Soil Temperature Dependent Growth of Cotton Seedlings Before Emergence

doi:10.1016/S1002-0160(07)60102-7

Pedosphere

Volume 18, Issue 1, February 2008, Pages 54-59

https://doi.org/10.1016/S1002-0160(07)60102-7 Get rights and content

Abstract

Soil temperature is an important variable governing plant growth and development. Studies were conducted under laboratory conditions to determine the effect of soil temperature on root and shoot growth of cotton during emergence. Cotton seedlings were grown for 192 h at 20, 32 and 38 °C in soil packed in 300 mm long and 50 mm diameter cylinders. The data indicated that the longest roots (173 mm) as well as shoots (152 mm) were recorded at 32 °C followed by 20 (130 mm root and 82 mm shoot) and 38 °C (86 mm root and 50 mm shoot). Roots grown at 20 and 38 °C were 20% and 50% shorter, respectively, than those grown at 32 °C after 192 h. Roots and shoots exhibited the lowest length and dry biomass at 38 °C. Shoot lengths grown at 20 (74 mm) and 38 °C (51 mm) were 44% and 61% shorter than those grown at 32 °C (131 mm) after 180 h growth period, respectively. Growth at all three temperatures followed a similar pattern. Initially there was a linear growth phase followed by the reduction or cessation of growth. Time to cessation of growth varied with temperature and decreased faster at higher temperatures. Sowing of cotton should be accomplished before seedbed reaches a soil temperature (≥ 38 °C) detrimental for emergence. Further, the seedbeds should be capable of providing sufficient moisture and essential nutrients for emerging seedling before its seed reserves are exhausted to enhance seedling establishment in soil.

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Cited by (21)

Variation in thermotolerance of photosystem II energy trapping, intersystem electron transport, and photosystem I electron acceptor reduction for diverse cotton genotypes
2023, Plant Physiology and Biochemistry
Cotton breeding programs have focused on agronomically-desirable traits. Without targeted selection for tolerance to high temperature extremes, cotton will likely be more vulnerable to environment-induced yield loss. Recently-developed methods that couple chlorophyll fluorescence induction measurements with temperature response experiments could be used to identify genotypic variation in photosynthetic thermotolerance of specific photosynthetic processes for field-grown plants. It was hypothesized that diverse cotton genotypes would differ significantly in photosynthetic thermotolerance, specific thylakoid processes would exhibit differential sensitivities to high temperature, and that the most heat tolerant process would exhibit substantial genotypic variation in thermotolerance plasticity. A two-year field experiment was conducted at Tifton and Athens, Georgia, USA. Experiments included 10 genotypes in 2020 and 11 in 2021. Photosynthetic thermotolerance for field-collected leaf samples was assessed by determining the high temperature threshold resulting in a 15% decline in photosynthetic efficiency (T₁₅) for energy trapping by photosystem II (Φ_Po), intersystem electron transport (Φ_Eo), and photosystem I end electron acceptor reduction (Φ_Ro). Significant genotypic variation in photosynthetic thermotolerance was observed, but the response was dependent on location and photosynthetic parameter assessed. Φ_Eo was substantially more heat sensitive than Φ_Po or Φ_Ro. Significant genotypic variation in thermotolerance plasticity of Φ_Eo was also observed. Identifying the weakest link in photosynthetic tolerance to high temperature will facilitate future selection efforts by focusing on the most heat-susceptible processes. Given the genotypic differences in environmental plasticity observed here, future research should evaluate genotypic variation in acclimation potential in controlled environments.
Soil conditions affect cotton root distribution and cotton yield under mulched drip irrigation
2020, Field Crops Research
Citation Excerpt :
It has been reported that the optimal soil temperature for maximum production of cotton root material ranges from 28 to 32 °C (Pearson et al., 1970). Additionally, soil temperature higher than 38 °C or lower than 10 °C is detrimental for cotton root metabolic activity (Nabi and Mullins, 2008; Tan et al., 2017). In our study, the soil temperature in the root zone for BWT and FWT were within the range of 16.5–36.0 °C, thus the temperature was not expected to affect cotton root growth significantly.
Film-mulched drip irrigation with brackish water has been used widely for cotton (Gossypium hirsutum L.) production in arid regions. Full consideration of the promise of brackish water irrigation requires consideration of the dependence of cotton root characteristics on soil conditions. To date, these dependences are complex and poorly understood. The present study compared the impacts of soil temperature, water and salinity on root characteristics on cotton yield for brackish water irrigation treatment (BWT) and fresh water irrigation treatment (FWT). Spatial differences were observed for cotton root mass, root length, root surface-area and root volume distribution that corresponded with differences in the distributions of soil conditions. There existed a strikingly linear relationship between cotton root length and root mass. Soil temperature did not exert clear control on root system growth. Lower average root density and coarser roots were observed for BWT than for FWT as a result of the reduced soil temperature and the elevated soil salinity associated with BWT. Cotton shoot growth was more sensitive than root growth to salt stress for BWT. Higher root density and fine root proportion for FWT than BWT led to higher cotton yield. That is, more cotton roots were observed in the shallow mulched zone, which corresponded with high root water uptake rate and salt accumulation. Our study indicated that the easily-measured cotton root mass could be used as a practical surrogate for root length under mulched drip irrigation. Subsurface drip irrigation with drip tapes set in the shallow mulched zone may further promote cotton root system growth, allowing for sustainable cotton production with brackish water irrigation.
Developing novel hybrid models for estimation of daily soil temperature at various depths
2020, Soil and Tillage Research
Citation Excerpt :
Soil thermal regime determines the directions and rates of physical processes in soil such as moisture gradient and thermal fluxes (Araghi et al., 2017). It can also affect mass transfer in soil, soil structure and nutrient uptake (Børresen et al., 2007; Li et al., 2008; Wu et al., 2010; Xing et al., 2018), plant growth (Brar et al., 1992; Liu and Huang, 2005), seed germination (Nabi and Muillins, 2008), accumulation of organic matter in soil, soil respiration, organic matter destruction (Seyfried et al., 2001; Schimel et al., 2004; Rube, 2005; Xing et al., 2018), root development, appearance and growth of seedling (Hillel, 1998). Despite the importance and critical need for the knowledge of ST values in various fields of engineering, particularly in agriculture, accessibility to the ST data is very limited in many areas (i.e., developing countries).
Estimation of soil temperature (ST) as one of the vital parameters of soil, which has an impact on many chemical and physical characteristics of soil, is of great importance in soil science. This study applies a time series-based model, namely fractionally autoregressive integrated moving average (FARIMA), as well as two machine learning-based models consisting of feed-forward back propagation neural networks (FFBPNN) and gene expression programming (GEP) for daily ST estimation. In doing so, the daily ST data of three stations at four depths (5, 10, 50, and 100 cm) in Iran were used for the time period from 1998 to 2017. Studied stations were selected from different climates including arid (Isfahan station), semi-arid (Urmia station), and very humid (Rasht station) to evaluate the performance of models and generalize the outcomes in different climate classes. The performances of the developed models are evaluated via three statistical metrics including the root mean square error (RMSE), mean absolute error (MAE), and relative RMSE (RRMSE). Results obtained demonstrated that the machine learning-based FFBPNN and GEP models performed better than the time series-based FARIMA approach at all depths. As a result, negligible differences were observed between the accuracies of FFBPNN and GEP. In addition, this study developed novel hybrid models through combining the FFBPNN and GEP techniques with the FARIMA to enhance the accuracy of traditional FARIMA, FFBPNN, and GEP. The developed hybrid models named GEP-FARIMA and FFBPNN-FARIMA were found to achieve better estimates of daily ST data at different depths in comparison with the classical models. The daily ST estimates with the highest accuracy were observed at a depth of 50 cm via the GEP-FARIMA at Isfahan station (RMSE = 0.05 °C, MAE = 0.03 °C, RRMSE = 0.25% for the testing phase), the GEP-FARIMA at Urmia station (RMSE = 0.04 °C, MAE = 0.03 °C, RRMSE = 0.26% for the testing phase), and the FFBPNN-FARIMA at Rasht station (RMSE = 0.07 °C, MAE = 0.05 °C, RRMSE = 0.35% for the testing phase).
Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature
2019, Geoderma
Citation Excerpt :
Ts also controls below-ground processes of relevance to global and continental carbon budgets. Optimal plant growth depends on appropriate Ts, to the point that seeding times can be influenced (Liu and Huang, 2005; Nabi and Mullins, 2008; Zare abyaneh et al., 2016; Araghi et al., 2017; Samadianfard et al., 2018). In addition, the yields of various plant species will be maximised at certain ranges of Ts (Sommers et al., 1981).
Root zone temperature is one of the most important soil characteristics, controlling many of the physical, chemical and biological processes in the soil. Temperature varies by soil depth, and exerts a profound impact on plant germination and growth. In this study, the accuracy of two artificial intelligence models including support vector regression (SVR) and elman neural network (ENN) and their hybrids with firefly algorithm (SVR-FA and ENN-FA) and krill herd algorithm (SVR-KHA and ENN-KHA) was assessed in estimating soil temperature (Ts) at 5, 10, 20, 30, 50 and 100 cm depths at Maragheh meteorological station in north-western Iran. The results of the models were evaluated under 5 scenarios with various inputs including the main meteorological parameters measured at the station (air temperature, sunshine hours, relative humidity, wind speed and saturation vapour pressure deficit). Daily Ts data recorded from January 1, 2006 to December 30, 2012 and from January 1, 2013 to December 30, 2015 were used for model training and testing, respectively. The results showed that error rates have decreased from 5 to 10 cm soil depth (root mean square error (RMSE) reduced by 2.97, 4.68 and 3.19% for the best scenarios of SVR, SVR-FA and SVR-KHA models, respectively), whereas error rates have been increasing from 10 to 100 cm soil depths (RMSE increased by 62.4, 80.9 and 73.6% for the best scenarios of SVR, SVR-FA and SVR-KHA models, respectively). For the best scenarios of ENN, ENN-FA and ENN-KHA models, RMSE values decreased by 2.1, 1.6 and 3.1% from 5 to 10 cm depth and increased by 61.1, 84.1 and 81.1% from 10 to 100 cm depth, so that all six models reached their best performance at 10 cm soil depth. Examination of the results in terms of under-estimation or over-estimation of Ts indicated that the lowest and highest differences in performance between under- and over-estimation sets were 0.01 °C (SVR-FA at 5 cm depth) and 1.64 °C (SVR at 100 cm depth) for SVR-based models and 0 °C (ENN at 10 cm depth) and 0.56 °C (ENN at 100 cm depth) for ELM-based models, respectively. According to the results from the best scenarios of SVR, SVR-FA and SVR-KHA models in the under-estimation set at 100 cm depth, all the three models have exhibited a poorer performance over the temperature range 15–25 °C (RMSE increased by 56.7, 47 and 61.3% for SVR, SVR-FA and SVR-KHA, respectively) compared to temperature values outside that range. Exactly the same trend was also observed for ELM-based models, where the mentioned increases in RMSE were about 37.7, 59.4 and 55.5% for ELM, ELM-FA and ELM-KHA, respectively. According to the results, bio-inspired metaheuristic optimisation algorithms based on SVR and ENN which use appropriate meteorological parameters as inputs can have a relatively satisfactory performance in estimating Ts under climatic conditions similar to our study area, especially in lower depths, and can be used as an alternative to direct measurement of this important parameter.
Growth and yield response of faba bean to soil moisture regimes and sowing dates: Field experiment and modelling study
2019, Agricultural Water Management
Citation Excerpt :
Longer germination and emergence period of late sown faba bean in this study is most probably due to significant drop in soil temperature in May and June. Soil temperature, driven by solar radiation and air temperature, can significantly influence seed germination and plant growth (Nabi and Mullins, 2008). Early sown faba bean has a longer post-flowering duration which can be even longer when irrigation is applied.
In semiarid environments, faba bean (Vicia faba L.) production shows high year to year variability. In order to implement measures that minimize this variability, it is important to understand how abiotic and management factors affect its growth and yield. The aim of this study is to determine supplemental irrigation strategy and sowing date that maximize yield and water productivity of faba bean. A field experiment and simulation modelling were conducted on a mid-flowering faba bean cultivar under different sowing dates and watering regimes in south-eastern Australia. Drip irrigation, with two laterals plot, was used. At sowing, 20 kg P ha⁻¹ of single-phosphate was applied. Four watering regimes were applied: not irrigated (NI), fully irrigated (FI = 245.1 mm), vegetative-stage irrigated (VI = 60.1 mm), reproductive-stage irrigated (RI = 106.4 mm). Soil water tension was measured monitored using gypsum blocks at 15, 45 and 75 cm depths. The Agricultural Production Systems sIMulator (APSIM) was used to simulate grain yield. Sowing dates were 22 April (SD1) and 20 May (SD2). There was no significant difference between the yields of SD1-fully irrigated (5.24 t ha⁻¹), SD1-reproductive stage irrigated (5.85 t ha⁻¹), SD2-fully irrigated (5.52 t ha⁻¹), and SD2-reproductive stage irrigated (5.24 t ha⁻¹) treatments. The highest grain water use efficiencies, 1.65 kg m^-3 and 1.63 kg m^-3, were obtained from the reproductive stage irrigated SD1 and SD2, respectively. Harvest index and seed weight were significantly affected by sowing dates while the number of pods per plant and grain water efficiencies were significantly affected by irrigation. There was positive linear relationship between grain yield and post-flowering, rather than pre-/during flowering, green canopy cover. APSIM simulated faba bean yield with RMSE of 468 t ha⁻¹. Simulation modelling showed that supplemental irrigation applied from mid-September to the end of October increased grain yield by 23% relative to the rainfed treatment while supplemental irrigation from mid-August to the end of September resulted in 13% increase. Sowing in the first week of May or in the first week of June, instead of in the first week of April, decreased grain yield by 26% and 52%, respectively.
Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm
2018, Information Processing in Agriculture
Citation Excerpt :
Soil temperature (ST) is a critical variable that controls the underground physical processes, global and continental carbon and energy budgets, and has a primary role playing as an indicator for the overall health of the underlying soil necessary for socio-economic related activities (e.g. agriculture). In practical terms, this property of soil can expressively affect the germination of seeds [1], plant growth [2], nutrient’s uptake [3] and respiration within the soils [4]. Other activities such as soil evaporation [5] and the intensity of physical [6], chemical [7,8], and microbiological processes [9,10] in the soil media are also greatly governed by the thermal state of the soil, therefore, a knowledge of ST is particularly important in several decision-making tasks.
Forecasting soil temperature at multiple depths is considered to be a core decision-making task for examining future changes in surface and sub-surface meteorological processes, land–atmosphere energy exchange, resilient agricultural systems for improved crop health and eco-environmental risk assessment. The aim of this paper is to estimate monthly soil temperature (ST) at multiple depth: 5, 10, 20, 50 and 100 cm with a hybrid multi-layer perceptron algorithm integrated with the firefly optimizer algorithm (MLP-FFA). To develop the hybrid MLP-FFA model, the monthly ST and relevant meteorological variables for the city of Adana (Turkey) are collated for the period of 2000–2007. Construction of hybrid MLP-FFA model is drawn upon a limited set of predictors, denoted as soil depth, periodicity (or the respective month), air temperature, pressure and solar radiation, while the objective variable for MLP-FFA model is the forecasted ST at multiple depths. To the evaluate MLP-FFA, statistical metrics applied to test the model’s performance are: the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean bias error (MBE) where the sign of the difference is also considered. In conjunction with statistical metrics, a Taylor diagram is utilized to visualize the degree of similarity between the observed and forecasted soil moisture. In terms of the forecasted results, the hybrid MLP-FFA model is seen to outperform the standalone MLP model. The optimal MLP-FFA is attained for soil temperature forecasting at a depth of 20 cm (RMSE, MAPE of 0.546 °C, 2.40%) whereas the optimal MLP is attained for soil temperature forecasting at a depth of 50 cm (RMSE of 0.544 °C, 2.21%). Conclusively, the study advocates through statistical metrics attained the better utility of the hybrid MLP-FFA hybrid model. Given its superior performance, it is ascertained that the hybrid MLP model integrated with Firefly optimizer is a qualified ancillary tool that can be applied to generate precise soil temperature forecasts at multiple soil depths.

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: Project supported by the Pakistan Agricultural Research Council, Islamabad, Pakistan.

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Abstract

References (26)

Efiect of soil temperature on ecophysiological traits in seedlings of four boreal tree species

Forest Ecology and Management

Germination and emergence of irrigated cotton in Pakistan in relation to sowing depth and soil physical characters of the seedbed

Soil Tillage Res.

Emergence and early growth of an epigeal seedling (Daucus carota L.): Influence of soil temperature, sowing depth, soil crusting and seed weight

Soil Tillage Res

Response of wheat growth and CO₂ assimilation to altering root-zone temperature

Can. J. Bot.

Environmental factors and ecological processes in boreal forests

Annu. Rev. Ecol. Syst.

Roots exert a strong influence on the temperature sensitivity of soil respiration

Nature

Leaf elongation in relation to leaf water potential in soybean

J. Exp. Botany

Root Temperature and Plant Growth—A Review

Limitations of photosynthesis in Pinus tadeda L. (Loblolly pine) at low soil temperatures

Plant Physiol.

The efiect of freezing nights on photosynthesis, stomatal conductance, and internal CO₂ concentration in seedlings of Engelmann spruce

Plant Cell Environ.

The potential for photoinhibition of Pinus sylvestris L. seedlings exposed to high light and low soil temperature

J. Exp. Botany.

The influence of supra-optimal root zone temperatures on growth and stomatal conductance in Capsicum annuum L

J. Exp. Botany

Sugarbeet seedling growth from germination to flrst leaf stage

J. Agric. Sci. Cambridge

Cited by (21)

Variation in thermotolerance of photosystem II energy trapping, intersystem electron transport, and photosystem I electron acceptor reduction for diverse cotton genotypes

Soil conditions affect cotton root distribution and cotton yield under mulched drip irrigation

Developing novel hybrid models for estimation of daily soil temperature at various depths

Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature

Growth and yield response of faba bean to soil moisture regimes and sowing dates: Field experiment and modelling study

Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm

Soil Temperature Dependent Growth of Cotton Seedlings Before Emergence

Abstract

Forest Ecology and Management

Soil Tillage Res.

Soil Tillage Res

Response of wheat growth and CO2 assimilation to altering root-zone temperature

Can. J. Bot.

Environmental factors and ecological processes in boreal forests

Annu. Rev. Ecol. Syst.

Roots exert a strong influence on the temperature sensitivity of soil respiration

Nature

Leaf elongation in relation to leaf water potential in soybean

J. Exp. Botany

Root Temperature and Plant Growth—A Review

Limitations of photosynthesis in Pinus tadeda L. (Loblolly pine) at low soil temperatures

Plant Physiol.

The efiect of freezing nights on photosynthesis, stomatal conductance, and internal CO2 concentration in seedlings of Engelmann spruce

Plant Cell Environ.

The potential for photoinhibition of Pinus sylvestris L. seedlings exposed to high light and low soil temperature

J. Exp. Botany.

The influence of supra-optimal root zone temperatures on growth and stomatal conductance in Capsicum annuum L

J. Exp. Botany

Sugarbeet seedling growth from germination to flrst leaf stage

J. Agric. Sci. Cambridge

Response of wheat growth and CO₂ assimilation to altering root-zone temperature

The efiect of freezing nights on photosynthesis, stomatal conductance, and internal CO₂ concentration in seedlings of Engelmann spruce