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Abstract Crop yields are adversely affected by nitrogen (N) losses in humid subtropical regions. This study was conducted to determine whether N dynamics could be manipulated through planting geometry and fertilizer placement. The effects of irrigation (irrigated and rainfed), row pattern (single‐ and twin‐row), and N placement (surface dribble, one knife, two knives, and control) on corn (Zea mays L.) productivity and N use efficiency were investigated at Leland, Mississippi, on a Bosket very fine sandy loam. The total N rate was split into two equal halves of 128 kg N ha−1 at V2 and V6 growth stages. The effects of row pattern and N placement were consistent between irrigated and rainfed environments. There was no interaction between row pattern and N placement nor did row pattern affect corn productivity or N use efficiency parameters. In 2020, when less than 30 mm of rainfall occurred from the first N application through 2 weeks after the last N application, corn grain yield and agronomic N efficiency were not different among placement methods and averaged 11.1 Mg ha−1 and 20.3 kg grain kg−1 fertilizer N, respectively. However, in 2021, applying N with one knife increased corn grain yield by 7% to 14% compared to the two knives and surface dribble application methods; in that year, 235 mm of rainfall occurred from the first N application through 2 weeks after the last N application. Applying N with one knife appears to consistently improve crop productivity and N use efficiency parameters for both irrigated and rainfed environments.

The Yazoo–Mississippi Delta is one of the regions within the Lower Mississippi River Basin where substantial irrigation development and consequent groundwater depletion have occurred over the past three decades. To describe this irrigation development, a study was conducted to analyze existing geospatial datasets and to synthesize the results with those of past government surveys. The effort produced a quantitative review characterizing three aspects of irrigation development from 1991 to 2020. First, the expansion of irrigated area was tracked in terms of absolute area and in terms of fraction relative to total land or cropland area. Second, trends in irrigated land cover were traced in terms of irrigated crop mix, irrigated fractions of main crops, and comparisons with non-irrigated land. Third, changes in irrigation systems were examined in terms of water sources, energy sources, and application methods. Original findings of this study for the end of 2020 included moderate positive spatial autocorrelation in the density of irrigated areas; a higher irrigated crop preference for soybean and rice over cotton and corn in highly hydric soils; and 91% and 3% of permitted areas studied being respectively under groundwater withdrawal permits exclusively and under surface water diversion permits exclusively. By compiling such information, this paper can serve as a convenient reference on the recent history and status of irrigation development in the Yazoo–Mississippi Delta.

Much of the research on irrigation scheduling sensors, especially soil water sensors, assesses and refines the accuracy of sensor calibrations. However, a sensor with an accurate calibration but high variability among replicates may require a larger-than-acceptable number of replicates for informing recommendations of optimal irrigation timing. To compare the interreplicate variability of sensors across types and calibration accuracy levels, this study presented eight metrics: (1) absolute spread-to-change ratio, (2) shifted spread-to-change ratio, (3) coefficient of change variation, (4) standard deviation of relative value, (5) standard deviation of relative change, (6) standard deviation of absolute triggering date, (7) standard deviation of shifted triggering date, and (8) standard deviation of relative triggering date. These metrics enabled comparisons either by nondimensionalizing sensor measurements or by expressing interreplicate variability in terms of time. For demonstrating their usage and their particularities, the metrics were applied to two datasets that included soil water sensor types such as neutron probe (503DR), dielectric sensor (TDR-315, CS616, CS655, HydraProbe II, 5TE, TEROS 12, Drill & Drop), and granular matrix sensor (Watermark 200SS). The neutron probe in the single-depth dataset and the granular matrix sensor in the multi-depth dataset generally displayed less interreplicate variability than other evaluated sensor types over multiple drying cycles. Future research is suggested to calculate and improve the eight metrics for identifying combinations of sensor types, deployment methods, and data interpretation techniques that minimize interreplicate variability and maximize irrigation scheduling precision. [ABSTRACT FROM AUTHOR]