Different water availability in the economic water productivity in soybean cultivars¹

ABSTRACT

The present work aims to evaluate grain productivity, water productivity, and economic water productivity of three soybean cultivars under supplementary irrigation. Two experiments were conducted during the 2018 and 2019 harvests in Santa Maria/RS, Brazil. The experimental design consisted of a random bifactorial block design with six irrigation depths as the first factor and three soybean cultivars (Glycine max L.) as the second. The irrigation system used was the conventional fixed sprinkler, with a fixed irrigation shift of seven days. Crop productivity, water productivity, and economic water productivity were evaluated. The highest productivity was for 100% of reference evapotranspiration (ETo) in both harvests. Maximum technical efficiency was obtained for depths of 73.03% (Harvest 1) and 77.94% (Harvest 2) of ETo. Both harvests presented higher water productivity and economic water productivity in the 50% and 25% ETo depths respectively. Productivity is increased with irrigation, and the economic water productivity is maximized with reduction of depth.

Keywords irrigation; Glycine max L; water use efficiency; maximum technical efficiency

INTRODUCTION

Soybean is the main crop in Brazil’s production volume, reaching 124.8 million tons in the 2019/20 harvest with a planted area of 36.9 million hectares (CONAB, 2020). This crop has an important role in the production chain due to its many forms of use, including animal feed, oil, bran, and biodiesel.

Water deficit is the main source of the soybean productivity gap, becoming a significant concern for increasing Brazilian production in current and future climatic conditions (Battisti & Sentelhas, 2017). One of the leading causes for the oscillations in the pluviometric regime is the ENSO phenomenon (El Niño Southern Oscillation), which causes severe problems for Brazilian agriculture, such as floods and droughts, depending on its phase (El Niño or La Niña) (Nóia Júnior & Sentelhas, 2019).

When this pluviometric regime does not meet the crop’s total demands, both quantitatively and temporally, it is necessary to use water supplementation as an alternative to seeking greater productivity (Gajić et al., 2018). A major challenge still facing soybean producers is how much and when to irrigate. Therefore, the relationship between crop productivity and irrigation water applied in conjunction with

knowledge of the region’s pluviometric demands and crop deficits can efficiently answer these questions (Zhang et al., 2018). According to Battisti et al. (2018), irrigation increases soybean productivity in different climatic scenarios.

However, many factors define the development, growth, and productive potential of the crop, being influenced by genetic (the type of growth, relative maturity group, and presence of the juvenile gene) and climatic factors (photoperiod, solar radiation, temperature, and water availability) and management (sowing time and soil physicochemical characteristics) (Pires et al., 2005; Zanon et al., 2016).

According to Ribeiro et al. (2017), crop productivity can vary widely depending on the cultivar chosen and the region of study. These authors also state that there is no difference in the soybean yield components for the sowing densities of 300 to 600 thousand plants per hectare. With a lack of answers when comparing soybean cultivars according to water availability, it is important that restrictive factors, such as irrigation, field management, soil, and climate conditions, be considered in addition to selecting the best cultivars in each year of cultivation (Araji et al., 2018).

Montoya et al. (2017) report that supplementary irrigation in soybean crops provided an increase in grain productivity, maximizing yield and profit margin. Adeboye et al. (2015) found that irrigation with total water replacement showed a better response when evaluating the economic productivity of water in soybean crops submitted to water deficits at different development stages. Additionally, Tewelde (2019) reports the importance of obtaining economic water productivity to deduct the farmers’ gains concerning water consumption. Thus, evaluating irrigation management and its increases in crop yield shows the importance of water productivity in the management of irrigated agriculture (Kirchner et al., 2019).

Management alternatives aimed at higher yields, with correct management of water resources are essential for the soybean production chain. Therefore, the efficiency of water application per crop area makes production sustainable, economical and consequently more profitable.

Given the above, the present work aims to evaluate grain productivity, water productivity, and economic water productivity of three soybean cultivars under supplementary irrigation.

MATERIAL AND METHODS

The experiment was conducted during the 2018 (Harvest 1) and 2019 (Harvest 2) harvests in an experimental area belonging to the Colégio Politécnico da UFSM, located in Santa Maria-RS, Brazil. The experimental area coordinates are 29°42’55.7”S 53°44’21.4”W, and an altitude of 120 m. According to the Köppen-Geiger classification, the region’s climate is type Cfa (humid subtropical climate), with well-defined seasons (Alvares et al., 2013).

According to INMET, the average annual precipitation in the region ranges from 1450 to 1650 mm with an average temperature of 18-20 °C. In this region, the distribution of rainfall during the summer is usually irregular and may not be sufficient to meet the water needs in certain periods of the crop cycle (Nied et al., 2005). The soil of the experimental area is classified as ‘Argissolo Vermelho Distrófico Típico’ (Santos et al., 2018).

Chemical and physical soil analyzes were performed in the area. The collection of soil samples for chemical soil analysis was conducted according to Arruda et al. (2014). The samples were analyzed at the Soil Analysis Laboratory of the Universidade Federal de Santa Maria (UFSM), where the macro and micronutrient soil requirements were determined.

Soil chemical analysis showed the following results: potential of hydrogen (pH) of 5.6, 8.1 cmol_c dm^-3 of calcium (Ca), 3.3 cmol_c dm^-3 of magnesium (Mg), 0.0 cmol_c dm^-3 of aluminium (Al), effective cation exchange capacity (CEC) of 11.7 cmol_c dm^-3, CEC at pH7 of 15.2 cmol_c dm^-3, base saturation of 77%, soil matric potential (SMP) index of 6.2, 2.3% of organic matter, 28% of clay, 9.7 mg dm^-3 of phosphorus (P) (Mehlich) and 96 mg dm^-3 of potassium (K) (Mehlich).

Fertilization was performed after chemical analysis in the quantities recommended by the Comissão de Química e Fertilidade do Solo do RS/SC (2016). The physical soil analyzes were performed at the Soil Analysis Laboratory of UFSM (Table 1).

Table 1
Hydro-physical characteristics of the soil in the experimental area

Soil Profundity (m)	Soil Density (g cm-3)	Soil Field Capacity (m3 m-3)	Permanent Wilt Point (m3 m-3)	Basic Infiltration Speed (mm h-1)	Soil Texture
0-0.2	1.42	0.31	0.14		Loam
0.2-0.4	1.38	0.34	0.17	15	Clay loam
0.4-0.6	1.36	0.37	0.23		Clay

The experiment site’s meteorological data were obtained through the National Institute of Meteorology’s automatic meteorological station, located at UFSM, situated approximately 2 km of the area. The data collected daily were maximum and minimum temperatures (ºC), relative humidity (%), wind speed (m s^-1), and solar radiation (kJ m^-2). Already the precipitation (mm) was collected in the experimental area using rain gauges.

Sowing for Harvests 1 and 2 was done on 12/14/2017 and 11/23/2018. The experimental design consisted of a random bifactorial block design, with four blocks, being six irrigation depths (L factor) and three soybean cultivars (Glycine max L., C factor), totaling 72 experimental units (UE). Each UE has dimensions of 4 x 4 m (16 m²), this area was considered a useful area of 12.25 m². Between each UE there was a space of 4 meters, so that in the application of irrigation there was no overlapping of depths.

Thirty days before sowing, the herbicide glyphosate was applied at a dose of 3 L ha^-1. The fertilization was carried out at sowing, applying 380 kg ha^-1 in the commercial formulation 5-20-20, of nitrogen (N), phosphorus (P₂O₅) and potassium (K₂O).

Two fungicide applications were carried out (0.5 L ha^-1), in a preventive manner, active ingredients bixafen (125 g L^-1), trifloxystrobin (150 g L^-1) and prothioconazole (175 g L^-1). Two applications of insecticide (0.75 L ha^-1), imidacloprid (100 g L^-1) and beta-cyfluthrin (12.5 g L^-1) were also carried out. The L factor was 0%, 25%, 50%, 75%, 100%, and 125% of the reference evapotranspiration and the C factor the cultivars NS 6909 PRO RR, BRASMAX Ponta IPRO 7166 RSF, and BRASMAX Valente RR 6968 RSF. The three cultivars have an indeterminate growth habit and medium cycle.

A fixed conventional sprinkler irrigation system was used for the irrigation management, consisting of the mainline of 92 meters and 24 lateral lines of 24 meters. The spacing between the lateral lines was 4 m. The sprinklers Agrojet, P5 model, were distributed on the lateral lines with a 4 m spacing and installed on an elevation of 1.5 m in height (Figure 1).

Figure 1
Sketch of the experimental area.

Christiansen’s Uniformity Coefficient test (CUC) was used to verify the irrigation uniformity and calibrate the system’s irrigation rate (mm h^-1). The irrigation uniformity was 82%, and the system’s application rate was 11.5 mm h^-1.

Irrigation was conducted with a fixed shift of seven days between irrigations when there was no precipitation to supply the crop’s water demand of the crop in the period and was started soon after its emergence. Irrigation management was based on reference evapotranspiration (ETo), calculated using the Penman-Monteith-FAO equation (Allen et al., 1998).

The need for irrigation was determined according to Equation 1:

$$NI=ETo-P_{ef}$$ (1)

where NI – is the irrigation requirements (mm), ETo – is the reference evapotranspiration for seven days (mm), and P_ef – is the effective precipitation (mm).

According to Millar (1978), the effective precipitation was determined, which considers the parameters of the textural class of the soil, declivity (%), and vegetation cover. The fraction of precipitation lost by runoff considered was 30% of the total precipitate for the place where the work was conducted.

The irrigation depths were applied for the irrigation times, according to Equation 2:

$$IT=\frac{Ln}{Lr\cdot Ua}\cdot 100$$(2) --

$$WP=\frac{Y}{W}$$ (3)

where WP – is the water productivity (kg ha^-1 mm^-1), Y – is the crop productivity (kg ha^-1), and W – is the total water depth applied during the crop cycle (mm).

Furthermore, the economic productivity of the water was determined through Equation 4.

$$EWP=\frac{p\cdot Y}{W}$$ (4)

where EWP – is the economic water productivity (US$ ha^-1 mm^-1), and p – is the average grain price (US$ kg^-1), Y – is the crop productivity (kg ha^-1), and W – is the total water depth applied during the crop cycle (mm).

Soybean commercialization price was determined using the averages for the state of Rio Grande do Sul in April of 2018 and 2019, following the harvesting, with values of R$ 74.18 and R$ 68.18 per bag, respectively. Prices were converted into dollars and during this period the average quotation was R$ 3.64.

The results were subjected to analysis of variance (ANOVA) at the 5% error probability level using the Sisvar program 5.6. Regression analysis and maximum technical efficiency were performed when there was an interaction between the cultivar factors and irrigation depths. When there was no interaction, the means were compared by the Tukey test for qualitative data (soybean cultivars) and regression analysis and maximum technical efficiency for quantitative data (irrigation depths). The regression analysis was performed using the SigmaPlot 11.0 software.

RESULTS AND DISCUSSION

Figure 2 shows the average maximum and minimum temperatures, effective precipitation, and daily evapotranspiration for Harvests 1 and 2. The average daily air temperature fluctuated between 15 ºC and 32 °C for the studied harvests. There were no significant differences for both the maximum average temperature and the minimum average temperature, considering that the appropriate thermal conditions for the growth and development of soybeans are between 20 and 30 °C (Battisti & Sentelhas, 2014).

The effective precipitation showed approximate values for both harvest years, with 369.18 mm and 374.55 mm for Harvests 1 and 2, respectively. These values were insufficient to supply the crop requirements, demanding an irrigation input to ensure production. According to Grassini et al. (2015), soybean crops require 450 to 700 mm of water to supply their water needs. For the southern region of Brazil, studies indicate that a water supply of approximately 800 mm (Zanon et al., 2016) and between 765 and 875 mm (Tagliapietra et al., 2021) are enough to maximize soybean productivity. The evapotranspiration values during the entire crop cycle in Harvests 1 and 2 were 336.60 and 315.76 mm, respectively. Bariviera et al. (2020) obtained evapotranspiration of 267.06 mm and precipitation of 922.28 mm with 62 precipitation events throughout the crop cycle when studying irrigated soybean crops in the 2015/16 harvest, in Mato Grosso state, which justifies the difference in evapotranspiration demand observed in the present study.

During the development of the crop, seven (Harvest 1) and six (Harvest 2) irrigations were required (Figure 3). The irrigations for each treatment of Harvest 1 totaled 30.28, 60.56, 90.84, 121.12, and 151.40 mm for depths of 25%, 50%, 75%, 100%, and 125% of ETo, respectively. The irrigation depths for each treatment of Harvest 2 were 30.17, 60.34, 90.51, 120.68, and 150.85 mm for 25%, 50%, 75%, 100%, and 125% of ETo, respectively.

Figure 2
Maximum and minimum temperature (°C), precipitation (mm), and evapotranspiration (mm) data for both analyzed harvests.

Figure 3
Precipitation (mm), evapotranspiration (mm), and irrigation depth (mm) accumulated for both crop cycles with an interval of seven days.

The analysis of variance showed no interaction between the depth and cultivar factors at the 5% level of significance for the soybean crop productivity. However, the cultivars showed a statistical difference for productivity, water productivity, and economic water productivity in both crops studied (Table 2).

Table 2
Crop productivity (kg ha^-1), water productivity (WP) (kg ha^-1 mm^-1), and economic water productivity (EWP) (US$ ha^-1 mm^-1) in the different soybean cultivars in Harvests 1 and 2

Cultivars	Productivity		WP		EWP
	Harvest 1	Harvest 2	Harvest 1	Harvest 2	Harvest 1	Harvest 2
**CV (%)	012.27	010.27	12.71 0	11.25	12.710 0	11.25 0
NS 6909	5,715.41b*	5,395.36b	12.92b	12.08b	4.21b0	3.77b
BMX Ponta	5,990.72ab	5,916.43a	13.59ab	13.24a	4.43ab	4.13a
BMX Valente	6,389.23a	6,123.94a	14.47a0	13.70a	4.72a0	4.28a

• ^*
  Mean followed by lowercase letters different in the vertical significantly differ at a 5% level of error of probability.
• ^**
  CV = coefficient of variation.

Cultivar BMX Valente presented the highest productivity, water productivity, and economic water productivity values in both harvest years, with no significant difference cultivar BMX Ponta, while cultivar NS 6909 showed the lowest results. Santos et al. (2019) found that the cultivars showed a significant difference at the level of 1% error probability for grain production when evaluating the productivity and water productivity of different soybean cultivars, corroborating the results of the present study.

The three cultivars studied responded equally to irrigation, unlike in the study conducted by Gava et al. (2017), who found that some genotypes do not respond to irrigation depending on each cultivar’s genetic characteristics when evaluating irrigated and non-irrigated soybean cultivars.

According to Kukal & Irmak (2020), irrigation has become a fundamental agricultural production tool, reducing crops’ annual variability due to climatic variations and efficient water resource use. Soybean productivity in both harvests responded positively to the amount of water supplied, showing a very similar behavior in both situations studied (Figure 4). This is in agreement with a study conducted by Montoya et al. (2017) in Salto, Uruguay, where the authors found that the soybean crop development was similar in both years regarding the total crop cycle and accumulated thermal time.

Figure 4
Average productivity of the soybean crop in function of the irrigation depths of Harvests 1 and 2.

The maximum technical efficiency for the irrigation depths in Harvest 1 was reached at 73.03% of ETo, providing a productivity of 6,602.92 kg ha^-1. In Harvest 2, the maximum technical efficiency was achieved with 77.94% of ETo, which produced 6,260.36 kg ha^-1. The productivity of irrigated soybeans ranged from 4,978.45 kg ha^-1 to 6,661.96 kg ha^-1 in Harvest 1, and 4,903.75 kg ha^-1 to 6,324.44 kg ha^-1 in Harvest 2, with little difference between harvests.

The increase in soybean crop productivity with irrigation depths of 25%, 50%, 75%, 100%, and 125% of ETo compared to productivity without irrigation, presenting values of 20.13%, 30.09%, 28.22%, 33.82%, and 14.69% for Harvest 1 and 16.95%, 24.29%, 24.08%, 28.97%, and 16.82% for Harvest 2. Montoya et al. (2017) reported that supplementary irrigation in the soybean crop provided an increase in grain productivity, after two experimental harvests, with grain yield values up to 35% higher than the non-irrigated experiment. The author also reports that, during both seasons studied, the maximum grain yield values were reached at 75% of crop evapotranspiration (ETc), which is similar to the maximum technical efficiency found in the present study.

The production functions were adjusted to a second-degree polynomial model, with a coefficient of determination inferior to 0.91. The productivity averages with the lowest values were obtained in the control, with 4,978.45 kg ha^-1 (Harvest 1) and 4,903.75 kg ha^-1 (Harvest 2). The highest values of 6,661.96 kg ha^-1 and 6,324.44 kg ha^-1 of productivity were found with the depth of 100% ETo for Harvests 1 and 2, respectively. There was an increase of 33.82% and 28.97% between the control and the depth of 100% of ETo.

Gajić et al. (2018) observed an increase of 42% in the treatment that obtained the highest productivity than the non-irrigated treatment. Panday et al. (2018) observed an increase in productivity of 27% when comparing irrigated and non-irrigated treatments. Candoğan & Yazgan (2016) report that the highest grain yield was obtained in treatments with total irrigation, presenting an average gain of 50.6% compared to the precipitation treatment.

Gava et al. (2018) observed that supplementary irrigation contributes to higher productivity in intermediate cycle cultivars than in super early cycle cultivars. The three cultivars evaluated in this study are of the intermediate cycle and corroborate that irrigation contributed to the increase in productivity since crop yield increased from the 25% ETo depth.

Despite the increase in productivity with the depth of 100% in both harvests, water productivity showed the best values at depths of 50% and 25% with productivity of 15.07 and 14.17 kg ha^-1 mm^-1 for Harvest 1 and 2, respectively. Consequently, the highest economic water productivity was obtained on the same irrigation depths (Table 3).

These results are similar with those found by Candogan et al. (2013), who observed the highest water productivity values for 25% of ETc. However, the authors report that this irrigation strategy can cause a 27.5% reduction in grain yield, differing from this study where reduction in the productivity of was 2.78% (Harvest 1) and 9.32% (Harvest 2). This information can facilitate decision-making when choosing the type of irrigation to provide greater water availability for an increase in productivity or smaller depths when there is water scarcity in a reservoir or for water cost savings (Candogan et al., 2013; Çetin & Kara, 2019).

Table 3
Crop productivity (kg ha^-1), water productivity (WP) (kg ha^-1 mm^-1), and economic water productivity (EWP) (US$ ha^-1 mm^-1) in the different irrigation depths (%ETo)

Irrigation Depths	Harvest 1				Harvest 2
	Productivity	WP	EWP		Productivity	WP	EWP
0	4,978.45	13.49	4.58		4,737.08	12.65	4.09
25	5,980.44	14.97	5.08		5,734.93	14.17	4.42
50	6,476.67	15.07	5.12		6,094.98	14.02	4.38
75	6,383.53	13.88	4.71		5,984.61	12.87	4.08
100	6,661.96	13.59	4.61		6,074.44	12.27	3.99
125	5,709.65	10.97	3.72		5,745.42	10.94	3.40

Different results were found by Panday et al. (2018) when comparing soybean productivity in dryland (600 mm precipitation) and irrigated, finding higher average water productivity values for the treatment that received supplementary irrigation. Adeboye et al. (2015) also found a increase in water productivity in full irrigation treatment. In contrast, Gajić et al. (2018) obtained a water productivity value in the non-irrigated treatment 10% higher than in the treatment of 100% water replacement.

Montoya et al. (2017) found higher water productivity in treatments with less water availability and the lowest result for full water replacement based on the culture’s evapotranspiration, which corroborated the findings of this study. The irrigation depth of 125% of ETo presented the lowest average value of water productivity, reaffirming that water availability above the crop’s evapotranspiration demand reduces the system’s productive efficiency.

The economic water productivity values ranged between 3.72 and 5.12 US$ ha^-1 mm^-1 for Harvest 1 and 3.40 and 4.42 US$ ha^-1 mm^-1 for the Harvest 2. The lowest values were obtained for the depth of 125% of ETo and the highest values for the depth of 50% (Harvest 1) and 25% (Harvest 2) of ETo, corresponding to an increase of 27.34% and 23.08% in relation to the lowest values of economic water productivity.

A similar behavior was obtained by Uygan et al. (2021) reporting values higher than those found in this study, reaching an increase of up to 70% at the lowest irrigation depth. Sahoo et al. (2018), working with different irrigation methods obtained an economic water productivity was 20.9% higher in drip than furrow irrigation.

The average price of soybeans in Rio Grande do Sul for April 2018 (Harvest 1) was US$ 20.38 per bag and US$ 18.73 for Harvest 2 in April 2019. This economic gain increases proportionally to the price of soybean. Noellemeyer et al. (2013) found low economic water productivity values for soybean, which reflected a low grain yield that does not counterbalance a high market cost.

Unlike what was found in this study, Adeboye et al. (2015) observed higher economic water productivity in the treatment with full irrigation than treatments that underwent water deficit in different phenological stages. Ben et al. (2017) report that the use of the lowest amount of irrigation provided the highest economic water productivity for rice crops and that this calculation shows the irrigation condition that makes production economically more efficient, as the volume of water is low enough to allow economic production, corroborating the results found in this study.

Although the 100% ETo irrigation depth provides a higher increase in soybean productivity for both studied harvests, in similar years when the rainfall regime is not scarce, one can opt for a lower water supplementation to obtain greater irrigation water productivity cost.

CONCLUSION

Supplementary irrigation provided an increase in grain yield for both crops, presenting a maximum technical efficiency of 73.03% and 77.94% of ETo for the irrigation depths. Water productivity demonstrated that better efficiency in water resources could be obtained for lower values of irrigation, minimizing the amount of water used in this process. Economic water productivity could assist in the decision-making of how much to irrigate to reduce production costs and ensure an economic return, even when higher soybean productivity is not achieved.

• 1
  This work is part of the first author’s Doctoral Thesis.

ACKNOWLEDGEMENTS, FINANCIAL SUPPORT AND FULL DISCLOSURE

This paper was written with the support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) – Financing Code 001, through the granting of a scholarship. The authors declare that there is no conflict of interest for the publication of this article.

REFERENCES

• Adeboye OB, Schultz B, Adekalu KO & Prasad K (2015) Crop water productivity and economic evaluation of drip-irrigated soybeans (Glyxine max L. Merr.). Agriculture & Food Security, 4:01-13.
• Allen RG, Pereira LS, Raes D & Smith M (1998) Crop evapotranspiration - Guidelines for computing crop water requirements. Rome, FAO. 15p. (FAO irrigation and drainage paper, 56).
• Alvares CA, Stape JL, Sentelhas PC, De Moraes Gonçalves JL & Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22:711-728.
• Araji HA, Wayayok A, Bavani AM, Amiri E, Abdullah AF, Daneshian J & Teh CBS (2018) Impacts of climate change on soybean production under different treatments of field experiments considering the uncertainty of general circulation models. Agricultural Water Management, 205:63-71.
• Arruda MR, Moreira A & Pereira JCR (2014) Amostragem e coleta de solos para fins de fertilidade. Manaus, Embrapa Amazônia Ocidental. 18p. (Documento, 115).
• Battisti R & Sentelhas PC (2014) New agroclimatic approach for soybean sowing dates recommendation: A case study. Revista Brasileira de Engenharia Agrícola e Ambiental, 18:1149-1156.
• Battisti R & Sentelhas PC (2017) Improvement of soybean resilience to drought through deep root system in Brazil. Agronomy Journal, 109:1612-1622.
• Battisti R, Sentelhas PC, Parker PS, Nendel C, Camara GMS, Farias JRB & Basso CJ (2018) Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop and Pasture Science, 69:154-162.
• Bariviera G, Dallacort R, De Freitas PS, Barbieri JD & Daniel DF (2020) Dual crop coefficient for the early-cycle soybean cultivar SoyTech 815 RR. Revista Brasileira de Engenharia Agrícola e Ambiental, 24:75-81.
• Ben HM, Monaco F, Facchi A, Romani M, Valè G & Sali G (2017) Desempenho econômico de variedades tradicionais e modernas de arroz sob diferentes sistemas de manejo da água. Sustentabilidade, 9:347.
• Candoğan B & Yazgan S (2016) Yield and quality response of soybean to full and deficit irrigation at different growth stages under sub-humid climatic conditions. Journal of Agricultural Sciences, 22:129-144.
• Candogan BN, Sincik M, Buyukcangaz H, Demirtas C, Goksoy AT & Yazgan S (2013) Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions. Agricultural Water Management, 118:113-121.
• CQFS RS/SC - Comissão de Química e Fertilidade do Solo do RS/SC (2016) Manual de calagem e adubação para os Estados do Rio Grande do Sul e de Santa Catarina. 11ª ed. Porto Alegre, Sociedade Brasileira de Ciência do Solo/Núcleo Regional Sul. 376p.
• CONAB - Companhia Nacional de Abastecimento (2020) Acompanhamento da Safra Brasileira - Grãos. Available at: <https://www.conab.gov.br/infoagro/safras/graos>. Accessed on: October 13^th, 2021.
  » https://www.conab.gov.br/infoagro/safras/graos
• Çetin O & Kara A (2019) Assesment of water productivity using different drip irrigation systems for cotton. Agricultural Water Management, 223:105693.
• Gajić B, Kresović B, Tapanarova A, Životić L & Todorović M (2018) Effect of irrigation regime on yield, harvest index and water productivity of soybean grown under different precipitation conditions in a temperate environment. Agricultural Water Management, 210:224-231.
• Gava R, Lima SFD, Santos OFD, Anselmo JL, Cotrim MF & Kühn IE (2018) Water depths for different soybean cultivars in center pivot. Revista Brasileira de Engenharia Agrícola e Ambiental, 22:10-15.
• Gava R, Anselmo JL, Neale CM, Frizzone JA & Leal AJ (2017) Different soybean plant populations under central pivot irrigation. Engenharia Agrícola, 37:441-452.
• Grassini P, Torrion JA, Yang HS, Rees J, Andersen D, Cassman KG & Specht JE (2015) Soybean yield gaps and water productivity in the western U.S. Corn Belt. Field Crops Research, 179:150-163.
• Kirchner JH, Robaina AD, Peiter MX, Torres RR, Mezzomo W, Ben LHB, Pimenta BD & Pereira AC (2019) Funções de produção e eficiência no uso da água em sorgo forrageiro irrigado. Revista Brasileira de Ciências Agrárias, 14:01-09.
• Kukal MS & Irmak S (2020) Impact of irrigation on interannual variability in United States agricultural productivity. Agricultural Water Management, 234:01-10.
• Millar AA (1978) Drenagem de terras Agrícolas: bases agronômicas. São Paulo, McGrawHill do Brasil. 276p.
• Montoya F, García C, Pintos F & Otero A (2017) Effects of irrigation regime on the growth and yield of irrigated soybean in temperate humid climatic conditions. Agricultural Water Management, 193:30-45.
• Nied AH, Heldwein AB, Estefanel V, Silva JC & Alberto CM (2005) Épocas de semeadura do milho com menor risco de ocorrência de deficiência hídrica no município de Santa Maria, RS, Brasil. Ciência Rural, 35:995-1002.
• Noellemeyer E, Fernández R & Quiroga A (2013) Crop and tillage effects on water productivity of dryland agriculture in Argentina. Agriculture, 3:01-11.
• Nóia Júnior R de S & Sentelhas PC (2019) Soybean-maize off-season double crop system in Brazil as affected by El Niño Southern Oscillation phases. Agricultural Systems, 173:254-267.
• Panday SC, Choudhary M, Singh S, Meena VS, Mahanta D, Yadav RP, Pattanayak A & Bisht JK (2018) Increasing farmer’s income and water use efficiency as affected by long-term fertilization under a rainfed and supplementary irrigation in a soybean-wheat cropping system of Indian mid-Himalaya. Field Crops Research, 219:214-221.
• Pires JLF, Costa JA, Rambo L & Ferreira FG (2005) Métodos para a estimativa do potencial de rendimento da soja durante a ontogenia. Pesquisa Agropecuária Brasileira, 40:337-344.
• Ribeiro ABM, Bruzi AT, Zuffo AM, Zambiazzi EV, Soares IO, Vilela NJD, Pereira JL de AR & Moreira SG (2017) Productive performance of soybean cultivars grown in different plant densities. Ciência Rural, 47:01-08.
• Sahoo P, Brar AS & Sharma S (2018) Effect of methods of irrigation and sulphur nutrition on seed yield, economic and bio-physical water productivity of two sunflower (Helianthus annuus L.) hybrids. Agricultural Water Management, 206:158-164.
• Santos HG dos, Jacomine PKT, Anjos LHC dos, Oliveira VA de, Lumbreras JF, Coelho MR, Almeida JA, De Araujo Filho JC, De Oliveira JB & Cunha TJF (2018) Sistema brasileiro de classificação de solos. 5ª ed. Brasília, Embrapa. 356p.
• Santos JWS dos, Barbosa WSS, Teodoro IPO, Silva JAC, Teodoro I & Lyra GB (2019) Desempenho produtivo da soja com irrigação suplementar nos tabuleiros costeiros de alagoas. Revista Brasileira de Agricultura Irrigada, 13:3714-3723.
• Tagliapietra EL, Zanon AJ, Streck NA, Balest DS, Rosa SL, Bexaira KP, Richter GL, Ribas GG & Silva MR (2021) Biophysical and management factors causing yield gap in soybean in the subtropics of Brazil. Agronomy Journal, 113:1882-1894.
• Tewelde AG (2019) Evaluating the Economic Water Productivity underfull and deficit irrigation; the case of sesamecrop (Sesumum indicum L.) in woreda Kafta-Humera, Tigrai-Ethiopia. Water Science, 33:75-83.
• Uygan D, Cetin O, Alveroglu V & Sofuoglu A (2021) Improvement of water saving and economic productivity based on quotation with sugar content of sugar beet using linear move sprinkler irrigation. Agricultural Water Management, 255:106989.
• Zanon AJ, Streck NA & Grassini P (2016) Climate and management factors influence soybean yield potential in a subtropical environment. Agronomy Journal, 108:1447-1454.
• Zhang B, Feng G, Ahuja LR, Kong X, Ouyang Y, Adeli A & Jenkins JN (2018) Soybean crop-water production functions in a humid region across years and soils determined with APEX model. Agricultural Water Management, 204:180-191.

Publication Dates

• Publication in this collection
  10 Mar 2023
• Date of issue
  Jan-Feb 2023

History

• Received
  25 Oct 2021
• Accepted
  21 May 2022