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In this paper we first compare three different methods of spatial interpolation, i.e., inverse distance weighting (IDW), thin plate splines (TPS), and kriging on weekly water table depth (WTD) measurements from 80 observation wells in two cranberry farms (Farm A and Farm B) located in Québec, Canada. We use the leave-one-out cross-validation approach to assess the performance of the methods. Second, we evaluate the influence of the density of measurement points over the interpolation error for the cited methods. Third, we assess the performance of drainage systems and their impacts on crop productivity as a result of cumulative rainfall. Results along with practical considerations show that TPS is the best interpolator for WTD and this superiority is maintained and further demonstrated through a sensitivity analysis of the methods to spatial sampling density, i.e., partitioning the data into subsets of 25, 50, and 75% of the dataset. However, the random approach for selecting these subsets shows an unexpected result; that is, the interpolation methods exhibit a higher performance in terms of the Pearson correlation (r) for the 25% data subset at Farm B. Meanwhile, the cumulative precipitation over a three-day period, the maximum time required to return the soil matric potential to the optimal value after a major rainfall event, had a steady influence on WTD and thus crop productivity in the studied farms. This influence is more apparent for Farm A, but a rather random effect is noted for Farm B. This study presents a water-management-based strategy that mitigates the supplementary cost and effort for sensor deployment in water table monitoring for cranberry production. It is therefore of practical interest to cranberry growers and decision-makers who aim to maximize yields through water-management-oriented strategies.

It is increasingly recognized that water scarcity, rather than a lack of arable land, will be the major constraint to increase agricultural production over the next few decades. Therefore, water represents a unique agricultural asset to drive agricultural sustainability. However, its planning, management and usage are often influenced by a mix of interdependent economic, engineering, social, hydrologic, environmental, and even political factors. Such a complex interdependency suggests that a sociotechnical approach to water resources management, a subject of the field of Hydroinformatics, represents a viable path forward to achieve sustainable agriculture. Thus, this paper presents an overview of the intersection between hydroinformatics and agriculture to introduce a new research field called agricultural hydroinformatics. In addition, it proposes a general conceptual framework taking into account the distinctive features associated with the sociotechnical dimension of hydroinformatics when applied in agriculture. The framework is designed to serve as a stepping-stone to achieve, not only integrated water resources management, but also agricultural sustainability transitions in general. Using examples from agricultural water development to horticultural and livestock farming, the paper highlights facets of the framework applicability as a new paradigm on data flows/sources consideration, and information and simulation models engineering as well as integration for a holistic approach to water resources management in agriculture. Finally, it discusses opportunities and challenges associated with the implementation of agricultural hydroinformatics and the development of new research areas needed to achieve the full potential of this emerging framework. These areas include, for example, sensor deployment and development, signal processing, information modeling and storage, artificial intelligence, and new kind of simulation model development approaches.

Potato is one of the most cropped plants worldwide. Hundreds of different varieties are cultivated only in North America. Potato growers usually crop multiple varieties on their farms to answer the market demands for potato’s specific physical properties. However, few pieces of information are available regarding the optimal management of irrigation across potato varieties. Knowing that modern potatoes share genetics similarities, the optimal irrigation comfort zone for the potato crop might be the same for different groups of varieties. This study evaluates the effect of precision irrigation thresholds on the potato yields of three varieties (Envol: very early, Kalmia: early, and Red Maria: mid-late) with different maturity classes. In a greenhouse, a soil matric potential sensor network used in combination with a precise irrigation system allows the identification of a common optimal precision irrigation threshold, allowing optimal yields for the three varieties. This paper presents the first identification of an optimal irrigation threshold, −15 kPa, shared by different potato varieties. The optimal irrigation threshold identified in this study is not dependent on the maturity class, plant height or tuber potential production. The determination of an optimal precision irrigation threshold will allow potato growers to adapt their farm management processes to integrate more sustainable water management practices as they will be able to irrigate a field with multiple varieties with the same threshold.

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Soil disturbance resulting from tuber crop harvesting is a major threat to soil health. The depth of soil intervention is a critical factor that further strengthens the effects of such disturbance and makes harvest one of the most harmful cropping operations. In the case of potato, soil moisture is a determining factor for root and stolon development, hence, a deeper soil intervention may be required at harvest. While potato ranks as the fourth most cultivated crop worldwide, the impact of soil moisture on potato tuber vertical and horizontal distribution has received very little attention. The objective of this study was to evaluate the effects of four soil matric potential thresholds (SMPTs; –10, –20, –30, and –45 kPa) on the spatial (vertical and horizontal) distribution of potato tubers grown in plastic containers filled with sandy soil using an X-ray computed tomography scanner. The results of the experiments conducted in a greenhouse environment suggest that the horizontal distribution of the tubers did not differ significantly across the irrigation treatments. However, a linear relationship between SMPT, and therefore irrigation threshold, and potato tuber depth was observed. In addition, the deepest tuber position was observed under the –10 kPa SMPT, while the tubers were closer to the soil surface under the –45 kPa SMPT, which could lead to a greater preponderance of tuber diseases such as late blight or greening. Thus, potato irrigation events implementing a SMPT between –20 and –30 kPa could reduce the harvest depth, hence, decreasing the negative impacts of soil disturbance on soil structural stability and soil organic carbon degradation while mitigating the impacts of disease as well as reducing fuel costs, greenhouse gas emissions, soil loss and erosion.