Agricultural technology adoption and land use: evidence for Brazilian municipalities

Figures & data

Figure 1. Brazilian biomes.

Table 1. Summary statistics.

Variable	Mean	Median	Standard deviation	Number of municipalities
Cropland area/(Cropland area + Pasture + Forest)	0.294	0.211	0.265	5507
Pasture area/(Cropland area + Pasture + Forest)	0.467	0.485	0.251	5507
Forest area/(Cropland area + Pasture + Forest)	0.239	0.189	0.190	5507
Mechanization – Number of tractors (with more than 100 hp) by cropland area	0.011	0.003	0.051	5507
Technical assistance – Number of agricultural engineers, agronomists and researchers by cropland area	0.003	0.000	0.121	5507
Average temperature in December, January and February (in °C)	24.602	24.882	1.947	5507
Average temperature in March, April and May (in °C)	22.856	23.370	2.855	5507
Average temperature in June, July and August (in °C)	20.306	20.674	4.121	5507
Average temperature in September, October and November (in °C)	23.160	23.586	3.279	5507
Average precipitation in December, January and February (in mm)	172.163	172.117	74.873	5507
Average precipitation in March, April and May (in mm)	130.146	113.608	63.149	5507
Average precipitation in June, July and August (in mm)	61.091	35.975	56.453	5507
Average precipitation in September, October and November (in mm)	101.032	115.383	51.412	5507

Table 2. Spatial autocorrelation statistics – Moran’s I*.

Variable**	Moran’s I	E(I)	p-value***
Cropland (cr)	0.6602	−0.0002	0.001
Pasture (pt)	0.6200	−0.0002	0.001
Forest (ft)	0.5234	−0.0002	0.001
Mechanization – Physical capital (k)	0.2617	−0.0002	0.001
Technical assistance – Human capital (h)	0.5154	−0.0002	0.001

Notes: * The neighborhood matrix $k15$ was chosen using the criterion set forth in Stakhovych and Bijmolt (2009) presented in the appendix (see Table A1). ** The variables are expressed in relation to the total. *** The empirical pseudo-significance is based on 99,999 random permutations.

Figure 2. Map of LISA clusters for selected variables.

Table 3. Model taxonomy.

Model	Equations	Simultaneity	Description
(1)	$N_{j,i}=f\left(N_{-j,i},T_i,C_i,E_i,N_{j\left(t-1\right),i},{\epsilon }_j\right)$	Feedback	In the feedback model, the equation that intends to explain the land use $j$ in region $i$ ($N_{j,i}$) has as determinants the other types of use ($N_{-j,i}$), the use of agricultural technology ($T_i$), climatic conditions ($C_i$), soil conditions ($E_i$) and the land use $j$ in region $i$in the past ($N_{j\left(t-1\right),i}$).
(2)	$N_{j,i}=f\left(W{N}_{j,i},N_{-j,i},T_i,C_i,E_i,N_{j\left(t-1\right),i},{\epsilon }_j\right)$	Feedback and lags	In this model, the equation that aims to explain the use of soil type $j$ in region $i$follows the previous structure with increase of spatial lag in the endogenous variable present on the left side of equation ($W{N}_{j,i}$), where $W$ is the spatial lag matrix.
(3)	$N_{j,i}=f\left(W{N}_{j,i},N_{-j,i},W{N}_{-j,i},T_i,C_i,E_i,N_{j\left(t-1\right),i},{\epsilon }_j\right)$	Feedback, lags and cross-lags	This model has as a novelty the insertion of endogenous variables spatially lagged from the right side ($W{N}_{-j,i}$).
(4)	$N_{j,i}=f\left(W{N}_{j,i},N_{-j,i},W{N}_{-j,i},T_i,W{T}_i,C_i,E_i,N_{j\left(t-1\right),i},{\epsilon }_j\right)$	Feedback, lags, cross-lags and lag in independent variable	Finally, we consider a complete model with feedback, spatial lag, cross-lag and spatial lag in an independent variable, more specifically in the variable of use of agricultural technology ($W{T}_i$).

Table B1. Determinants of agricultural and forest land use for model (1): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Robinson. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.518	0.027	-19.185	-0.520	0.028	-18.571	-0.441	0.033	-13.364
Mechanization - Physical capital (k)	0.000	0.006	0.000	0.007	0.006	1.167	0.001	0.007	0.143
Technical advice - Human capital (h)	-0.007	0.006	-1.167	0.001	0.006	0.167	-0.008	0.007	-1.143
Climate - December, January and February (cdjf)	0.115	0.042	2.738	-0.055	0.045	-1.222	-0.075	0.052	-1.442
Climate - March, April and May (cmam)	0.029	0.024	1.208	-0.012	0.024	-0.500	-0.007	0.029	-0.241
Climate - June, July and August (cjja)	0.047	0.023	2.043	-0.055	0.025	-2.200	-0.032	0.029	-1.103
Climate - September, October and November (cson)	-0.044	0.040	-1.100	0.053	0.043	1.233	0.124	0.049	2.531
Biome - Caatinga (dcaa)	0.090	0.035	2.571	0.016	0.037	0.432	-0.012	0.044	-0.273
Biome - Cerrado (dcer)	0.102	0.029	3.517	-0.029	0.031	-0.935	-0.054	0.038	-1.421
Biome - Pampa (dpmp)	0.115	0.048	2.396	0.059	0.055	1.073	0.037	0.057	0.649
Biome - Pantanal (dptn)	-0.047	0.150	-0.313	0.099	0.159	0.623	-0.197	0.184	-1.071
Biome - Atlantic Forest (dmatl)	0.134	0.030	4.467	-0.009	0.030	-0.300	-0.039	0.036	-1.083
Cropland (cr)				-0.977	0.026	-37.577	-1.443	0.043	-33.558
Pasture (pt)	-0.906	0.029	-31.241				-1.401	0.042	-33.357
Forest (ft)	-0.672	0.020	-33.600	-0.699	0.029	-24.103
Dependent variable with time lag (Nt-1)	-0.030	0.017	-1.765	-0.026	0.016	-1.625	-0.048	0.014	-3.429
Dependent variable with spatial and time lag (WNt-1)	0.023	0.018	1.278	0.049	0.017	2.882	0.014	0.024	0.583
AIC	5313.14			5904.52			7539.34
R-squared	0.847			0.829			0.771

Table B2. Determinants of agricultural and forest land use for model (2): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Robinson. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.521	0.027	-19.296	-0.520	0.028	-18.571	-0.437	0.033	-13.242
Dependent variable spatially lagged (prWN)	0.092	0.058	1.586	0.062	0.043	1.442	0.197	0.055	3.582
Mechanization - Physical capital (k)	-0.002	0.006	-0.333	0.008	0.006	1.333	0.001	0.007	0.143
Technical advice - Human capital (h)	-0.011	0.006	-1.833	0.002	0.006	0.333	-0.001	0.007	-0.143
Climate - December, January and February (cdjf)	0.104	0.043	2.419	-0.049	0.045	-1.089	-0.08	0.052	-1.538
Climate - March, April and May (cmam)	0.032	0.025	1.280	-0.019	0.025	-0.760	0.007	0.030	0.233
Climate - June, July and August (cjja)	0.041	0.023	1.783	-0.053	0.025	-2.120	-0.021	0.029	-0.724
Climate - September, October and November (cson)	-0.041	0.040	-1.025	0.046	0.043	1.070	0.113	0.049	2.306
Biome - Caatinga (dcaa)	0.093	0.035	2.657	0.010	0.037	0.270	-0.018	0.044	-0.409
Biome - Cerrado (dcer)	0.104	0.029	3.586	-0.027	0.031	-0.871	-0.059	0.038	-1.553
Biome - Pampa (dpmp)	0.124	0.048	2.583	0.057	0.055	1.036	0.011	0.058	0.190
Biome - Pantanal (dptn)	-0.056	0.150	-0.373	0.118	0.159	0.742	-0.201	0.184	-1.092
Biome - Atlantic Forest (dmatl)	0.139	0.030	4.633	-0.009	0.030	-0.300	-0.040	0.036	-1.111
Cropland (cr)				-0.928	0.043	-21.581	-1.226	0.074	-16.568
Pasture (pt)	-0.840	0.051	-16.471				-1.191	0.072	-16.542
Forest (ft)	-0.618	0.039	-15.846	-0.664	0.037	-17.946
Dependent variable with time lag (Nt-1)	0.002	0.026	0.077	-0.009	0.020	-0.450	-0.009	0.018	-0.500
Dependent variable with spatial and time lag (WNt-1)	-0.023	0.034	-0.676	0.023	0.025	0.920	-0.054	0.031	-1.742
AIC	5312.59			5904.52			7528.56
R-squared	0.847			0.829			0.771

Table B3. Determinants of agricultural and forest land use for model (3): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Robinson. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.520	0.027	-19.259	-0.519	0.028	-18.536	-0.447	0.033	-13.545
Dependent variable spatially lagged (prWN)	0.548	0.155	3.535	0.687	0.148	4.642	0.737	0.127	5.803
Mechanization - Physical capital (k)	-0.001	0.006	-0.167	0.008	0.006	1.333	0.003	0.007	0.429
Technical advice - Human capital (h)	-0.010	0.006	-1.667	0.001	0.006	0.167	-0.003	0.007	-0.429
Climate - December, January and February (cdjf)	0.094	0.043	2.186	-0.048	0.045	-1.067	-0.073	0.052	-1.404
Climate - March, April and May (cmam)	0.033	0.025	1.320	-0.012	0.025	-0.480	0.002	0.030	0.067
Climate - June, July and August (cjja)	0.045	0.023	1.957	-0.049	0.025	-1.960	-0.015	0.029	-0.517
Climate - September, October and November (cson)	-0.051	0.040	-1.275	0.036	0.043	0.837	0.090	0.049	1.837
Biome - Caatinga (dcaa)	0.087	0.035	2.486	0.002	0.038	0.053	-0.018	0.044	-0.409
Biome - Cerrado (dcer)	0.103	0.029	3.552	-0.022	0.031	-0.710	-0.044	0.038	-1.158
Biome - Pampa (dpmp)	0.123	0.048	2.563	0.038	0.055	0.691	0.026	0.058	0.448
Biome - Pantanal (dptn)	-0.063	0.150	-0.420	0.106	0.159	0.667	-0.205	0.183	-1.120
Biome - Atlantic Forest (dmatl)	0.139	0.030	4.633	-0.011	0.031	-0.355	-0.030	0.036	-0.833
Cropland (cr)				-0.935	0.046	-20.326	-1.339	0.078	-17.167
Pasture (pt)	-0.859	0.051	-16.843				-1.351	0.080	-16.888
Forest (ft)	-0.647	0.049	-13.204	-0.633	0.058	-10.914
Cropland with spatial lag (prWcr)				0.657	0.153	4.294	0.892	0.191	4.670
Pasture with spatial lag (prWpt)	0.436	0.140	3.114				0.908	0.183	4.962
Forest with spatial lag (prWft)	0.349	0.109	3.202	0.426	0.117	3.641
Dependent variable with time lag (Nt-1)	-0.007	0.027	-0.259	-0.008	0.022	-0.364	-0.033	0.019	-1.737
Dependent variable with spatial and time lag (WNt-1)	-0.031	0.034	-0.912	0.009	0.029	0.310	-0.009	0.032	-0.281
AIC	5306.17			5888.51			7506.37
R-squared	0.847			0.831			0.772

Table B4. Determinants of agricultural and forest land use for model (4): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Robinson. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.526	0.027	-19.481	-0.518	0.028	-18.500	-0.450	0.033	-13.636
Dependent variable spatially lagged (WN)	0.511	0.155	3.297	0.685	0.148	4.628	0.734	0.127	5.780
Mechanization - Physical capital (k)	0.001	0.006	0.167	0.008	0.006	1.333	0.003	0.007	0.429
Technical advice - Human capital (h)	-0.007	0.006	-1.167	0.001	0.006	0.167	-0.003	0.007	-0.429
Physical Capital with spatial lag (Wk)	-0.071	0.023	-3.087	0.022	0.025	0.880	-0.042	0.029	-1.448
Human Capital with spatial lag (Wh)	-0.015	0.026	-0.577	-0.031	0.025	-1.240	0.049	0.028	1.750
Climate - December, January and February (cdjf)	0.093	0.044	2.114	-0.056	0.046	-1.217	-0.063	0.052	-1.212
Climate - March, April and May (cmam)	0.013	0.025	0.520	-0.009	0.025	-0.360	-0.005	0.03	-0.167
Climate - June, July and August (cjja)	0.060	0.024	2.500	-0.053	0.025	-2.120	-0.010	0.029	-0.345
Climate - September, October and November (cson)	-0.065	0.040	-1.625	0.034	0.044	0.773	0.088	0.050	1.760
Biome - Caatinga (dcaa)	0.095	0.035	2.714	0.000	0.038	0.000	-0.014	0.044	-0.318
Biome - Cerrado (dcer)	0.104	0.030	3.467	-0.025	0.031	-0.806	-0.039	0.038	-1.026
Biome - Pampa (dpmp)	0.144	0.049	2.939	0.048	0.056	0.857	0.024	0.058	0.414
Biome - Pantanal (dptn)	-0.056	0.150	-0.373	0.11	0.159	0.692	-0.206	0.183	-1.126
Biome - Atlantic Forest (dmatl)	0.147	0.030	4.900	-0.011	0.031	-0.355	-0.028	0.037	-0.757
Cropland (cr)				-0.923	0.048	-19.229	-1.330	0.082	-16.220
Pasture (pt)	-0.906	0.056	-16.179				-1.338	0.084	-15.929
Forest (ft)	-0.712	0.054	-13.185	-0.621	0.061	-10.180
Cropland with spatial lag (Wcr)				0.653	0.153	4.268	0.887	0.192	4.620
Pasture with spatial lag (Wpt)	0.422	0.140	3.014				0.901	0.185	4.870
Forest with spatial lag (Wft)	0.378	0.109	3.468	0.423	0.117	3.615
Dependent variable with time lag (Nt-1)	-0.035	0.029	-1.207	-0.003	0.023	-0.130	-0.031	0.019	-1.632
Dependent variable with spatial and time lag (WNt-1)	-0.030	0.035	-0.857	0.012	0.030	0.400	-0.004	0.032	-0.125
AIC	5296.38			5890.88			7506.74
R-squared	0.847			0.830			0.772
Breusch-Pagan test of independence	735.54***

Figure 3. Determinants of agricultural and forest land use for model (4): estimation by system of simultaneous equations in space using procedure of Kelejian and Robinson (1993) – Period: 2006.

Table B5. Determinants of agricultural and forest land use for model (1): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Prucha. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.004	0.012	-0.333	-0.004	0.013	-0.308	-0.004	0.017	-0.235
Mechanization - Physical capital (k)	0.000	0.003	0.000	0.001	0.003	0.333	0.001	0.004	0.250
Technical advice - Human capital (h)	0.001	0.003	0.333	0.001	0.003	0.333	0.001	0.004	0.250
Climate - December, January and February (cdjf)	0.001	0.019	0.053	0.001	0.020	0.050	0.001	0.027	0.037
Climate - March, April and May (cmam)	-0.013	0.011	-1.182	-0.014	0.012	-1.167	-0.017	0.015	-1.133
Climate - June, July and August (cjja)	-0.015	0.010	-1.500	-0.016	0.011	-1.455	-0.021	0.014	-1.500
Climate - September, October and November (cson)	0.040	0.019	2.105	0.042	0.020	2.100	0.056	0.026	2.154
Biome - Caatinga (dcaa)	0.024	0.017	1.412	0.025	0.017	1.471	0.033	0.023	1.435
Biome - Cerrado (dcer)	-0.003	0.013	-0.231	-0.003	0.014	-0.214	-0.005	0.018	-0.278
Biome - Pampa (dpmp)	0.024	0.022	1.091	0.025	0.023	1.087	0.031	0.030	1.033
Biome - Pantanal (dptn)	0.020	0.072	0.278	0.022	0.076	0.289	0.028	0.099	0.283
Biome - Atlantic Forest (dmatl)	-0.002	0.014	-0.143	-0.003	0.014	-0.214	-0.004	0.019	-0.211
Cropland (cr)				-1.056	0.002	-528.000	-1.374	0.004	-343.500
Pasture (pt)	-0.947	0.002	-473.500				-1.301	0.004	-325.250
Forest (ft)	-0.726	0.002	-363.000	-0.767	0.002	-383.500
R-squared	0.963			0.959			0.931

Table B6. Determinants of agricultural and forest land use for model (2): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Prucha. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.004	0.012	-0.333	-0.004	0.013	-0.308	-0.006	0.016	-0.375
Dependent variable spatially lagged (WN)	0.001	0.002	0.500	0.002	0.002	1.000	0.005	0.003	1.667
Mechanization - Physical capital (k)	0.000	0.003	0.000	0.001	0.003	0.333	0.001	0.004	0.250
Technical advice - Human capital (h)	0.000	0.003	0.000	0.001	0.003	0.333	0.001	0.004	0.250
Climate - December, January and February (cdjf)	0.001	0.019	0.053	0.001	0.020	0.050	0.000	0.026	0.000
Climate - March, April and May (cmam)	-0.013	0.011	-1.182	-0.014	0.011	-1.273	-0.018	0.015	-1.200
Climate - June, July and August (cjja)	-0.014	0.010	-1.400	-0.015	0.011	-1.364	-0.020	0.014	-1.429
Climate - September, October and November (cson)	0.039	0.018	2.167	0.041	0.019	2.158	0.054	0.025	2.160
Climate - December, January and February squared (cdjf2)	-0.019	0.015	-1.267	-0.020	0.016	-1.250	-0.025	0.021	-1.190
Climate - March, April and May squared (cmam2)	0.029	0.011	2.636	0.030	0.012	2.500	0.039	0.015	2.600
Climate - June, July and August squared (cjja2)	0.004	0.007	0.571	0.004	0.007	0.571	0.005	0.009	0.556
Climate - September, October and November squared (cson2)	-0.022	0.014	-1.571	-0.023	0.015	-1.533	-0.030	0.019	-1.579
Biome - Caatinga (dcaa)	0.024	0.016	1.500	0.025	0.017	1.471	0.033	0.022	1.500
Biome - Cerrado (dcer)	-0.002	0.013	-0.154	-0.003	0.014	-0.214	-0.003	0.018	-0.167
Biome - Pampa (dpmp)	0.025	0.022	1.136	0.027	0.023	1.174	0.034	0.030	1.133
Biome - Pantanal (dptn)	0.021	0.071	0.296	0.022	0.075	0.293	0.030	0.098	0.306
Biome - Atlantic Forest (dmatl)	-0.001	0.013	-0.077	-0.002	0.014	-0.143	-0.002	0.018	-0.111
Cropland (cr)				-1.053	0.003	-351.000	-1.372	0.005	-274.400
Pasture (pt)	-0.947	0.003	-315.667				-1.300	0.005	-260.000
Forest (ft)	-0.724	0.002	-362.000	-0.764	0.003	-254.667
R-squared	0.964			0.959			0.931

Table B7. Determinants of agricultural and forest land use for model (3): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Prucha. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.007	0.012	-0.583	-0.007	0.013	-0.538	-0.009	0.017	-0.529
Dependent variable spatially lagged (WN)	0.217	0.047	4.617	0.221	0.047	4.702	0.225	0.047	4.787
Mechanization - Physical capital (k)	0.001	0.003	0.333	0.001	0.003	0.333	0.001	0.004	0.250
Technical advice - Human capital (h)	0.001	0.003	0.333	0.001	0.003	0.333	0.001	0.004	0.250
Climate - December, January and February (cdjf)	0.002	0.02	0.100	0.002	0.021	0.095	0.002	0.027	0.074
Climate - March, April and May (cmam)	-0.011	0.011	-1.000	-0.012	0.011	-1.091	-0.015	0.015	-1.000
Climate - June, July and August (cjja)	-0.01	0.011	-0.909	-0.011	0.011	-1.000	-0.015	0.015	-1.000
Climate - September, October and November (cson)	0.031	0.019	1.632	0.033	0.02	1.650	0.043	0.026	1.654
Biome - Caatinga (dcaa)	0.024	0.016	1.500	0.025	0.017	1.471	0.032	0.022	1.455
Biome - Cerrado (dcer)	0.002	0.013	0.154	0.002	0.014	0.143	0.003	0.018	0.167
Biome - Pampa (dpmp)	0.024	0.022	1.091	0.026	0.023	1.130	0.033	0.03	1.100
Biome - Pantanal (dptn)	0.02	0.071	0.282	0.021	0.075	0.280	0.028	0.098	0.286
Biome - Atlantic Forest (dmatl)	0.003	0.014	0.214	0.003	0.014	0.214	0.003	0.019	0.158
Cropland (cr)				-1.051	0.004	-262.750	-1.367	0.006	-227.833
Pasture (pt)	-0.948	0.003	-316.000				-1.299	0.005	-259.800
Forest (ft)	-0.727	0.003	-242.333	-0.765	0.003	-255.000
Cropland with spatial lag (Wcr)				0.227	0.05	4.540	0.293	0.065	4.508
Pasture with spatial lag (Wpt)	0.208	0.045	4.622				0.284	0.061	4.656
Forest with spatial lag (Wft)	0.161	0.034	4.735	0.17	0.036	4.722
R-squared	0.964			0.959			0.932

Table B8. Determinants of agricultural and forest land use for model (4): estimation by System of Simultaneous Equations in Space using procedure of Kelejian and Prucha. Period: 2006.

	Cropland			Pasture			Forest
Explanatory variables	coef.	std. error	t-value	coef.	std. error	t-value	coef.	std. error	t-value
Constant	-0.010	0.012	-0.833	-0.010	0.013	-0.769	-0.013	0.017	-0.765
Dependent variable spatially lagged (WN)	0.213	0.047	4.532	0.218	0.047	4.638	0.223	0.047	4.745
Mechanization - Physical capital (k)	0.001	0.003	0.333	0.001	0.003	0.333	0.001	0.004	0.250
Technical advice - Human capital (h)	0.001	0.003	0.333	0.001	0.003	0.333	0.001	0.004	0.250
Physical Capital with spatial lag (Wk)	-0.020	0.011	-1.818	-0.020	0.011	-1.818	-0.027	0.015	-1.800
Human Capital with spatial lag (Wh)	0.002	0.011	0.182	0.002	0.011	0.182	0.002	0.015	0.133
Climate - December, January and February (cdjf)	0.004	0.020	0.200	0.004	0.021	0.190	0.005	0.027	0.185
Climate - March, April and May (cmam)	-0.014	0.011	-1.273	-0.015	0.012	-1.250	-0.020	0.015	-1.333
Climate - June, July and August (cjja)	-0.008	0.011	-0.727	-0.008	0.011	-0.727	-0.011	0.015	-0.733
Climate - September, October and November (cson)	0.027	0.019	1.421	0.028	0.020	1.400	0.037	0.026	1.423
Biome - Caatinga (dcaa)	0.027	0.016	1.688	0.028	0.017	1.647	0.037	0.023	1.609
Biome - Cerrado (dcer)	0.005	0.013	0.385	0.005	0.014	0.357	0.006	0.018	0.333
Biome - Pampa (dpmp)	0.028	0.022	1.273	0.030	0.023	1.304	0.038	0.030	1.267
Biome - Pantanal (dptn)	0.022	0.071	0.310	0.023	0.075	0.307	0.031	0.098	0.316
Biome - Atlantic Forest (dmatl)	0.006	0.014	0.429	0.006	0.015	0.400	0.008	0.019	0.421
Cropland (cr)				-1.051	0.004	-262.750	-1.367	0.006	-227.833
Pasture (pt)	-0.948	0.003	-316.000				-1.298	0.005	-259.600
Forest (ft)	-0.727	0.003	-242.333	-0.766	0.003	-255.333
Cropland with spatial lag (Wcr)				0.223	0.050	4.460	0.288	0.065	4.431
Pasture with spatial lag (Wpt)	0.205	0.045	4.556				0.279	0.061	4.574
Forest with spatial lag (Wft)	0.160	0.034	4.706	0.169	0.036	4.694
R-squared	0.963			0.959			0.932

Figure 4. Determinants of agricultural and forest land use for model (4): estimation by system of simultaneous equations in space using procedure of Kelejian and Prucha (2004) – Period: 2006.

Table A1. Stakhovych and Bijmolt Criteria for selection of spatial lag matrix*.

Spatial lag matrix	Cropland (cr)	Pasture (pt)	Forest (ft)
1-nearest neighbor (k1)	7635.49	7815.27	9378.89
5-nearest neighbors (k5)	7288.16	7724.41	9337.82
10-nearest neighbors (k10)	7183.67	7715.99	9357.30
15-nearest neighbors (k15)	7125.28	7670.22	9319.87
20-nearest neighbors (k20)	7138.73	7684.61	9339.26

Note: *The values within the table represent the Akaike criterion (AIC). The 15-nearest neighbors (k15) matrix was selected.

Table A2. Description of the variables.

	Description		Source	Period
I. Endogenous Variables (N) Land Use
cr	Cropland area / (Cropland area + Pasture + Forest)		Agricultural Census (IBGE)	2006
pt	Pasture area / (Cropland area + Pasture + Forest)
ft	Forest area / (Cropland area + Pasture + Forest)
II. Exogenous Variables (T) Agricultural Technology
k	Mechanization - Number of tractors (with more than 100 hp) by cropland area		Agricultural Census (IBGE)	2006
h	Technical asssistance - Number of agricultural engineers, agronomists and researchers by cropland area		RAIS	2006
(C) Climatic Conditions
tdjf	Average temperature in December, January and February (in °C)		NCEP-DOE Reanalysis 2 project and Climate Hazards group Infrared Precipitation with Station (CHIRPS)	2005-2006
tmam	Average temperature in March, April and May (in °C)
tjja	Average temperature in June, July and August (in °C)
tson	Average temperature in September, October and November (in °C)
pdjf	Average precipitation in December, January and February (in mm)
pmam	Average precipitation in March, April and May (in mm)
pjja	Average precipitation in June, July and August (in mm)
pson	Average precipitation in September, October and November (in mm)
cdjf	Interaction between variables tdjf and pdjf		Self elaboration	2005-2006
cmam	Interaction between variables tmam and pmam
cjja	Interaction between variables tjja and pjja
cson	Interaction between variables tson and pson
(E) Edaphic Conditions
damz	Biome - Amazon (= 1)		IBGE (shapefile)	2006
dcer	Biome - Cerrado (= 1)
dmatl	Biome - Atlantic Forest (= 1)
dcaa	Biome - Caatinga (= 1)
dpmp	Biome - Pampa (= 1)
dptn	Biome - Pantanal (= 1)
(S) Spatial Interdependence
Wk, Wh		Technological spillover - average number of tractors and qualified professionals in the nearest neighbors	Self elaboration	2006
WN		Spatial dependence - average of the type n soil used in the nearest neighbors
III. Variable Instruments (time-lag land use)
cr(t-1)	Cropland area / (Cropland area + Pasture + Forest) with time lag		Agricultural Census (IBGE)	1995/1996
pt(t-1)	Pasture area / (Cropland area + Pasture + Forest) with time lag
ft(t-1)	Forest area / (Cropland area + Pasture + Forest) with time lag

Stakhovych, S., & Bijmolt, T.H.A. (2009). Specification of spatial models: A simulation study on weights matrices. Papers in Regional Science, 88(2), 389–408.

 Web of Science ®Google Scholar

Kelejian, H.H., & Robinson, D.P. (1993). A suggested method of estimation for spatial interdependent models with autocorrelated errors, and an application to a county expenditure model. Papers in Regional Science, 72(3), 297–312.

 Google Scholar

Kelejian, H.H., & Prucha, I.R. (2004). Estimation of simultaneous systems of spatially interrelated cross sectional equations. Journal of Econometrics, 118(1–2), 27–50.

 Web of Science ®Google Scholar