  123. Investigating access and use of digital tools for agriculture among rural farmers: A case study of Nkomazi Municipality, South Africa
  124. Does sex influence the impact of dietary vitD3 and UVB light on performance parameters and welfare indicators of broilers?
  125. Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
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Heruntergeladen am 12.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/opag-2022-0369/html?lang=de
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