Maize production systems, farmers’ perception and current status of maize lethal necrosis in selected counties in Kenya

Figures & data

Figure 1. Maize plants infected with MLN, showing the various symptoms observed. Photo taken by Faith Njeru at farmers’ field in Kenya.

Table 1. Profile of sample farmers.

	Sample counties
	Bomet	Narok	Embu	Kirinyaga	Nakuru	Overall
	n = 106	n = 50	n = 61	n = 103	n = 86	n = 406
Female	16.9	8.5	23.2	36.7	14.7	43.6
Male	33.2	15.3	8.7	16.6	26.2	56.4
Average household size	5.8	5.8	5.6	4.8	5.0	5.4
Average Dependants	4.1	4.9	3.8	4.0	3.6	4.0
Average Age of household head	41.1	41.4	48.9	46.1	43.7	44.1

Note: Survey data May–June 2021.

Table 2. Maize production practices for 2021.

	Bomet	Narok	Embu	Kirinyaga	Nakuru	overall
Average area under maize cultivation (acres)	1.84	3.41	0.42	0.67	2.12	1.58
maize yield (t/ha in good season)	2.49	6.86	1.56	2.03	4.98	3.30
maize yield (t/ha in 2020/2021 season)	1.22	1.42	0.77	0.94	2.96	1.48
Average cost of inputs (Ksh)
organic fertilizer	3376	10,506	2387	4434	10,626	6065
pesticides	1150	3500	1207	2604	1236	2014
weed management (per cent)
	(n = 102)	(n = 50)	(n = 61)	(n = 98)	(n = 86)	(n = 397)
manual weeding	100	92	85	57	73	80
use of herbicides	0	2	5	2	13	4
use both methods	0	6	10	41	14	15
pest control methods (percent)
pesticides	88	93	79	77	66	75
Biological/ cultural methods	13	7	18	6	26	15
practice irrigation (percent)	0	2	34	60	1	21
plough (percent)
	(n = 104)	(n = 50)	(n = 61)	(n = 98)	(n = 86)	(n = 399)
Tractor	2	100	0	8	60	28
hand plough with jembe	4	0	100	11	33	26
animal plough	4	0	0	67	3	18
Maize variety grown
	n = 119	n = 34	n = 59	n = 94	n = 77	n = 383
Improved maize variety (percent)	82	56	98	88	97	87
OPV (percent)	18	44	2	12	3	13
Other constraints to maize production
	n = 5	n = 25	n = 34	n = 78	n = 69	n = 211
Poor market prices	20	72	38	41	41	91
High cost of inputs	20	12	44	37	17	21
Weevils					38	12

Table 3. Percent of farmers knowing and having observed MLN in their farms.

	Bomet	Narok	Embu	Kirinyaga	Nakuru	Overall
	n = 106	n = 50	n = 61	n = 101	n = 86	n = 404
Know MLN-yes (percent)	45	90	97	99	84	80
Observed MLN in the farm (percent)	99	98	93	86	90	93
Season when MLN incidence is high
	(n = 102)	(n = 49)	(n = 55)	(n = 93)	(n = 82)	(n = 381)
Short rains	72	98	71	74	87	79
long rains	5	0	16	14	1	7
all seasons maize is planted	2	2	13	10	5	6
do not know	22	0	0	2	7	8

Table 4. Perceived yield loss due to MLN in different counties of Kenya.

	Bomet	Embu	Kirinyaga	Nakuru	Narok	Overall
	(n = 53)	(n = 49)	(n = 88)	(n = 84)	(n = 48)	(n = 322)
0–10%	0		8	43		13
11–25%	2	8	11	12		8
25–40%	2	4	5	25	8	10
40–50%	8	2	3	0	2	3
50–70%	85	10	24	6	52	31
70–90%	2	12	16	10	33	14
90–100%	2	63	33	5	4	21

Figure 2. Correlation between level of weeds in the farm, number of times of weeding per season and mean MLN scores of randomly selected maize plants in the farm (MLN mean scores of 25 maize plants in each farm. Correlogram plotted in R studio: https://www.R-project.org/. (More intense colours for more extreme correlations)).

Figure 3. Methods used to control MLN by farmers in the study counties.

Figure 4. Map showing severity (A) and incidence (B) of MLN in 4 study counties.

Table 5. MLN severity analysis, incidence and prevalence. The difference in the mean level of severity among the different counties was statistically not significant at α = 0.05 (F-Value = 0.863, P-value = 0.4446).

County	Mean severity	Incidence	Prevalence
Bomet	4.928	96.8	100
Kirinyaga	4.412	92.2	100
Narok	4.23	90.67	100
Nakuru	3.93	79.68	100

Figure 5. Results of MCMV and SCMV on a 2% agarose gel: total RNA was extracted from the samples collected from farmers’ field, cDNA was then prepared followed by RT-PCR for MCMV and SCMV using specific primers for these viruses.

Table 6. MLN viruses testing results using RT-PCR for MCMV and SCMV.

		MCMV		SCMV		MLN
County	No. of samples tested	Positive	Percent positive	Positive	Percent positive	Positive	Percent positive
Kirinyaga	25	11	40	20	80	8	32
Bomet	25	24	96	19	76	18	72
Narok	34	34	100	34	100	34	100
Overall	84	69	82	73	87	60	72

Figure 6. Phylogenetic relationships among SCMV coat protein genes of isolates determined in this study (SCMVKirinyaga, SCMVBomet, SCMVNarok) and those retrieved from GenBank (accession number and geographic region is given). The evolutionary history was inferred by using the Maximum Likelihood method and Jukes-Cantor model. Evolutionary analyses were conducted in MEGA X.

Figure 7. Phylogenetic tree reconstructed based on coat protein genes of isolates determined in these study (OL461945, OL461943, OL461944) and those retrieved from the GenBank: The evolutionary history was inferred using the Maximum Likelihood method based on the Jukes-Cantor model at 1000 bootstraps. GenBank accession numbers and country of origin are indicated.

Data availability statement

The authors confirm that the data supporting the findings of this study are available in the manuscript, and within the articles referenced and their supplementary materials. The raw data used for analysis is found at: njeru, faith (2021), ‘Maize production systems, farmers’ perception and current status of Maize Lethal Necrosis in selected counties in Kenya’, Mendeley Data, V2: https://doi.org/10.17632/n5cpfr4536.2. The raw data that has been deposited include the MLN survey data, the QGIS raw data, the sequencing raw data and the unprocessed RT-PCR gel images which are in the folder named new folder.

The partial coat protein gene sequences of 6 samples in this study were registered in GenBank(https://www.ncbi.nlm.nih.gov) and the accession numbers for MCMV (OL461943, OL461944, and OL461945); SCMV (OL461946, OL461947, and OL461948) were provided.