Pasture Science and other Crops

Influence of climate on corn (Zea mays) yield in the center of Buenos Aires province, Argentina

0000-0002-6088-9759María C. Miranda del Fresno^{1  *}  , 0000-0003-3402-3537Adriana Confalone² 

¹Centro de Investigaciones y Estudios Ambientales (CINEA-CICPBA), Facultad de Ciencias Humanas, UNCPBA. Paraje Arroyo Seco s/n, 7000 Tandil, Provincia de Buenos Aires. Argentina

²Núcleo de Estudios en Actividades Agropecuarias y Cambio Climático (NAACCE), Facultad de Agronomía, UNCPBA. Av. República de Italia 780, 7300 Azul, Provincia de Buenos Aires. Argentina

ABSTRACT

The crop yield is the result of the interactions of the continuum soil-plant-atmosphere in the time. The understanding of these interactions and the description of climate effects on the yield requires a constant effort to reduce the negative effects of the climatic changes. The objective of this study was to determine the influence of agroclimatic variables on the increase of corn (Zea mays) yield for the last decades in Azul municipality, Buenos Aires province, Argentina. The yield series of Ministerio de Agricultura was compared, where a tendency to the increase was observed, with a yield series corrected through a fit equation, to which the technology effect and two yield series simulated in the program Decision Support System for Agrotechnology: "potential yield" and dry "potential yield" for an agricultural soil were take off . These results were linked with agroclimatic variables of maximum and minimum temperature, rainfalls and global radiations. It could verified that when the technology effects disappear, decrease yield (30 %). The increase of potential yield (6.20 %) is associated with the increase of global radiation and temperature. The decrease of rainfalls in the last decade (30 %) cause the fall of the corrected yield series (11 %) and the dry potential yield series (25 %). It is concluded that the climate has a negative effect on yield, but the technology hide it. The decrease of rainfalls in the spring-summer months is the most influence element in the yield.

Key words: crop; agroclimatic variables; technology; Decision Support System for Agrotechnology Transfer

The pampas agroecosystems record deep changes since some decades, resulted in an expansion to more dry areas, mainly due to economic, institutional and climatic conditions, and also to the incorporation of new technologies (^{Bert et al. 2021}). The increase of international prices of raw matters, in special soybean (^{Zorzoli 2018}), had motivated the production of certain crops. In Argentina, Uruguay and the southeastern of Brazil, had been recorded the highest climate changes worldwide in the last decades of 20th century. Especially, in the pampas area increased the spring- summer rainfalls, the minimum temperatures increased and the maximum decreased (^{Magrín et al. 2005} and Camillioni and ^{Barros 2020}).

The Azul municipality is located in Buenos Aires province, Argentina, to 300 km of the southwest of Buenos Aires city. Approximately, 50% of this municipality, center and north area has livestock talent, while the south area has better conditions for agriculture. In agreement with ^{Requesens (2011)}, livestock had been moving to lands with higher edaphic restrictions and the agriculture has advancing in livestock places, with an important role of soybean among summer crops. Particularly, the corn (Zea mays) is one of the summer crops most important in Azul municipality. It is a crop that could play an important function from the environmental point of view, due to its contribution to atmospheric carbon capture and to the recovery of the soil organic matter, in accordance with its high productivity and stubble volume (^{Bocchio et al. 2019}).

When consider the climate changes in the pampas area during spring and summer (^{Magrín et al. 2005}), in addition of above mentioned, it is of interest to evaluate a summer crop as a corn with respect to the climate effects. This evaluation can provide valuable information that contributes to increase yields and mitigate the consequences of climate variability, when develop adaptation strategies to climatic changes (^{Zhou et al. 2017}, ^{Meira et al. 2019} and ^{Bassu et al. 2021}).

Materials and Methods

In order to determine the influence of agroclimatic variables on the increase of corn yield in the last decades in Azul municipality, the agroclimatic requirements of this crop with respect to the agroclimatic variables of Azul municipality were analyzed.

The daily agrometeorological data with their standard deviation were obtained, which include maximum and minimum temperatures, total rainfalls and global radiation of the center area from Azul municipality, recorded by Centro Regional de Agrometeorología de la Facultad de Agronomía (CRAGM) de la Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA). A climatic series of 30 years (1988-2018) was obtained. A total of three decades were considered because with relation to agrometeorological data in climatology, 30 years is the minimum data set that allow defining the climate of a place. From them, the monthly average values were analyzed, from November to March, because they are the most important month in the corn growth. The 2009-2018 decade and the date set of the two previous decades (the last 10 years with the previous 20) were compared, since a decade is a recommended period to study the climate variability in this type of study.

Later, the corn yield was obtained, according to the data of Estimaciones Agrícolas del ^{Sistema Integral de Información Administrativa} del Ministerio de Agricultura (MinAgri), named ‘MinAgri yield", for the period 1988-2018.

The MinAgri yield series at first was fitted to different linear and non linear models using the year as independent variable to separate the technology effect. Different authors suggest that when there is a positive tendency in the data (after linear and non linear fit), is associate to technology progress (new hybrids, improvements in fertilization levels, sowing systems, pests and diseases control, among others). In this way the relation with agroclimatic variables were analyzed. In addition, as do not superseded the non linear models of the linear model performance, it was chose this fit through the program Curve Expert v. 1.40 (^{Hyams 2009}) to obtain the term used in equation 1, proved in ^{Miranda del Fresno et al. (2017)}, which allow the series correction. "The corrected MinAgri" was obtained:

${Rc}_i=R_i-\left[R\left(x_i\right)-R\left(x_0\right)\right]$ (1)

Rc_i = corrected yield in the year_i

R_i = original yield of the year_i

R(x_i) = yield of the estimated year (i) by linear or non linear fit

R(x₀) = yield of the initial year (0) estimated by linear or non linear fit

Later, two yield series were simulated with the program Decision Support System for Agrotechnology Transfer (DSSAT) v.4.6, which include 16 crops models of economic importance and allow simulating their growth, when provide true estimates of the crops performance, according to different management strategies and environmental conditions (^{Hoogemboom et al. 2019}).

This simulation was use to eliminate the effects of different technologies, sowing dates and soils, in order to determine the "potential yield"(without nutritional deficiency and irrigation) and the "dry potential yield" (without nutritional deficiency with the water contribution that comes from the rainfalls).

The Weather Man tool from the DSSAT program was used, adding the Ceres-corn model (^{Hoogemboom et al. 2019}):

• a) climate: maximum and minimum temperature, rainfalls and global radiation, with daily data from ^{CRAGM-UNCPBA (1988-2018)}

• b) soil: chemical and physical properties of each horizon of the profile, with actual profiles (^{Pazos 2009})

• c) management: waste, sowing dates (November 1st), fertilization, irrigation.

• d) genetic coefficient of cultivars: species, ecotype and cultivar, obtained from the calibration performed by ^{Confalone et al. (2016)} for Azul municipality.

Finally, the five months of agrometereological data were averaged and related with the corrected MinAgri yield, estimating the average and the variation coefficient (VC = standard deviation/ average for the last decade and the previous years and the correlation between each climatic variable and the yield. The corrected yield series was contracted with the variability of the agroclimatic elements.

Results and Discussion

The corn is the third crop most important of Azul municipality, after the soybean and wheat, taking into account the sown surface. Between 1988/89 and 2007/08 were sown as average 23 745 ha, and 31 159 ha between 2008/09 and 20017/18 respectively.

In relation to the agroclimatic requirements, as being a C4 species, the corn is demanding regarding temperature. This is the main controller factor of growing and development of this crop, whenever there was an adequate humidity (^{Fassio et al. 2018}). The mean minimum temperature of the soil for germination should be of 8-9 °C, with 23 °C as optimum and maximum 30 °C and sufficient humidity to germination takes place. The different phenological stages have different heat requirements, but generally are accepted that the basic temperature be 8 °C, the optimum from 30 to 34 °C, and the maximum from 40 to 44 °C (^{Andrade et al. 1996}).

In Azul municipality, corn may growth between November and March, when global radiation is of wide availability. From halfway through October to halfway through March, receive 22 MJ/m2d or more, low limit for high cereal productions (^{Navarro Duymovich et al. 2011}). The annual mean temperature is 13.61 °C. The maximum varies from 28 to 29 °C in January, and from 12 to 13 °C in June. The minimum range between 13° and 15 °C in January, and between 0 to 2 °C in June without reach as average less than 9 °C between November and March , which gave acceptable conditions for the growing and development of corn in the municipality.

Regarding rainfalls, from the sowing to maturity, the corn needs of 500 to 800 mm, with daily requirement of 5 mm, that could varies during the phenological stages. The sensitivity considering the lack of water increases from the differentiation of the male flower in the meristematic tissue, and have a maximum in the flowering, which is the critical period, in the maturity stage the required water decrease (^{Llano and Vargas 2015}). For Azul region, the pattern of distribution of rainfalls is more or less constant in the year, with an average of 802 mm annual. Rainfalls are more abundant in summer months. In October - March period exceeded the 400 mm, this can be a limiting element in the crop development in the region.

The corn crop is very sensitive to frosts. Even, the later frosts can caused the plants loses and the senescence of emerged leaves. The earliest, at the end of the cycle, can shorten the grain filling (^{Martínez Álvarez 2015}). In Azul region, the average date of the first frost is on May 30, with a free period of 202 days (^{Navarro Duymovich et al. 2011}).It means that, if corn is sowing at the beginning of November, the later frosts dates had been passed and for the earliest the crop had been harvested.

Table 1 shows the monthly average of the four considered agroclimatic variables and their standard deviation for November to March period, among 1988-2008 and 2009-2018 decades.

As shown in the table, global radiation, as the climate main element, increase 4.12 % as average. December and January have high difference: 1.87 and 1.34 MJ/m2d, respectively.

Table 1 Agroclimatic variables, monthly average and standard deviation (November to March) for 1998-2008 and 2009-2018 decades 

	Months	November	December	January	February	March
1988-2008	Maximum temperature, °C	23.57	27.08	28.74	27.29	25.16
	Standard deviation	2.14	1.60	1.37	1.43	1.33
	Minimum temperature, °C	9.71	12.51	14.10	14.03	12.29
	Standard deviation	1.32	1.23	0.94	1.46	1.50
	Rainfalls , mm	74.70	96.44	109.22	90.18	111.98
	Standard deviation	53.59	56.87	62.26	62.59	70.26
	Global radiation, MJ/m2.d	24.23	25.59	25.82	23.41	17.91
	Standard deviation	2.14	2.31	1.56	2.36	2.08
2009-2018	Maximum temperature, °C	24.26	28.81	29.82	27.49	24.88
	Standard deviation	1.54	1.52	1.30	1.60	1.32
	Minimum temperature, °C	10.69	13.53	14.98	14.73	11.79
	Standard deviation	1.86	0.94	0.38	1.36	1.60
	Rainfalls, mm	72.25	39.42	72.44	80.38	72.36
	Standard deviation	45.56	49.73	68.44	61.16	37.03
	Global radiation , MJ/m2d	24.89	27.46	27.16	23.38	18.89
	Standard deviation	1.27	1.78	1.77	2.32	2.64

The maximum temperature increase 2.59 % as average. It was positive from November to February with high difference in December (1.73 °C) and negative in March (-0.28 °C). Minimum temperature increase 4.89 %, as same as maxium temperature which was positive from November to February, with higher difference in November and December (1 °C in both months).It was negative in March (-0.50 °C).

Regarding the rainfalls, they decreased 30.19 % as average, with negative differences in all months, but more marked in January (-36.78 mm) and March (-36.62 mm).

Table 2 show the average and variation coefficient of the four yield series.

The outlets of the Ceres- corn potential model and dry potential model allow verifying the yields differences, whose values exceeded those of MinAgri and corrected MinAgri (table2), as being a synthetic series without the technology interferences that are in MinAgri series. The difference between the dry potential yield and the MinAgri series is 6569.30 kg/ha for all the period. It means, in Azul municipality, the true yield is 56.63 % less than the reached limit. In the last decade, due to the technology progress and the decrease of the dry potential yields, the difference was of 33.42 % (2913.50 kg/ha).

Table 2 Corn yield of the four series for 1998-2008 and 2009-2018 decades 

Yield, kg/ha
Period		MinAgri	Corrected MinAgri	Ceres-Corn model potential	Ceres-Maíz model dry potential
Average 1988-2018		5032.10	3523.67	16673.73	11601.40
Variation coefficient, %		27.25	34.51	10.97	37.67
1988/89 to 2007/08	Average	4645.85	3657.57	16336.15	13043.05
	Variation coefficient , %	22.98	23.47	10.93	29.19
2008/09 to2017/18	Average	5804.60	3255.87	1815.15	8718.10
	Variation coefficient, %	28.08	53.98	10.46	47.43

As figure 1 show, the technology progress (MinAgri series) increases the yield 25 % in the last decade. In turn, in the corrected MinAgri series, in the last decade, the yield decrease 11 % and the variability increase from 23.47 % to 53.98 %. When comparing the two series, there was a general difference of 30 %, 43.91 % in the last decade and 21.27 % in the previous two, which showed that the linear fits between the two are expanded when years passes. The original data of the yield series of corn turned off the correction with the fit:

$y=3367.9+100.1x$

(r₂ = 0.80; standard error of the model = 870.5)

Figure 1 Yield of corn MinAgri and corrected MinAgri and tendency lines between 1998 and 2018 

The outlet of the Ceres-corn model (table 2 and figure 2) show that the potential yield increase 6.20 % in the last decade. However, under dry potential conditions, decrease 33.16 % and the variability increase. It also increases the differences between both series (49.75 % in the last decade).

Figure 2 Yield of the corn (Ceres-corn) potential and dry and tendency lines of the compared series 1998 and 2018 

According with the results of table 1, rainfalls decrease in the last decade. While, global radiation and temperatures increase, which make possible beneficial conditions for a C4 tropical grass, like corn. There is an increase of potential yield (6.20 %) by the radiation increase, because the amount of fixed grains is positively associated with the incident energy (^{Otegui and López Pereira 2003}).

In accordance with these results, ^{Zhou et al. (2017)} showed that the variations of temperature and radiation were the main factors that influence on the grain weight of the corn and on its filled indicators. So, it is concluded that the variation in these climatic variables greatly affected the growth rate, the time of the grain filled, its weight and corn yield. The increase in the yield also depends on the temperature. That is why it is beneficial the temperature increase when there is not water stress, according to the phenological stage (^{Confalone et al. 2017}).

In the last decade, the dry potential cultivation differ in 49.75 % of potential yields, because of rainfalls decrease. While, in the previous decades the water effect only explained 20.16 %. If the data of corrected MinAgri yields and dry potential have wide differences under dry conditions, it was showed in both series decrease of the last decade values. Test show that rainfalls explain the high incidence of this environmental factor on yield, under dry conditions. This is because when the water in not a limitation, have low incidence on the results. (^{Fassio et al. 2018}).

The inclusion of corn in an agronomic project that includes strategies adapted to each environment could has more available water in the water balance of the soil, which allow higher production of grains, biomass and carbon, and favors the systems sustainability (^{Bocchio et al. 2019}).

As table 3 show, the average of MinAgri yield decreased and the variability increased. While, the agroclimatic data refers to energy, global radiation, maximum temperature and minimum temperature, increased in the last decade. The rainfalls, decreased in its cumulative values with respect to 1988-2008 period. Related to variability, there was increase of the maximum temperature in the last decade, and decrease of other variables. In study by ^{Rizzo et al. (2022)}, it was found that 48 % of the yield gain was associated with a decennial climatic tendency: 39 % with agronomic improvements and only 13 % with improvements in the genetic yield potential. It means, the future production gains will depend more on the yield gains of the improvements agronomic experiences.

Table 3 MinAgri yields and agroclimatic variables for 1998-2008 and 2009-2018 decades 

Yield, kg/ha				Agroclimatic variables
Periods		Corrected MinAgri	Maximum temperature, °C	Minimum temperature, C	Rainfalls, m	Global radiation, MJ/m^2d
1988/89 to 2007/08	Average	3657.57	26.38	12.53	482.52	23.39
	Variation coefficient, %	23.47	2.72	6.06	33.09	5.19
2008/09 to 2017/18	Average	3255.87	27.07	13.14	336.84	24.37
	Variation coefficient, %	53.98	3.06	5.49	28.57	3.16

The comparison of the correlations between each climate element (climatic variables) and the yield of MinAgri series show that there are significant (P < 0.001), only for the cumulative rainfall from November to March (Pearson coefficient = 0.249).

The performed analysis between the corn yield and the agroclimatic variables showed the relations between climate and corn yield, whose study allows understanding those relations and take decisions in consequence. In studies where the corn growth under future climatic conditions was formed, the relation with the climatic variables had been proved. In those studies it is concluded that looses only could partially compensate, by changes in the phenology and sowing dates (^{Bassu et al. 2021}). It has been state that to modify the sowing date has effect on yield (^{Zhou et al. 2017}, ^{Fassio et al. 2018} and ^{Zhai et al. 2022}).

Conclusions

It is concluded that in Azul municipality the climate tendencies in the last years exerts a negative effect on the corn yield, although technology hide it, since when the effects is get off (corrected MinAgri) the yield decrease.

The climate influence increase in the last years of the series. This can be observing from the rainfalls decrease of the last decade, when the yield values decrease in the corrected series. The fall of rainfalls in the spring - summer months is the most influence element in the yield, which shows the marked difference in the series under dry conditions and water contribution.

Acknowledgments

Thanks to Centro Regional de Agrometeorología de la Facultad de Agronomía (CRAGM) de la Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA) for providing the agroclimatic data to this study.

References

Andrade, F., Cirilo, A., Uhart, S. & Otegui, M. 1996. "Ecofisiología del cultivo de maíz". 1ra. ed. Argentina: Editorial la Barrosa.

Barros, V. & Camilloni, I. 2020. "La Argentina y el cambio climático: de la física a la política". Eudeba. 1ra. ed. Argentina: Eudeba. ISBN: 978-950-23-3097-6.

Bassu, S., Fumagalli, D., Toreti, A., Ceglar, A., Giunta, F., Motzo, R., Zajac, Z. & Niemeyer, S. 2021. "Modelling potential maize yield with climate and crop conditions around flowering". Field Crops Research, 271, 108226: 1-12, ISSN: 0378-4290. https://doi.org/10.1016/j.fcr.2021.108226.

Bert, F., de Estrada, M., Naumann, G., Negri, R., Podestá, G., de los Milagros Skansi, M., Spennemann, P. & Quesada, M. 2021. "The 2017-18 drought in the Argentine Pampas-Impacts on Agriculture". United Nations Office for Disaster Risk Reduction. GAR Special Report on Drought. <https://www.preventionweb.net/files/78456_cs1.14laplataargentinafinal20210215.pdf> [Consulted: February 28, 2022].

Bocchio, V., Requesens, E. & Mestelan, S. 2019. "Tendencias y equitatividad de los principales cultivos extensivos en el centro de la provincia de Buenos Aires". Ediciones INTA. RIA 45(2): 196-203, ISSN: 1669-2314. ISSN: 0325-8718.

Confalone, A., Vilatte, C. A., Aguas, L. M., Barufaldi, M. S., Eseiza, M. F. & Ponce, G. D. 2017. "Efecto biofísico del cambio climático en el rendimiento de cultivos de verano". Cuban Journal of Agricultural Science, 51(2): 249-259, ISSN: 2079-3480.

Confalone, A., Vilatte, C., Lázaro, L., Roca, N., Mestelan, S., Aguas, L., Navarro, M. & Sau, F. 2016. "Parametrización del modelo CROPGRO-soybean y su uso como herramienta para evaluar el impacto del cambio climático sobre el cultivo desoja". Revista de la Facultad de Ciencias Agrarias, 48(1): 49-64. ISSN: 1853-8665.

CRAGM. "Boletín Agrometeorológico del Centro-Sur de la Prov. de Bs. As. 1988-2018". Facultad de Agronomía. Azul. Bs. As.

Fassio, A., Ibañez, W., Fernández, E., Cozzolino, D., Pérez, O., Restaino, E., Pascal A., Rabaza C. & Vergara, G. 2018. "El cultivo de maíz para la producción de forraje y grano y la influencia del agua". Serie técnica, (239), ISBN: 978-9974-38-390-6. [Consulted: July 01, 2022].

Hyams, D. G. 2009. "Curve Expert 1.3: A comprehensive curve fitting system for Windows". <http://s91928265.onlinehome.us/curveexpert> [Consulted: October 01, 2019].

Hoogenboom, G., Porter, C.H., Boote, K.J., Shelia, V., Wilkens, P.W., Singh, U., White, J.W., Asseng, S., Lizaso, J.I., Moreno, L.P., Pavan, W., Ogoshi, R., Hunt, L.A., Tsuji, G.Y. & Jones, J.W. 2019. "El ecosistema de modelado de cultivos DSSAT". En: Boote, K.J. (ed.) Advances in Crop Modeling for a Sustainable Agriculture. Cambridge, Reino Unido: Burleigh Dodds Science Publishing. pp. 173-216. http://dx.doi.org/10.19103/AS.2019.0061.10. [Consulted: July 01, 2022].

Llano, P. & Vargas, W. 2015. "Climate characteristics and their relationship with soybean and maize yields in Argentina, Brazil and the United States". International Journal of Climatology, 36(3): 1471-1483, ISSN: 1097-0088. https://doi.org/10.1002/joc.4439.

Magrín, G., Travasso, M., López, G., Rodríguez, G. & Lloveras, A. 2005. "Vulnerabilidad de la producción agrícola en la región pampeana argentina". Informe Final. 2da Comunicación Nacional sobre el Cambio Climático. Componente B3. Available: <http://www.ecopuerto.com/bicentenario/informes/VulnerabilidadProdAgr.pdf> [Consulted March 01, 2022].

Martínez Álvarez, D. 2015. "Ecofisiología del cultivo de maíz. En: Garay, J. A. y Colazo, J. C. (eds.). El cultivo de maíz en San Luis. Argentina. INTA Ediciones Información Técnica. pp. 7-31. <https://inta.gob.ar/sites/default/files/script-tmp-inta_-_maizensanluis.pdf> [Consulted: February 28, 2022].

Meira S., Rodríguez Baide, J. M., Confalone A., Fatecha Fois, D.A., Fernandes J. M., Pérez González O. & Van den Berg, M. 2019. "Modelación del cultivo de soja en Latinoamérica: Estado del arte y base de datos para parametrización", EUR29057 ES, Publications Office of the European Union, Luxembourg. ISBN: 978-92-79-77709-7. ISSN: 1831-9424. https://doi.org/10.2760/639226. [Consulted February 28. 2022].

Miranda del Fresno, M. C., Aguas, L., Bongiorno. F., Vilatte, C. & Confalone, A. 2017. "Cambios en el clima y rendimiento de maíz en Azul, provincia de Buenos Aires. Repositorio Digital CICPBA", IV Congreso Anual Científico y Tecnológico, CICPBA. Available: <https://digital.cic.gba.gob.ar/handle/11746/6715> [Consulted March 01, 2022].

Navarro Duymovich, M., Confalone, A., Vilatte, C. & Aguas, L. 2011. "Características agroclimáticas para la sustentabilidad agropecuaria en el Partido de Azul". In: Requesens, E. (coord.). Bases agroambientales para un desarrollo sustentable del partido de Azul. Argentina: Docuprint S.A. pp. 29-60. ISBN: 978-950-658-269-2. <http://www.faa.unicen.edu.ar/archivos/Bases_Agroambientales.pdf> [Consulted February 28, 2022].

Otegui, M. E. & López Pereira, M. 2003. "Fecha de siembra". In: Satorre, E., Benech, R., Slafer, G., De La Fuente, E., Miralles, D., Otegui, M. & Savin, R. (eds.). Producción de cultivos de granos: bases funcionales para su manejo. Argentina: Editorial Facultad de Agronomía. pp. 260-272.

Pazos, M. S. 2009. "Los Phaeozem pampeanos en la WRB 2006". Geociències. Asociación de ciencias geológicas y del medio natural, (16): 19-22, ISSN: 1699-9053.

Requesens, E. 2011. "Integración agroecológica del partido de Azul. En: Requesens, E. (coord.). Bases agroambientales para un desarrollo sustentable del partido de Azul. Argentina": Docuprint S.A. pp. 117-136. ISBN: 978-950-658-269-2. <http://www.faa.unicen.edu.ar/archivos/Bases_Agroambientales.pdf> [Consulted February 28 2022].

Rizzo, G., Monzon, J. P., Tenorio, F. A., Howard, R., Cassman, K. G., & Grassini, P. 2022. "Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments". Proceedings of the National Academy of Sciences, 119(4): 1-6. ISSN: 1091-6490. https://doi.org/10.1073/pnas.2113629119. [Consulted July 01, 2022].

Sistema Integral de Información Administrativa (SIIA). "Estimaciones Agrícolas del Ministerio de Agricultura, Ganadería y Pesca". Disponible <http://www.siia.gob.ar/sst_pcias/estima/estima.php> verificado 15/jul/2018> [Consulted February 28, 2022].

Zhai, L., Zhang, L., Yao, H., Zheng, M., Ming, B., Xie, R., Zhang, J., Jia, X.& Ji, J. 2022. "The Optimal Cultivar × Sowing Date × Plant Density for Grain Yield and Resource. Use Efficiency of Summer Maize in the Northern Huang-Huai-Hai Plain of China". Agriculture, 12(1): 7. https://doi.org/10.3390/ agriculture12010007.

Zhou, B., Yue, Y., Sun, X., Ding, Z., Ma, W. & Zhao, M. 2017. "Maize kernel weight responses to sowing date-associated variation in weather conditions", The Crop Journal, 5(1): 43-51, ISSN: 2214-5141. https://doi.org/10.1016/j.cj.2016.07.002.

Zorzoli, F. 2018. "¿Límites ecológicos y fronteras tecnológicas en el negocio agrícola? Agricultura y ambiente en los sectores agrarios medios del noroeste argentino". Población & Sociedad, 25(1): 163-195, ISSN: 1852-8562. https://doi.org/10.19137/pys-2018-250106.

Received: April 27, 2022; Accepted: July 30, 2022

^* Email:[e-mail],[e-mail]

The authors declare that there are no conflicts of interest among them.

Author’s contribution: María C. Miranda del Fresno: Original idea, analysis of data and writing. Adriana Confalone: Original idea, conducting of the investigation, analysis of data and review.