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HighlightsMeasured field harvest index improved the performance of AquaCrop simulations.Optimal irrigated tomato yield can be achieved by maintaining available soil water depletion below 25%.Water productivity for tomatoes in humid regions can be suitably simulated using AquaCrop.Irrespective of the soil type, the water productivity was highest for fully irrigated fields compared to water limiting irrigated fields.Abstract. Methodologies to predict crop water requirements in arid and semi-arid regions are well known. Humid regions such as eastern Canada pose a challenge because irrigation is normally only required for short periods during the growing season to supplement rainfall. This study assessed the capability of the AquaCrop model to simulate the effects of different irrigation regimes on field-grown tomatoes (Solanum lycopersicum L.) in the humid region of eastern Canada. The experimental study was conducted at the Horticultural Research station of McGill University, Quebec, Canada. There were three irrigation treatments in 2017 and in 2019, that were based on the % depletion of available water content (AWC). The AquaCrop model was calibrated and verified with the 2017 and 2019 field data, respectively. The verified model was used to predict irrigation water requirements and fruit yield for the driest year (1993) and the average rainfall year (2001) of a 35-year historic weather dataset from 1986 to 2000, for three different soil types (silty clay, sandy loam, and heavy clay) under four irrigation regimes. Model performance was greatly improved when the seasonal harvest index (HI) measured from experimental data was used instead of the default model HI values. AquaCrop was suitable for estimating dry yield and total biomass with RMSE = 0.57 ton ha-1 and 0.89 ton ha-1, respectively, in the calibration phase, and RMSE = 0.28 ton ha-1 and 0.01 ton ha-1 for dry yield and total biomass, respectively, in the verification phase. These results indicate a very high accuracy of AquaCrop to estimate total above ground biomass and fruit yield in humid regions with seasonally adjusted HI values. The predictions showed that maintaining AWC depletion below 25% resulted in no significant decrease in crop yield and biomass, making it an optimum water management guideline for irrigated tomato production in Quebec. The findings of this study are useful to crop growers and water resource managers in eastern Canada, who seek better irrigation strategies to optimize productivity. Keywords: AquaCrop, Crop Modeling, Humid climate, Irrigation water requirement, Tomatoes, Yield.

HighlightsWater table rise was slower under a sparsely perforated pipe as compared to a pipe that was densely perforated.Response time increased as buried pipe depth decreased for both combinations of perforation characteristics.Fluxes from buried pipes spaced at 15 m in loamy sand satisfied the water demand for cereal/grain crops (6 mm d-1).Increasing perforation density on the pipe wall is the most effective way to reduce exit head losses.Abstract. It is important to consider the water table rise into the unsaturated zone when designing subsurface irrigation systems that consist of buried perforated corrugated pipes. Corrections to pipe spacing equations for head losses due to perforations are based on the entrance resistance, which is derived for buried pipes operating in drainage mode. However, the boundary condition at the soil-pipe interface when operated in subsurface irrigation mode requires the use of the exit resistance to accurately account for perforations in the pipe wall. Simulations of a subsurface irrigation system with four soil textures, three lateral pipe spacings, and three buried depths were carried out, with the numerical model COMSOL, to demonstrate the effects of variable perforation characteristics on the transient water table rise into the unsaturated zone. Soil-water fluxes were also computed for the system when operated under steady-state conditions. The simulated water table rise exceeded or approached a predetermined target water level of 1.65 m above the buried pipes in coarse-textured soils (loamy sands and silt loams) with lateral pipe spacings as large as 10 m. Increasing the number and length of rectangular slots in the pipe wall (densely perforated) can reduce the water table response time to reach the target level by 15 h in a silt loam soil and 25 h in a clay soil. The optimum pipe spacing needed to sustain a soil-water flux of 6 mm d-1 (common to eastern Canada and the U.S. Midwest) can be increased from 15.1 to 16.2 m in loamy sands by using densely perforated pipes. In contrast, increasing perforations in pipes buried in clays and sandy clays will result in pipe spacing increases of less than 0.2 m because these fine-textured soils have lower hydraulic conductivity. This study has demonstrated that variable perforation characteristics can be included in the analysis and design of subsurface drainage systems for improving water management. Keywords: Corrugated perforated pipes, Exit resistance, Head loss, Subirrigation, Unsaturated zone, Water table response.

Climatic parameters influence CO2 emissions and the complexity of the relationship is not fully captured in biophysical models. Machine learning (ML) is now being applied to environmental problems, and it is, therefore, opportune to investigate ML models in CO2 predictions from agricultural soils. In this study, six ML models were compared for their predictive performance by comparing field measurements of CO2 emissions from two fertiliser treatments: inorganic fertiliser (IF) and solid cattle manure supplemented with inorganic fertiliser (SCM) applied to a maize-soy rotation. The study also included a generalised scenario where all the data from IF and SCM were included in one dataset. The ML models include support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), feed-forward neural network (FNN), radial basis function neural network (RBFNN), and extreme learning machine (ELM). The input parameters were soil moisture, soil temperature, soil organic matter, soil total carbon, soil total nitrogen, air temperature, solar radiation and pan evaporation, while the output parameter was field measured CO2 emissions. The results of this study demonstrated that RF was the best at predicting CO2 emissions from IF [coefficient of determination (R2) = 0.92 and root mean square error (RMSE) = 2.27], SCM (R2 = 0.94 and RMSE = 2.86) and generalised scenarios (R2 = 0.86 and RMSE = 3.05). We conclude that ML models provide an innovative, robust and time-efficient alternative to biophysical models.

Soil organic phosphorus (Po) that is hydrolyzed by phosphatases into inorganic phosphate is a source of phosphorus (P) for agricultural crops. However, the hydrolysis of Po compounds is contingent on their chemical form and stabilization in the soil matrix. We quantified three hydrolyzable Po pools (simple phosphomonoesters, phospholipids, and nucleic acids) by adding substrate-specific phosphatases to Hedley sequentially fractionated extracts from three organic soils cultivated with carrots in Southern Ontario, Canada. We observed that most of the molybdate-unreactive P was hydrolyzable in the deionized water, 0.5 mol L−1 NaHCO3, and 0.1 mol L−1 NaOH P pools (67–92%) and accounted for 14–254 kg P ha−1 in these arable organic soils. Nucleic acids represented 79% of the hydrolyzable Po in these soils but were mostly found in the 0.1 mol L−1 NaOH P pool (89%), suggesting that this Po form is bound to the soil matrix, probably to organic complexes based on the strong association between nucleic acids and total organic C (ρ = 0.805). Therefore, we propose that nucleic acids are a major hydrolyzable Po pool that are stabilized by the organic C contained in the organic soils of this study. This Po compound class should be considered in the Po biogeochemical cycle of these soils.

There are increasing demands to reduce greenhouse gas emissions from agricultural soils worldwide. A significant portion of these emissions occur in cold regions during soil’s freezing and thawing. Focusing on over-winter cropland nitrous oxide (N2O) emissions, a review of 21 relevant peer-reviewed studies with a total of 88 comparisons was conducted to quantify the efficacy of field management practices (no-till, cover crops (CCs), nitrification, and urease inhibitors (NI + UI)) in reducing emissions. We also assessed these mitigation practices’ efficacy across soil types and between cold humid and cold dry areas. The ratio of non-growing season emissions to full-year N2O emissions reported in the studies used in this review ranged between 5 and 91%. No-till significantly reduced N2O emissions by 28%, and this effect was more pronounced in drier climates. NI + UI also significantly reduced over-winter emissions by 23% compared to conventional fertilizers, and this effect was more evident in medium-textured soils than coarse soils. CCs showed an overall reduction potential of 18%; however, this effect was not significant. This review showed that under the CC practice, N2O emissions were reduced overall in humid climates but increased in drier climates, while no-till and NI + UI practices effectively reduced over-winter emissions in both dry and humid winter regions and all soil types.

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A 3-year study was conducted to determine the irrigation requirements for field tomato production in southwestern Ontario, Canada. Irrigation requirements were ascertained using both a weather-based crop water model, and data from soil moisture sensors. The equivalent irrigation depth applied based on real-time soil moisture measurements among four soil moisture treatments ranged from 102 to 168 mm, 85 to 190 mm, and 58 to 196 mm for 2008, 2009, and 2010, respectively. Using the crop simulation model—AquaCrop, the net irrigation water requirement (NIWR) was estimated as 163–236 mm over the 3-year period. The model overestimated the NIWR compared to the real-time sensor-based measurements. A comparison between these two approaches indicated that a soil moisture depletion of ≤ 45% available water content (AWC) resulted in adequate moisture to meet crop water requirements, and correspondingly produce higher crop yields. From this study, we conclude that real-time soil moisture sensors proved to be very effective for scheduling irrigation of processing tomatoes in Eastern Canada and leads to higher water savings.

Water table management is a recommended practice to maintain crop production in Eastern Canada. Grain corn is highly susceptible under climate change and adoption of better management practices is ...

Conservation tillage and crop residues should increase the soluble organic carbon and nitrate concentration in agricultural soil, which increases the denitrification potential. Basal denitrification (72 h laboratory incubation) was 2.1–2.7 times higher in a sandy loam soil under 15 yr of conservation tillage than conventional tillage and 1.8–2.0 times higher with high-residue (additional input 8.6–9.4 Mg dry matter·ha−1·yr−1) than low-residue inputs. Adding glucose and nitrate increased the soil denitrification potential 3- to 14-fold. Denitrification was limited by carbon availability, even in soil with 15 yr of conservation tillage and high-residue inputs.

This study assessed the use of reflectance indices for detecting the combined effects of water and nitrogen stress in tomatoes (Solanum lycopersicum L.). Spectral reflectance data were acquired from tomato plants, subjected to three water and three nitrogen treatments. Irrigation water was applied in amounts of 100, 70, and 30% of full replenishment of root zone soil water to field capacity. Nitrogen application was 100, 70, and 30% of crop nutrient requirement. The treatments were replicated five times in a randomised complete block design. Plant stress indicators, including leaf temperature (Tc), relative water content (RWC), yield, and leaf chlorophyll content (LCC) were measured at the same time of leaf reflectance data, during the growing season. Reflectance indices including Normalised Difference Vegetation Index (NDVI), Renormalised Difference Vegetation Index (RDVI), Optimised Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 550 nm (PRI550), normalised PRI (PRInorm), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and WI/NDVI were obtained from the reflectance data. The results showed that the PRI550, PRInorm, and WI were the most sensitive indices for distinguishing crop water stress, while RDVI, PRInorm, and TCARI had the best correlation with nitrogen stress indicators. PRInorm was the most sensitive index for detecting the combined effect of water and nitrogen stress. This study provided more insights into the usefulness of leaf spectral features for assessing crop abiotic stress. Measuring these indices with hyperspectral sensors provides a rapid, non-destructive, and reliable approach for estimating crop stress.

HighlightsRZWQM2 was compared with DNDC to predict greenhouse gas emissions.RZWQM2 was applied to simulate the greenhouse gas emissions under manure application.RZWQM2 performed better than DNDC in simulating soil water content and CO2 emissions.Abstract. N management has the potential to mitigate greenhouse gas (GHG) emissions. Process-based models are promising tools for evaluating and developing management practices that may optimize sustainability goals as well as promote crop productivity. In this study, the GHG emission component of the Root Zone Water Quality Model (RZWQM2) was tested under two different types of N management and subsequently compared with the Denitrification-Decomposition (DNDC) model using measured data from a subsurface-drained field with a corn-soybean rotation in southern Ontario, Canada. Field-measured data included N2O and CO2 fluxes, soil temperature, and soil moisture content from a four-year field experiment (2012 to 2015). The experiment was composed of two N treatments: inorganic fertilizer (IF), and inorganic fertilizer combined with solid cattle manure (SCM). Both models were calibrated using the data from IF and validated with SCM. Statistical results indicated that both models predicted well the soil temperature, but RZWQM2 performed better than DNDC in simulating soil water content (SWC) because DNDC lacked a heterogeneous soil profile, had shallow simulation depth, and lacked crop root density functions. Both RZWQM2 and DNDC predicted the cumulative N2O and CO2 emissions within 15% error under all treatments, while the timing of daily CO2 emissions was more accurately predicted by RZWQM2 (RMSE = 0.43 to 0.54) than by DNDC (RMSE = 0.60 to 0.67). Modeling results for N management effects on GHG emissions showed consistency with the field measurements, indicating higher CO2 emissions under SCM than IF, higher N2O emissions under IF in corn years, but lower N2O emissions in soybean years. Overall, RZWQM2 required more experienced and intensive calibration and validation, but it provided more accurate predictions of soil hydrology and better timing of CO2 emissions than DNDC. Keywords: CO2 emission, Corn-soybean rotation, Inorganic fertilization, Manure application, N2O emission, Process-based modeling.

This paper presents a machine learning (ML) surrogate modeling for fast processing in meshless/meshfree methods. The main idea is to leverage the universal approximation (UA) propriety of supervised ML models (shallow/deep learning and other regression models) to surrogate the heavy shape function construction in meshless methods. The resulting ML metamodel preserves the same accuracy of the meshless interpolation while avoiding costly matrix inversion operations. The total computation time for solving 3D test simulation problems (using more than 20[Formula: see text]k nodes) is reduced by a factor of 1[Formula: see text]k in the case of the Gaussian process (GP) metamodel.

Fertilizer and water management practices have short- and long-term effects on soil chemical and physical properties and, in turn, greenhouse gas (GHG) emissions. The goal of this 4-yr field study was to establish the relationships between soil properties, agronomic practices, and GHG (CO

Greenhouse gas (GHG) emissions from arable soils are released to the atmosphere in the form of nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4). These soil emissions were measured from three horticultural production systems in Quebec and Ontario, Canada, for the following crops: (1) cranberry production in an artificial bog on sandy soil; (2) tomato production on a conventionally farmed loamy sand soil; and, (3) onion production on an organic soil. In-situ soil N2O, CO2 and CH4 fluxes were measured at each site using the non-steady-state static chamber method. Tomato production on the loamy sand and onion production on the organic soils were the greatest producers of soil GHG emissions, with highest global warming potentials of 11,000 kg CO2-eq ha−1 and 7800 kg CO2-eq ha−1, respectively. The cranberry crop had the lowest global warming potential (2700 kg CO2-eq ha−1). Water management (water table depth, subsurface and surface drip irrigation) had a negligible impact on soil GHG emissions. The main factors influencing the production of N2O were spring thaw, rate and timing of inorganic fertilizers, and precipitation. Soil CO2 fluxes were primarily governed by soil temperature. Soil CH4 fluxes were low, as the soils from all crops were both oxidizing and producing CH4 simultaneously. Production of CH4 increased when the cranberry fields were flooded. Organic and mineral soils produced comparable amounts of soil greenhouse gas emissions.

Early detection of plant water status is essential to optimize crop water use, and to implement water savings methods such as precision irrigation. This study investigated the potential of using crop reflectance indices to detect water stress, in order to improve irrigation of bell pepper (Capsicum annuum L.), grown under greenhouse conditions. Spectral data were acquired from bell pepper plants, with five different irrigation regimes namely 100, 80, 60, 40, and 20% of plant available water, in a completely randomized design. Plant stress parameters including stomatal conductance (Gs), canopy temperature (Tc), relative water content (RWC), yield, and volumetric soil moisture content (SMC) were concurrently measured with spectral data acquisition from the plants throughout the growing season. Various reflectance indices including Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 570 nm (PRI570), Photochemical Reflectance Index centered at 553 nm (PRI553), normalized PRI (PRInorm), Water Index (WI), and WI/NDVI were obtained from the spectral data. The relationships between these crop reflectance indices and the water stress indicators were statistically examined at the five irrigation levels. The results revealed that PRI553, WI, RDVI, PRInorm, and WI/NDVI were the most useful indices for detecting water stress in bell pepper plants. The findings of this study show promise of using a proximal method for assessing water stress and to improve water management of high value vegetable crops grown under greenhouse conditions.

Water table management with controlled drainage and subsurface-irrigation (SI) has been identified as a Beneficial Management Practice (BMP) to reduce nitrate leaching in drainage water. It has also been shown to increase crop yields during dry periods of the growing season, by providing water to the crop root zone, via upward flux or capillary rise. However, by retaining nitrates in anoxic conditions within the soil profile, SI could potentially increase greenhouse gas (GHG) fluxes, particularly N2O through denitrification. This process may be further exacerbated by high precipitation and mineral N-fertilizer applications very early in the growing season. In order to investigate the effects of water table management (WTM) with nitrogen fertilization on GHG fluxes from corn (Zea mays) agro-ecosystems, we conducted a research study on a commercial farm in south-western Quebec, Canada. Water table management treatments were: free drainage (FD) and controlled drainage with subsurface-irrigation. GHG samples were taken using field-deployed, vented non-steady state gas chambers to quantify soil CO2, N2O and CH4 fluxes weekly. Our results indicate that fertilizer application timing coinciding with intense (≥24 mm) precipitation events and high temperatures (>25 °C) triggered pulses of N2O fluxes, accounting for up to 60% of cumulative N2O fluxes. Our results also suggest that splitting bulk fertilizer applications may be an effective mitigation strategy, reducing N2O fluxes by 50% in our study. In both seasons, pulse GHG fluxes mostly occurred in the early vegetative stages of the corn, prior to activation of the subsurface-irrigation. Our results suggest that proper timing of WTM mindful of seasonal climatic conditions has the potential to reduce GHG emissions.

Advancements in soil sensor technology have allowed for comprehensive measurements of soil physical characteristics. Sensor measurements help in understanding the qualities of the soil rooting zone; they can be used to create detailed soil maps to facilitate the application of site-specific management decisions for agriculture or resource management purposes. Currently, the benefits and widespread use of these advanced methods are hindered by several factors, including the ability to capture several soil properties at once, consistency between measurements, and the labour required to collect the data. This paper describes a partnership between the autonomous electric tractor company Ztractor and McGill University, for the development of a sensor system capable of capturing several soil physical characteristics at one time through on-the-spot measurements linked to the autonomous tractor Bearcub. Integrating traditional measurement techniques with automated functionality, the sensor platform, centered around a cone penetrometer, logs data for several soil physical properties to the tractor. The automated functionality seeks to improve the quality of data as all parameters in the testing process are standardized, eliminating inconsistencies caused by manual measurements.

Corrugated high density polyethylene pipes, where groundwater enters through perforations on the pipe wall, are widely used in subsurface drainage systems on agricultural lands. There has b...

Machine learning (ML) models are increasingly used to study complex environmental phenomena with high variability in time and space. In this study, the potential of exploiting three categories of ML regression models, including classical regression, shallow learning and deep learning for predicting soil greenhouse gas (GHG) emissions from an agricultural field was explored. Carbon dioxide (CO2) and nitrous oxide (N2O) fluxes, as well as various environmental, agronomic and soil data were measured at the site over a five-year period in Quebec, Canada. The rigorous analysis, which included statistical comparison and cross-validation for the prediction of CO2 and N2O fluxes, confirmed that the LSTM model performed the best among the considered ML models with the highest R coefficient and the lowest root mean squared error (RMSE) values (R = 0.87 and RMSE = 30.3 mg·m−2·hr−1 for CO2 flux prediction and R = 0.86 and RMSE = 0.19 mg·m−2·hr−1 for N2O flux prediction). The predictive performances of LSTM were more accurate than those simulated in a previous study conducted by a biophysical-based Root Zone Water Quality Model (RZWQM2). The classical regression models (namely RF, SVM and LASSO) satisfactorily simulated cyclical and seasonal variations of CO2 fluxes (R = 0.75, 0.71 and 0.68, respectively); however, they failed to reasonably predict the peak values of N2O fluxes (R

Global pandemics, epidemics, and disease outbreaks have plagued humanity for ages. However, the scale and spread of pandemics and epidemics increased drastically in recent history. Currently, COVID-19 pandemic (respiratory illness) caused by SARS-CoV-2 virus ravaging fright around the world. The infectious nature of the disease affected the global and local economies and societies. Nations are forced to implement precautionary measure such as restriction of mobility to stop the spread of disease, which vastly affecting the major economic sectors including food and agricultural industry. The precautionary measures such as travel restrictions disrupt the food production, distribution, and supply chain network. The impact is widely seen on livestock and aquaculture farming, which threatens the food security and calls for immediate policy interventions from the government. This chapter investigates the challenges posed by COVID-19 pandemic on agricultural sector and food industry in India and determine the possible mitigation measures. The study presented the recommendation to call for government support in strengthen policies to boost agricultural sector activities to achieve transitions towards food security during and post-pandemic period.

Irrigation practices change the soil moisture in agricultural fields and influence emissions of greenhouse gases (GHG). A 2 yr field study was conducted to assess carbon dioxide (CO2) and nitrous oxide (N2O) emissions from surface and subsurface drip irrigated tomato (Solanum lycopersicum L.) fields on a loamy sand in southern Ontario. Surface and subsurface drip irrigation are common irrigation practices used by tomato growers in southern Ontario. The N2O fluxes were generally ≤50 μg N2O-N m−2 h−1, with mean cumulative emissions ranging between 352 ± 83 and 486 ± 138 mg N2O-N m−2. No significant difference in N2O emissions between the two drip irrigation practices was found in either study year. Mean CO2 fluxes ranged from 22 to 160 mg CO2-C m2 h−1 with cumulative fluxes between 188 ± 42 and 306 ± 31 g CO2-C m−2. Seasonal CO2 emissions from surface drip irrigation were significantly greater than subsurface drip irrigation in both years, likely attributed to sampling time temperature differences. We conclude that these irrigation methods did not have a direct effect on the GHG emissions from tomato fields in this study. Therefore, both irrigation methods are expected to have similar environmental impacts and are recommended to growers.

Agricultural system models are promising tools in evaluating the agro-environmental effects of water management practices. However, very few models have been tested using a comprehensive hydrologic data set. The present study’s objective was to evaluate the hydrologic component of RZWQM2 (Root Zone Water Quality Model) using a comprehensive hydrological dataset including subsurface tile drainage, subirrigation, soil water content, sap flow and crop growth data such as leaf area index, crop yield and crop growth stages. Drawing on 2008 and 2009 data from a farm site in Southern Quebec, the RZWQM2 model showed accurate simulation in soil water content, sap flow, growth stage, leaf area index, and crop yield. While mean values for growing season tile flow under both free drainage (FD) and controlled drainage with subirrigation (CD-SI) were reasonably accurate, winter tile flow was significantly overestimated, indicating RZWQM2’s reliability to be compromised by its imperfect winter drainage process. Accordingly, a Kalman filter technique was applied to enhance model reliability and reduce predictive uncertainties. A novel RZWQM2 model equipped with a Kalman filter algorithm adequately simulated, in both calibration and validation phases, the hydrology and corn growth which occurred under both FD and CD-SI systems at the selected field site. Simulation results suggest that RZWQM2 model can be used for water management under subsurface drained and irrigated field and the Kalman filter technique significantly improved the accuracy of RZWQM2 model in simulating winter drainage in cold areas.

Differences in the boundary condition near buried corrugated pipes used for combined drainage and irrigation show that the hydraulic resistance due to perforations are not identical under both drainage and subsurface irrigation modes. No studies to date have sufficiently examined how perforation characteristics such as size and configuration affect the exit head loss of buried corrugated pipes used for subsurface irrigation. This research proposes a new dimensionless parameter called the exit resistance (αx) that accounts for variable perforations and can be used to compute the exit head loss due to capillary rise under subsurface irrigation. Datasets obtained from 189 numerical simulations were used to investigate how the size and configuration of rectangular slots affect αx. The results show that the number of perforation lines (N) and longitudinal spacing of slots (ay) on the pipe wall have the largest impact on αx. The difference in αx can be as large as 50% when N varies between 4 and 10, while slots placed in every corrugation valley (ay = 1.645 cm) provide less than 60% of the exit resistance compared to those spaced in every third valley (ay = 4.935 cm). Exit resistance was also found to vary with the pressure head on the subsurface irrigation pipes. The impact of heterogeneity from layered soils on αx of buried perforated pipes was also investigated. The results indicated that αx is not influenced by soil heterogeneity. In general, the findings from this study implies that it is better to perforate buried corrugated pipes with slots in every corrugation valley to reduce exit head losses and maximize upward soil-water fluxes in subsurface irrigation systems.

Variable rate irrigation (VRI) can increase water use efficiency and productivity by applying water based on site-specific needs. In this study, the performance of a five-span centre-pivot irrigation system (CPIS) retrofitted with a commercial variable-rate irrigation package was evaluated at constant and variable application depths at the Alberta Irrigation Technology Centre (AITC) in southern Alberta, Canada. Two sets of experiments were designed to investigate the uniformity of application of the system during the 2013 and 2014 growing seasons. The first set of catch-can trials were carried out with three irrigation rates in the direction of pivot travel. Three different wind regimes were observed during the catch-can trials. Catch-cans were arranged in grid configurations within the experimental plots located under one irrigation zone in span 4. The Christiansen coefficient of uniformity (CU) ranged from 90.4 to 94.4%. Wind speeds of 3.3 and 6.5 m s-1 negatively and significantly impacted the CU values. The second set of catch-can trials were performed with used and new sprinklers in a transect along the pivot lateral during the 2014 growing season. The Heermann and Hein coefficient of uniformity (CUHH) ranged from 89.0 to 93.5% and from 81.7 to 94.4% with constant and variable application depths, respectively. The greatest (94.4%) and least (81.7%) CUHH values were observed where water applications were 100 and 40% of the set point, respectively. Overall, the uniformity of application of CPIS retrofitted with the commercial VRI package both along the system's lateral and in the travel direction were above 90% for the majority of the trials under the different wind speeds and water application depths. Copyright © 2017 John Wiley & Sons, Ltd.

Long-term (1991-2013) field experiments were conducted to study the effects of different tillage and residue management practices on grain corn production in eastern Canada. Three different tillage practices, namely conventional tillage (CT), reduced tillage (RT), and no tillage (NT), along with two residue management practices, namely with residue (R) and without residue (NR), were considered. Field measurements of grain yield, biomass, soil organic carbon (SOC), soil moisture, and mineral nitrogen were used to evaluate the performance of the DSSAT model and to understand the impacts of long-term tillage and residue management practices. The observed corn yield, biomass, LAI, and nitrate-nitrogen (NO3-N) were not found to be significantly different under the various tillage and residue practices. The observed soil moisture showed a significant difference in the upper 10 cm soil layer. The SOC pool at 0-20 cm depth was reduced by 9.5% for CT and increased by 17.3% and 7.6% for RT and NT treatments, respectively. The DSSAT model was able to simulate corn and biomass yield, LAI, soil moisture, and SOC with RMSE ranging from 2% to 31%, indicating reasonable model performance. The model did not provide accurate results for NO3-N and soil moisture simulations, with RMSE ranging from 34% to 78%. Further improvements in the model are needed to better simulate soil moisture and N dynamics under different tillage and residue practices.

Climate change significantly influences the global hydrological cycle and consequently affects climatic extremes. Therefore, the present study focuses upon the varying patterns of climate extremes across India for past and future. Here, a comprehensive methodological framework incorporating univariate and joint probabilistic analysis (using copulas) has been proposed to study the climate extremes. Moreover, multi-model ensembles of climate extreme indices are developed using reliability ensemble averaging (REA) technique for reducing uncertainty in projecting future climate extremes. The datasets used are daily precipitation, maximum temperature and minimum temperature for past (1975–2019), and future (2025–2095) under RCP4.5 and RCP8.5 scenario. A preliminary assessment of climate extremes indicates that R20, R95p, consecutive wet days (CWD), TXx, TNx, TX90p, TN90p, TNn and TXn show a positive trend predominantly across India in future. The bivariate assessment of precipitation extreme indices for the period (1989–2019) suggests that parts of north-western, north-eastern, southern, western region and Western Ghats are highly prone to floods and a large portion of country is vulnerable towards co-occurrence of floods and droughts. Moreover, integrated assessment of extreme number of hot days (TX90p) and nights (TN90p) indicate that along with the projected increase in hot days/nights, the frequency of their concurrence in a year is likely to increase in future over the country. The present study provides useful information on the regional distribution of climate extremes and can further contribute to facilitate an effective adaptation strategy.

Large quantities of mineral phosphorus (P) fertilizer are often applied to intensively cultivated organic soils. Although erosion and runoff can contribute to loss of P, the large amount of fertilizer applied causes a rapid build-up of this nutrient, resulting in the downward movement of excess P in the soil profile and subsequent loss through tile drainage water. For arable organic soils, these losses often occur through subsurface tile drains, a common requirement to maintain a favorable air–water balance in the crop root zone, as well as to prevent soil subsidence. As such, subsurface drainage is a major pathway for agricultural P loss, contributing to persistent eutrophication of rivers, lakes, and estuaries globally. Although studies have been conducted on P mitigation within organic soils, application of drainage water management (DWM) as a P mitigation strategy in these soils, has not been extensively studied. The objective of this paper is to address this gap in knowledge by reviewing previous studies on P losses from subsurface drained agricultural organic soils while evaluating potential mitigation strategies. Specifically, this paper assesses the unique properties of organic soils that could influence P fate and transport, such as the distribution of P pools within the soil pools; variable pore geometry, hydrophobicity, and shrinkage; P loads exiting tile drains; and DWM practices in mitigating P losses. It is concluded that P retention is affected by the dynamic nature of soil water movement in organic soils and that substantial P loads enter surrounding water bodies via subsurface drainage effluent. There is evidence that DWM is an effective best management practice in the abatement of subsurface P losses.

In this study, seasonal trends as well as dominant and significant periods of variability of drought variables were analyzed for 30 rainfall subdivisions in India over 141 years (1871–2012). Standardized precipitation index (SPI) was used as a meteorological drought indicator, and various drought variables (monsoon SPI, non-monsoon SPI, yearly SPI, annual drought duration, annual drought severity and annual drought peak) were analyzed. Discrete wavelet transform was used in conjunction with the Mann-Kendall test to analyze trends and dominant periodicities associated with the drought variables. Furthermore, continuous wavelet transform (CWT) based global wavelet spectrum was used to analyze significant periods of variability associated with the drought variables. From the trend analysis, we observed that over the second half of the 20th century, drought occurrences increased significantly in subdivisions of Northeast and Central India. In both short-term (2–8 years) and decadal (16–32 years) periodicities, the drought variables were found to influence the trend. However, CWT analysis indicated that the dominant periodic components were not significant for most of the geographical subdivisions. Although inter-annual and inter-decadal periodic components play an important role, they may not completely explain the variability associated with the drought variables across the country.

Escherichia coli outbreaks associated with Ready-to-Eat vegetables have brought attention to irrigation water contaminated with pathogens. A greenhouse study was conducted to determine the potential transfer and survival of E. coli from contaminated irrigation water to lettuce leaves and in soil. Contamination of lettuce was studied using two irrigation methods (drip and sprinkler irrigation) and two soil types (organic and mineral soil) during June–August 2014. There were pots inoculated with bacteria E. coli ATCC8739, and un-inoculated pots without bacteria. The four treatments were replicated five times each for the inoculated and un-inoculated pots. One-time application of E. coli-contaminated bacterial water (7.23 log cfu/ml) was used to irrigate lettuce, after two weeks of transplanting the seedlings into pots. The transfer and survival of bacteria were observed in the soil and lettuce leaves over a time period of 30 days after inoculation. It was observed that there was highest transfer and survival of bacteria on organic soil with drip irrigation, i.e., 7.03 log cfu/g (63.1% bacterial retention) and dropped to 4.71 log cfu/g (0.3% retention) on the 30th day. For lettuce leaves, maximum contamination was observed with plants growing in organic soil with sprinkler irrigation and it decreased from 5.32 log cfu/g (1.23% retention) to 2.88 log cfu/g (0.004% retention) by the 30th day. Soil type significantly influenced survival of the bacteria, as organic soil showed more contamination (i.e., 63.1 and 0.57% retention) than mineral soil (i.e., 0.47 and 0.04% retention) on the 2nd day. Also, an early bacterial decay or movement to lower depths was observed in mineral soil compared to the organic soil. Irrigation method has a significant influence in contaminating the lettuce leaves, as sprinkler irrigation resulted in higher transfer and retention of bacteria on the edible portion of the plant. Also, in the case of sprinkler irrigation, fate and transport of bacteria were independent of the soil type. Drain water from the pots was also collected to study bacteria transport, but none were positive for the bacterial presence.