Calibration and Validation of the AquaCrop Model to Estimate Maize Production in Campos Gerais, Paraná State, Brazil

Souza, Jorge Luiz Moretti de; Oliveira, Cibelle Tamiris de; Rosa, Stefanie Lais Kreutz; Tsukahara, Rodrigo Yoiti

doi:10.1590/0102-7786352001

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Artigo • Rev. bras. meteorol. 35 (2) • Apr-Jun 2020 • https://doi.org/10.1590/0102-7786352001 copy

Calibration and Validation of the AquaCrop Model to Estimate Maize Production in Campos Gerais, Paraná State, Brazil

Calibração e Validação do Modelo AquaCrop para Estimar a Produtividade de Milho nos Campos Gerais, Estado do Paraná, Brasil

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Crop productivity evaluation with models simulations can help in the prediction of harvests and in the understanding of the interactions resulting from the soil-plant-atmosphere continuum. The aim of this study was to calibrate and validate the AquaCrop model for maize crop in the edaphoclimatic conditions of Campos Gerais region, Paraná State, Brazil. The analyses were carried out for maize crop with model input data (climate, crop, soil and soil management) obtained from the ABC Foundation Experimental Station in Castro, Ponta Grossa and Socavão. The climate in the region is humid subtropical, with rainfall evenly distributed. The relief varies from flat to gently undulating. The period analyzed in the calibration and validation process comprised 2011 to 2016 and 2012 to 2016 harvests, respectively. The data used in the calibration of AquaCrop was different from those used in the validation process. Observed and simulated yields were evaluated by simple linear regression analyses, absolute and relative errors, correlation coefficient (r), concordance (d) and performance (c) indexes. The calibration of AquaCrop was satisfactory in the locations studied for maize crop, obtaining absolute errors varying from 6 to 121 kg ha^–1. The highest calibration errors occurred in Castro. However, the errors were not enough to reduce the performance in the validation process for this localitie. The model validation resulted in “excellent” performance in all locations evaluated. The AquaCrop can be used to predict the maize yield with acceptable accuracy in the Campos Gerais Region, Paraná State, Brazil.

Keywords:
agricultural production; crop models; modeling; Zea mays

Localitie	Year	Maximum Temp. (°C)	Minimum Temp. (°C)	Precipitation (mm)	Relative Humidity (%)	Wind speed>/> (m s^–1)	Evapotranspiration (mm)
Castro	2011	22.3	12.0	4.6	88.4	2.1	2.2
	2012	23.5	12.7	4.4	88.2	2.1	2.3
	2013	23.0	12.2	4.0	86.3	2.2	2.3
	2014	24.1	13.6	5.3	90.5	2.1	2.4
Socavão	2011	23.5	11.8	16.8	3.8	84.8	2.0
	2012	23.4	12.4	17.1	5.2	88.0	2.3
	2013	22.3	11.9	16.4	4.5	88.2	2.7
Ponta Grossa	2012	24.8	13.7	18.5	4.7	79.9	3.1
	2013	24.1	13.0	17.8	4.7	80.5	2.9
	2014	25.0	14.0	18.7	4.5	80.7	2.9
	2015	25.3	14.4	18.8	5.6	91.9	1.7
	2016	27.9	17.2	21.5	6.2	89.5	1.6

Localitie	Layer (m)	Texture	Soil water content (m³ m⁻³)			K_sat⁽⁴⁾ 4 Saturated hydraulic conductivity. (mm day⁻¹)
Localitie	Layer (m)	Texture	θ_PWP⁽¹⁾ 1 Volumetric water content at wilting point.	θ_FC⁽²⁾ 2 Volumetric water content at field capacity.	θ_Sat⁽³⁾ 3 Volumetric water content at saturation.	K_sat⁽⁴⁾ 4 Saturated hydraulic conductivity. (mm day⁻¹)
Castro	0.00–0.10	Clay	0.36	0.50	0.63	418.32
Castro	0.10–0.25	Clay	0.33	0.47	0.60	368.23
Castro	0.25–0.40	Clay	0.32	0.45	0.62	325.74
Ponta Grossa	0.00–0.10	Clay	0.20	0.39	0.51	743.27
Ponta Grossa	0.10–0.25	Clay	0.20	0.35	0.50	732.57
Ponta Grossa	0.25–0.40	Clay	0.25	0.36	0.54	636.30
Socavão	0.00–0.10	Clay	0.28	0.43	0.57	335.96
Socavão	0.10–0.25	Clay	0.27	0.41	0.59	351.07
Socavão	0.25–0.40	Clay	0.24	0.40	0.59	355.54

Symbol	Description	Values
WP*	Water productivity normalized for ETo and CO₂ (g m⁻²)	32 to 33
Kc _TR,x	Crop coefficient when canopy is complete but prior to senescence	1.05
CDC	Canopy decline coefficient (% day⁻¹; GDD)	0.427 to 0.489
HI _o	Reference harvest index (%)	30 to 53

	Calibration						Validation
Cultivar	Harvest	Yr	Ys	\|MAE\|	MRE	Cultivar	Harvest	Yr	Ys	\|MAE\|	MRE
		kg ha⁻¹	%	kg ha⁻¹	%			kg ha⁻¹	kg ha⁻¹	kg ha⁻¹	%
Castro
P32R22H	2011/12	12453	12550	97	0.77	30R50YH	2014/15	14418	14530	112	0.77
P30R50YH	2013/14	13554	13440	114	0.85	AG8041PRO	2014/15	14293	14383	90	0.63
AG8041PRO	2013/14	13078	13088	10	0.08	30F53YH	2014/15	14439	14482	43	0.30
P30F53YH	2013/14	14681	14659	22	0.15	Velox TL	2014/15	14111	14247	136	0.95
Celeron TL	2013/14	12987	12896	91	0.71	WXA 504 Waxy	2014/15	12495	12474	21	0.17
DKB240PRO	2015/16	14348	14366	18	0.13	DKB 240PRO	2014/15	14663	14741	78	0.53
P30R50YH	2015/16	12734	12819	85	0.66	30R50YH	2014/15	10940	10948	8	0,07
P1630H	2015/16	9815	9859	44	0.45	AG8041PRO	2014/15	10902	10774	128	1.19
P2530	2015/16	10188	10309	121	1.17	P32R22H	2014/15	10746	10715	31	0.29
DKB240PRO	2015/16	9497	9400	97	1.03	30F53YH	2014/15	12340	12208	132	1.08
P30R50YH	2015/16	7758	7830	72	0.92	Velox TL	2014/15	9962	9850	112	1.14
All harvests	—	11918	11929	70.09	0.63	AS 1656PRO2	2014/15	10884	10812	72	0.67
—	—	—	—	—	—	WXA 504 Waxy	2014/15	10899	10768	131	1.22
—	—	—	—	—	—	DKB 240PRO	2014/15	9309	9663	354	3.66
—	—	—	—	—	—	All harvests	—	12172	12185	103.43	0.90
Ponta Grossa
P30R50H	2012/13	12239	12233	6	0.05	30R50YH	2015/16	11902	11738	164	1.40
P32R22H	2013/14	10233	10117	116	1.15	AG8041PRO	2015/16	12681	12750	69	0.54
P32R22H	2013/14	10262	10192	70	0.69	P32R22H	2015/16	10520	10517	3	0.03
P30F53YH	2014/15	11369	11336	33	0.29	30F53YH	2015/16	11306	11303	3	0.03
AG8041PRO	2015/16	11099	11217	118	1.05	Velox TL	2015/16	11663	11781	118	1.00
Velox TL	2015/16	10017	10060	43	0.43	AS 1656PRO2	2015/16	13401	13470	69	0.51
AS 1656PRO2	2015/16	11347	11465	118	1.03	WXA 504 Waxy	2015/16	11010	11073	63	0.57
WXA 504 Waxy	2015/16	10417	10530	113	1.07	DKB 240PRO	2015/16	12273	12144	129	1.06
DKB 240PRO	2015/16	12577	12639	62	0.49	30R50YH	2015/16	11878	11885	7	0.06
All harvests	—	11062	11088	75.44	0.69	P32R22H	2015/16	9352	9391	39	0.42
—	—	—	—	—	—	30F53YH	2015/16	11383	11471	88	0.77
—	—	—	—	—	—	All harvests	—	11579	11593	68.36	0.58
Socavão
P30R50H	2011/12	13513	13584	71	0.52	P30R50YH	2012/13	10659	11837	1178	9.95
AG8041YG	2011/12	11981	11924	57	0.48	AG8041PRO	2012/13	13341	13395	54	0.40
P32R22H	2011/12	13713	13741	28	0.20	P30R50YH	2013/14	13110	13239	129	0.97
P1630H	2012/13	12045	12021	24	0.20	AG8041PRO	2013/14	13661	13686	25	0.18
30F53YH	2012/13	13155	13218	63	0.48	P2530H	2013/14	13674	13507	167	1.24
Celeron TL	2012/13	11606	11538	68	0.59	30F53YH	2013/14	13970	13820	150	1.09
All harvests	—	12669	12671	51.83	0.41	Celeron TL	2013/14	13002	13171	169	1.28
—	—	—	—	—	—	—	—	13060	13236	267.43	2.16
All localities	—	11795	11809	67.73	0.60	—	—	12162	12212	127.25	1.07

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E-mail: sbmet@sbmet.org.br

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[1] Autor de correspondência: Stefanie Lais Kreutz Rosa, skreutzrosa@gmail.com.