Tables!!

Center for the Advancement of Digital Scholarship; egfinch

3 Tables!!

1.3 Tables

Table 1

Descriptive Statistics, LSMS-ISA Data

Variable	Mean	S.D.	Min	Max
Millet Yield (kg/ha)	695	629	0.660	3759
Sorghum Yield (kg/ha)	735	686	0.530	3930
Maize Yield (kg/ha)	1492	1221	1.26	6000
Material Inputs
Nitrogen Fertilizer (N nutrient kg/ha)	6.74	23.6	0.000	288
Manure (kg/ha)	1594	3800	0.000	29850
Compost (kg/ha)	25.8	247	0.000	4889
Other Organic Fertilizer (kg/ha)	5.64	60.6	0.000	1708
Pesticides (liter/ha)	0.053	0.510	0.000	10.5
Fungicide (liter/ha)	0.033	0.420	0.000	19.7
Herbicide (liter/ha)	0.270	1.17	0.000	19.1
Other Protecting Liquids (liter/ha)	0.007	0.110	0.000	3.14
Local Seed (kg/ha)	10.3	15.4	0.000	236
Improved Seed (kg/ha)	1.00	4.45	0.000	50.7
Labor
Total Labor (no. of days/ha)	45.1	84.8	0.000	1031
Plot Characteristics
Plot Area (ha)	3.07	6.12	0.020	52.7
Distance to plot from house (km)	2.81	4.00	0.000	60.0
Plain (0/1)	0.700	0.464	0.000	1.00
Plateau (0/1)	0.147	0.355	0.000	1.00
Lowlands (0/1)	0.034	0.180	0.000	1.00
Sloped (0/1)	0.130	0.340	0.000	1.00
Soil Sandy (0/1)	0.536	0.498	0.000	1.00
Soil Clay (0/1)	0.357	0.479	0.000	1.00
Soil Lateritic (0/1)	0.110	0.310	0.000	1.00
Anti-Erosion Structure (0/1)	0.043	0.200	0.000	1.00

Source: Authors, based on LSMS-ISA, Mali. Number of plot observations=3733

Table 4

Dryland Cereals Yield Response to N Fertilizer Applied, Instrumental Variables-household Fixed Effects Model

Variables	(1)	(2)	(3)	(4)
Nitrogen Fertilizer	0.190***	0.218***	0.162**	0.149*
	(0.073)	(0.081)	(0.075)	(0.080)
Manure		0.026***	0.024***	0.026***
		(0.008)	(0.009)	(0.010)
Compost		-0.058	-0.044	-0.033
		(0.038)	(0.037)	(0.041)
Other Organic Fertilizer		0.051	0.024	0.022
		(0.066)	(0.066)	(0.067)
Pesticide		0.253	0.304	0.304
		(0.211)	(0.201)	(0.280)
Fungicide		0.076	0.029	0.082
		(0.272)	(0.248)	(0.259)
Herbicide		0.104	-0.063	-0.095
		(0.137)	(0.134)	(0.144)
Other Protecting Liquids		0.673	0.769	0.967
		(1.285)	(1.178)	(1.202)
Total Labor		0.447***	0.335***	0.283***
		(0.030)	(0.033)	(0.039)
Local Seed			0.353***	0.294***
			(0.039)	(0.044)
Improved Seed			0.488***	0.417***
			(0.073)	(0.085)
Millet	-0.159*	0.071	0.249***	0.295**
	(0.090)	(0.093)	(0.096)	(0.117)
Sorghum	-0.393***	-0.188**	-0.036	0.053
	(0.075)	(0.077)	(0.079)	(0.101)
Plot Area				-0.025***
				(0.005)
Distance (km) from House				0.017*
				(0.009)
Plain				-0.008
				(0.130)
Plateau				-0.019
				(0.170)
Lowland				-0.005
				(0.210)
Sandy				-0.032
				(0.210)
Clay				-0.028
				(0.212)
Anti-Erosion Structure				0.486**
				(0.198)
Observations	2,453	2,043	1,707	1,307
Number of households	776	671	548	425
Kleibergen Paap F statistic	218.6	155.8	148.3	112.5
Marginal Effect of N N	23.13	26.95	19.49	16.81
Nutrient Applied

Source: Authors, based on LSMS data. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Sample sizes drop with missing observations in more complete models, particularly those including seed quantities.

1.4 Tables

Table 1

Definition of Control Variables in Multivalued Treatment Effect Model

Control variable	Definition
Treatment covariates
individually-managed	plot managed individually by male or female who is not the EAF head or designate=1, else 0
manager	head=1; individual male not head=2; individual female not head=3
education	plot manager attended primary school=1, 0 else
labor supply	number of adults in EAF between 12 and 55 years of age (inclusive)/total area operated by EAF
weeding cost	cost of hiring weeding labor per ha
subsidy	EAF benefited from fertilizer subsidy=1, 0 else
market	weekly market fair in village=1, 0 else
village	village fixed effect

Outcome covariates
seed	quantity of seed in kgs used per ha
fertilizer	kgs of fertilizer applied per ha
male labor	number of adult male person-days (14 years and above) per ha
female labor	number of adults female person-days (14 years and above) per ha
child labor	number of children person-days (under 14 years) per ha
machinery use	hours of equipment use her ha
manure	manure use on plot=1, 0 else
location	time in minutes to travel from home to the plot
erosion control	any anti-erosion structure built on plot=1, 0 else

Table 5

Covariates Balancing Test

	Standardized	differences	Variance	ratio
	Raw	Weighted	Raw	Weighted
Not registered herbicides
manager	0.538	0.538	2.662	2.660
village	-1.401	-1.401	0.451	0.451
subsidy	-0.283	-0.283	1.158	1.157
age	-0.192	-0.192	1.012	1.011
education	0.278	0.278	1.647	1.645
market	-0.301	-0.301	0.654	0.654
labor supply	0.334	0.334	1.216	1.215
weeding cost	0.732	0.732	12.777	12.768
individually-managed	0.559	0.559	2.745	2.743
seed	-0.069	-0.069	2.048	2.046
fertilizer	0.031	0.031	0.891	0.890
male labor	-0.141	-0.141	1.043	1.043
female labor	-0.192	-0.192	0.569	0.568
child labor	0.194	0.194	1.742	1.740
location	-0.033	-0.033	1.108	1.107
erosion control	-0.070	-0.070	0.999	0.999
machinery use	-0.313	-0.313	0.667	0.666
manure	0.020	0.020	1.016	1.015
Registered herbicides
manager	0.189	0.189	1.600	1.599
village	-0.824	-0.824	0.716	0.716
subsidy	-0.140	-0.140	1.099	1.098
age	-0.072	-0.072	1.022	1.021
education	0.180	0.180	1.427	1.426
market	-0.222	-0.222	0.751	0.750
labor supply	0.318	0.318	1.156	1.155
weeding cost	0.535	0.535	21.569	21.555
individually-managed	0.198	0.198	1.643	1.642
seed	0.180	0.180	1.887	1.886
fertilizer	0.521	0.521	2.954	2.952
male labor	0.146	0.146	1.423	1.422
female labor	-0.185	-0.185	0.710	0.709
child labor	0.154	0.154	1.518	1.517
location	-0.172	-0.172	1.092	1.092
erosion control	-0.251	-0.251	0.797	0.796
machinery use	-0.095	-0.095	0.967	0.966
manure	0.324	0.324	1.138	1.137

Table 6

Rosenbaum Bounds Analysis for the Main Outcomes

Outcome variables	Gamma	CI+	CI-
	1	-401.395	-189.458
Yield	1.2	-512.604	-77.024
	*1.4*	*-609.629*	*16.257*

	1	-7.902	-4.134
Male labor	1.2	-10.132	-2.312
	1.4	-12.194	-0.802
	*1.6*	*-14.034*	*0.524*

	1	-0.003	-0.003
	1.2	-0.003	-0.003
	1.4	-0.004	-0.003
	1.6	-0.004	-0.004
Female labor	1.8	-2.013	-0.004
	2	-2.885	-0.004
	2.2	-3.962	-0.004
	*2.4*	*-4.878*	*0.553*

	1	-0.003	-0.004
	1.2	-0.003	-0.004
	1.4	-0.704	-0.004
Children labor	1.6	-1.676	-0.003
	1.8	-2.102	-0.002
	2	-3.106	-0.003
	2.2	-3.484	-0.003
	*2.4*	*-3.896*	*0.549*

Table 7

Average Treatment Effects by Outcome and Model

	RA	AIPW	IPWRA	PSM
ATE on Yield
not registered	-219.4*	-204.0**	-152.1
registered	66.10	108.2	137.3
use herbicide				1.876
ATE on men weeding labor
not registered	-4.686**	-5.441**	-6.284***
registered	-7.973***	-8.674***	-9.327***
use herbicide				-8.061***
ATE on women weeding labor
not registered	-1.055	-0.920	-1.318
registered	-1.397	-1.788*	-2.144**
use herbicide				-1.599
ATE on children weeding labor
not registered	-1.620	-1.785	-2.279*
registered	-1.913*	-2.241**	-2.706**
use herbicide				-1.342
N	1136	1136	1136	1136

t statistics in parentheses

* p < 0.10, ** p < 0.05, *** p < 0.01

1.5 Tables

Table 2

Habit of Receiving Climate Information

Region	Type of Actor	Habit of receiving climate information?
No	Yes
Source
Agriculture	ANACIM	Yeuglé Plat form	Cowpea Sector Platform	Radio	Tele vision	Traditional Information
Diourbel	Participated in the Training	29%	–	57%	–	7%	7%	–	–
Extensive Experience on CI	–	–	25%	75%	–	0%	–	–
No Training but CI	100%	–	0%	–	–	0%	–	–
No Training and No CI	–	–	–	–	–	–	–	–
Fatick	Participated in the Training	12%	–	82%	–	–	0%	6%	–
Extensive Experience on CI		–	100%	–	–	0%	–
No Training but CI	64%	–	9%	–	–	18%	–	9%
No Training and No CI	89%	–	11%	–	–	0%	–	–
Kaffrine	Participated in the Training	11%	11%	67%	–	–	11%	–	–
Extensive Experience on CI	15%	–	77%	–	–	8%	–	–
No Training but CI	–	–	–	–	–	–	–	–
No Training and No CI	100%	–	–	–	–	–	–	–
Sample	33%	1%	52%	4%	1%	6%	1%	1%

Source: Author based on ISRA/BAME survey data, 2016

Table 3

Use of Climate Information

Region	Type of Actor	Did you use the climate and weather information you received to conduct your campaign?
Yes	No	Not Received
Diourbel	Participated in the Training	93%	0%	7%
Extensive Experience on CI	100%	0%	0%
No Training but CI	100%	0%	0%
Fatick	Participated in the Training	100%	0%	0%
Extensive Experience on CI	100%	0%	0%
No Training but IC	82%	9%	9%
No Training and No CI	11%	0%	89%
Kaffrine	Participated in the Training	89%	11%	0%
Extensive Experience on CI	100%	0%	0%
No Training and No CI	0%	0%	100%
Sample	84%	2%	13%

Source: Author based on ISRA/BAME survey data, 2016

Table 4

Types of Use of Climate Information

Region	Type of actor	Types of use of climate information
Crop Selec tion	Variety Selection	Plot Preparation	Ploughing Period	Sowing/ Seeding Period	NPK Applica tion Period	Weeding/ Hoeing Period
Diourbel	Participa- ted in the Training	54%	39%	0%	0%	8%	0%	0%
Extensive Experien- ce on CI	50%	50%	0%	0%	0%	0%	0%
No Training but CI	50%	50%	0%	0%	0%	0%	0%
Fatick	Participa- ted in the Training	18%	65%	0%	6%	12%	0%	0%
Extensive Experien- ce on CI	0%	0%	50%	0%	50%	0%	0%
No Training but CI	22%	11%	0%	0%	44%	11%	11%
No Training and No CI	100%	0%	0%	0%	0%	0%	0%
Kaffrine	Participa-ted in the Training	100%	0%	0%	0%	0%	0%	0%
Extensive Experience on CI	77%	23%	0%	0%	0%	0%	0%
No Training and No CI	–	–	–	–	–	–	–
Sample	48%	35%	1%	1%	12%	1%	1%

Source: Author based on ISRA/BAME survey data, 2016

2.7 Tables

Table 1

Characteristics of the Varieties Studied

Variety	Code	Type	Cycle (days)	Height (cm)	Potential yield (t/ha)	Panicle form	Photoperiod- sensitivity	Isohyet (mm)	Origin
Fadda	V1	Guinea (hybrid)	128	200-300	4.5	noncompact	mean	700-1000	Mali
NIELENI	V2	Guinea (hybrid)	115	300	4	semicompact	low	700-800	Mali
IS15401	V3	Guinea-Caudatum	115	400-450	2	semicompact	high	900-1200	Mali
PABLO	V4	Guinea (hybrid)	125	400	4	noncompact	mean	700-1000	Mali
CSM63E	V5	Guinea	90	400	2	noncompact	low	600-1000	Mali
SK5912	V6	Caudatum	170	200	2.5-3.5	semicompact	high	700-900	Nigeria
GRINKAN	V7	Caudatum	90	120	4	semicompact	non	500-800	Mali
SOUMBA	V8	Caudatum	115	250	2.5	semicompact	low	600-1000	Mali
621B	V9	Caudatum	105	175	2.5-3	semicompact	non	600-900	Senegal
F2-20	V10	Caudatum	110	210	3-5.3	semicompact	low	600-900	Senegal

Table 4

Performance of 10 Sorghum Varieties Under Two Water Treatments (Well-watered and Drought Stress) for Agro-morphological Traits Measured During 2018 and 2019 Field Trials

		NLA	PHS	DLW	PHT	SDW	YIELD
	V	ww	ds	ww	ds	ww	ds	ww	ds	ww	ds	ww	ds
Year 2018	V1	14.6cd	13.5bc	133.7bc	111.7bcd	5.7e	13.9cd	172.5cd	148.9cd	427.6bc	358.7abc	4183.4abc	3182.3b
V2	15.3bc	13.6bc	125.7bc	101.6bcd	16.8ab	28.7a	162.8d	140.4cde	470.2abc	349.3abc	3715.9d	3359.3b
V3	15.5abc	13.2bc	204.3a	179.3a	12.6bcd	25.8a	191.2bc	193.6ab	498.7ab	409.0abc	2001.5e	1817.6de
V4	15.5abc	13.8bc	200.6a	137.5b	10.7cde	17.3bc	205.6b	161.3bc	530.0ab	449.8ab	1606.4ef	1540.3e
V5	15.0bc	13.5bc	186.3a	193.5a	6.0e	16.6bc	235.3a	215.6a	603.2a	461.4a	1473.1f	1168f
V6	16.6a	15.9a	108.5cd	91.2cd	7.8de	9.9de	159.8d	133.6cde	408.0bc	424.4ab	3926abcd	2598.3c
V7	13.5d	12.4c	92.5d	86.2cd	14.2bc	19.2b	127.2e	108.9e	315.0c	294.5bc	3812.1cd	2006.9d
V8	16.0ab	14.4ab	147.5b	111.8bcd	22.4a	26.9a	168.1d	126.0de	496.8ab	339.9abc	3922bcd	3132.8b
V9	15.7abc	14.9ab	98.4d	74.0d	18.3ab	8.6e	152.2d	117.1de	321.5c	259.3c	4246.0ab	3851.7a
V10	15.1bc	13.8bc	141.0b	116.1bc	21.4a	28.3a	169.3cd	140.0cde	451.0abc	359.3abc	4349.3a	2643.02c
Grand mean	15.3a	13.9b	143.8a	120.3b	13.6b	19.5a	174.8a	148.5b	453.3a	370.6b	3271.85a	2526.21b
ANOVA
V	***	***	***	***	***	***	***	***	***	**	***	***
E	***		***		***		***		***		***
V×E	ns		**		***		*		Ns		***
Year 2019	V1	15.7ab	13.7a	120.4a	77.5a	13.7d	15.1e	173.2cd	127abcd	494.2bc	377.3abc	3876.8a	2424a
V2	16.5a	14.2a	140.6a	83.5a	14.9ab	16abcd	149.4de	120.1bcd	404.5bcd	259.0bc	2127.7cd	2070.3abc
V3	15.0ab	13.8a	163.1a	103.1a	11.9f	15.7de	216.7ab	170.4abc	329.6cd	323.0abc	1886.4d	1752.5bc
V4	15.2ab	14.3a	107.3a	38.3a	15.3a	16.1cd	204.6bc	183.2ab	791.2a	399.4ab	1233.9e	1124.5de
V5	15.5ab	14.3a	154.6a	68.5a	14.8ab	16.3bcd	250.8a	198.2a	564.5b	428.4a	771.2f	817.4e
V6	16.0a	15.3a	107.1a	60.8a	12.6e	16.1cd	137.9ef	88.4d	413.2bcd	274.0abc	3429.2ab	1582bcd
V7	13.5b	14.1a	106.5a	55.3a	14.5bc	16abcd	109.2f	93.4d	380.8bcd	246.5c	3166.7b	2027.6abc
V8	15.8ab	14.4a	124.5a	49.0a	15.1ab	17.2a	143def	106.1cd	330.4cd	267.8abc	2514.8c	2087.6ab
V9	15.1ab	14.4a	99.4a	53.5a	15.0ab	17.1ab	131.4ef	101.8cd	303.7d	272.0abc	2105.5cd	2037abc
V10	14.3ab	13.7a	149.4a	66.3a	14.0cd	16.7abc	140def	102.6cd	382.6bcd	278.3abc	3085.8b	1462.9cd
	Grand mean	15.3a	14.2b	130.5a	66.5b	14.2b	16.3a	165.7a	127.2b	427.3a	308.6b	2419.84a	1738.64b
	ANOVA
	V	*	ns	ns	ns	***	***	***	***	***	**	***	***
	E	***		***		***		***		***		***
	V×E	ns		ns		***		*		**		***
Both	Y	ns		***		***		***		***		***
years	V×Y	*		***		***		***		**		***
	E×Y	ns		***		***		ns		**		*
	V×E×Y	ns		**		*		ns		ns		***

Table 1

Type of Actor

Region	Type of Actor	Total
Participated in the Training	Extensive Experience on CI	No Training but CI	No Training and No CI
Diourbel	14	4	2	0	20
Fatick	17	2	11	9	39
Kaffrine	9	13	0	1	23
Groundnut Basin	40	19	13	10	82

Source: Author based on ISRA/BAME survey data, 2016

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