Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe.

Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011-2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011)(2012)(2013)(2014)(2015) and validated (2016)(2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R 2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.Water 2020, 12, 5 2 of 18 procedure that requires thorough knowledge of physical hydrological parameters, big data, hydrological models, model inputs, and the geometry of watersheds [3]. Aspects of hydrogeology-i.e., geological factors affecting the distribution and movement of groundwater underneath the soil surface-need to be properly understood when modeling GWLs and manipulating the modeling results. Watershed scale fluctuations in GWLs occur over a period of several decades, and the resulting cumulative effects on streamflow depletion may not be fully realized for years [4]. Resultantly, depending upon the distance of the pumping station from the stream and the geologic characteristics of the aquifer, the groundwater system may take decades to recover from streamflow depletion caused by intermittent pumping. Components of the surface-and the sub-surface physical hydrology of a watershed-i.e., streamflow and groundwater flow, respectively-are interconnected, making the stream-aquifer interaction one of the key processes governing the groundwater flow pattern in a watershed [1]. Groundwater fluctuations affect streamflow and vice versa, as the pumping wells capture groundwater that would otherwise discharge to connected streams, rivers, and other surface-water bodies [4]. Francis [5] reported that, in typical watersheds of Prince Edward Island, the base flow represents almost 80% of the streamflow in the late summer and fall months. Stream length in these island watersheds ranges from less than 1 km to 2...

Climate change induced uneven patterns of rainfall emphasize the use of supplemental irrigation in rainfed agriculture. The Penman-Monteith method was used to calculate supplemental irrigation for water budgeting of a potato crop in Prince Edward Island, Canada. Cumulative gaps between rainfall and crop evapotranspiration (ETc) during August and September of the study years were due to high crop coefficient factor, justifying the need for supplemental irrigation. Pressurized irrigation systems, including sprinklers, fertigation, and drip irrigation were installed, to evaluate the impact of scheduled supplemental irrigation in offsetting deficits in irrigation water requirements in comparison with conventional practice of rainfed cultivation (control). A two-way ANOVA examined the effect of irrigation methods and year on potato tuber yield, water productivity, tuber quality, and payout. Sprinkler and fertigation systems performed better than drip and control treatments. In terms of payout returns and potato tuber quality (percentage of marketable potatoes), the sprinkler treatment performed significantly better than the other treatments. However, for water productivity, fertigation treatment performed significantly better than control and sprinkler treatments during both years. The use of supplemental irrigation is recommended for profitable cultivation of potatoes in soil, agricultural, and environmental conditions resembling to those of Prince Edward Island.

To understand climate change impacts on Prince Edward Island (PEI), Canada, historical daily precipitation and temperature of the island was investigated between the periods: 1931–60 (1940s), 1961–90 (1970s), and 1991–2020 (2000s) in its eastern, central, and western parts. Observed climatic data were utilized, augmented by some validated modeled data of Pacific Climate Impact Consortium (PCIC) for missing years. Statistically significant warming of the island was found ranging from 1.14°C in the east to 0.75°C in the west from the 1970s to 2000s. The warming trend during the period was distributed throughout the year including winters. In the east, mean monthly temperature significantly increased in all the months except for January, March, and June. Significant increase in temperature was found solely during August (+0.80°C) in central, and for August (+0.64°C), September (+0.99°C), and October (+0.73°C) in western parts. Proportionate increase in annual minimum temperature was greater than the maximum, particularly in eastern (+1.57°C) and central (+0.75°C) parts and thus indicated moderated cold there. Over the same 30‐year period, annual precipitation increased 6 percent in the east but decreased 5 and 8 percent in the central and the western PEI, respectively. The changes in precipitation were not statistically significant, except snowfall reduction (−20%) in the west, which was a statistically significant change. Interannual precipitation variations during wet and dry years having 20 and 80 percent probabilities of exceedance, respectively, ranged 350–470 mm/year during 1991–2020. Rainfall intensities, measured by hourly data, increased from 1.15 to 2.24 mm/hr, on average in central and western parts, respectively, in 2004–17 compared to 1970s. Impacts of the rising temperatures, decreasing precipitation, and uneven and intense rainfalls patterns on water resources and rainfed agriculture need further investigations. Climate change adaptations be included in existing water policies to mitigate the impacts.