Daniel K. Fisher

Responses of nitrogen metabolism and seed nutrition to drought stress in soybean genotypes differing in slow-wilting phenotype

Recent advances in soybean breeding have resulted in genotypes that express the slow-wilting phenotype (trait) under drought stress conditions. The physiological mechanisms of this trait remain unknown due to the complexity of trait × environment interactions. The objective of this research was to investigate nitrogen metabolism and leaf and seed nutrients composition of the slow-wilting soybean genotypes under drought stress conditions. A repeated greenhouse experiment was conducted using check genotypes: NC-Roy (fast wilting), Boggs (intermediate in wilting); and NTCPR94-5157 and N04-9646 (slow-wilting, SLW) genotypes. Plants were either well-watered or drought stressed. Results showed that under well-watered conditions, nitrogen fixation (NF), nitrogen assimilation (NA), and leaf and seed composition differed between genotypes. Under drought stress, NF and NA were higher in NTCPR94-5157 and N04-9646 than in NC-Roy and Boggs. Under severe water stress, however, NA was low in all genotypes. Leaf water potential was significantly lower in checks (−2.00 MPa) than in the SLW genotypes (−1.68 MPa). Leaf and seed concentrations of K, P, Ca, Cu, Na, B were higher in SLW genotypes than in the checks under drought stress conditions. Seed protein, oleic acid, and sugars were higher in SLW genotypes, and oil, linoleic and linolenic acids were lower in SLW genotypes. This research demonstrated that K, P, Ca, Cu, Na, and B may be involved in SLW trait by maintaining homeostasis and osmotic regulation. Maintaining higher leaf water potential in NTCPR94-5157 and N04-9646 under drought stress could be a possible water conservation mechanism to maintain leaf turgor pressure. The increase in osmoregulators such as minerals, raffinose, and stachyose, and oleic acid could be beneficial for soybean breeders in selecting for drought stress tolerance.

Trend Analysis and Forecast of Precipitation, Reference Evapotranspiration, and Rainfall Deficit in the Blackland Prairie of Eastern Mississippi

AbstractTrend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ETo, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ETo, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894–2014) were analyzed for annual, seasonal, and monthly periods. The analysis showed historical average annual rainfall, ETo, and dryness index (DI) in the location to be 1307 mm, 1210 mm, and 0.97, respectively. Monthly rainfall and ETo ranged from 72 to 118 mm and from 94 to 146 mm, respectively, between May and October, resulting in a monthly rain deficit from 22 to 62 mm. Annual rainfall showed an increasing trend of 1.17 mm yr−1 while annual ETo exhibited a decreasing trend of −0.51 mm yr−1, resulting in an annual DI reduction of 0.001 per year. Seasonal trends were found for rainfall in autumn (1.06 mm yr−1), ETo in summer (−0.29 mm yr−1) and autumn (−0.18 mm yr−1), and DI in autumn (−0.006). ...

Effects of Row-Type, Row-Spacing, Seeding Rate, Soil-Type, and Cultivar Differences on Soybean Seed Nutrition under US Mississippi Delta Conditions

The new Early Soybean Production System (ESPS), developed in the Midsouth USA, including the Mississippi delta, resulted in higher yield under irrigated and non-irrigated conditions. However, information on the effects of the agricultural practices such as row-type (RT: twin- vs. single-row), row-spacing, (RS), seeding rate (SR), soil-type (ST) on seed nutrition under the ESPS environment in the Mississippi delta is very limited. Our previous research in the Mississippi delta showed these agricultural practices altered seed nutrients in one cultivar only. However, whether these effects on seed nutrients will be exhibited by other soybean cultivars with earlier and later maturities across multiple years are not yet known. Therefore, the objective of this research was to evaluate the effects of agricultural practices and cultivar (Cv) differences on seed nutrition in clay and sandy soils under ESPS environment of high heat and drought. Two field experiments were conducted; one experiment was conducted in 2009 and 2010, and the other in 2008, 2009, and 2010 under irrigated conditions. Soybean were grown on 102 cm single-rows and on 25 cm twin-rows with 102 cm centers at seeding rates of 20, 30, 40, and 50 seeds m–2. Two soybean cultivars (94M80 with earlier maturity; and GP 533 with later maturity) were used. Results showed that increasing seeding rate resulted in increases of protein, sucrose, glucose, raffinose, B, and P concentrations on both single- and twin-rows. However, this increase became either constant or declined at the higher rates (40 and 50 seeds m–2). Protein and linolenic acid concentrations were higher in GP 533 than in 94M80 on both row-types, but oil and oleic acid concentrations were in 94M80 than GP 533. Generally, cultivar GP 533 accumulated more seed constituents in seeds than 94M80. In 2010, there were no clear responses of seed nutrients to SR increase in both cultivars, perhaps due to drier year and high heat in 2010. It is concluded that RT and SR can alter seed nutrition under clay and sandy soils, especially under high heat and drought conditions as in the Mississippi delta.

Aerial Multispectral Imaging for Cotton Yield Estimation under Different Irrigation and Nitrogen Treatments

Cotton yield varies spatially within a field. The variability can be caused by various production inputs such as soil properties, water management, and fertilizer application. Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on yield qualitatively and quantitatively in a timely and cost-effective fashion. A 10-ha cotton field with irrigation and non-irrigation 2x2 blocks was used in this study. Six nitrogen application treatments were randomized with 2 replications within each block. As plant canopy was closed, airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera. The images were processed to generate various vegetation indices. The vegetation indices were evaluated for best performance to characterize yield. The effect of irrigation on vegetation indices was significant. Models for yield estimation were developed and verified by comparing the estimated and actual yields. Results indicated that ratio of vegetation index (RVI) had a close relationship with yield (R2=0.47). Better yield estimation could be obtained using a model with RVI and soil EC (electrical conductivity) measurements of the field as explanatory variables (R2=0.53). This research demonstrates the capability of aerial multispectral remote sensing in estimating cotton yield variation, and providing methods for similar studies.

Assessment of Cotton Plant Nitrogen Status and Yield Potential Using Plant Height Mapping System

Plant nitrogen status and yield potential are important factors for optimizing field management in cotton production. An experiment was conducted in 2011 at Stoneville, Mississippi to develop a method to assess plant N status and yield potential in cotton. The experimental plots were laid out in a split plot design with two irrigation treatments as main unit and six nitrogen treatments as subunit in a 10-ha cotton field. Cotton plant height was measured in real time in situ using an experimental ultrasonic device coupled with a GPS (global positioning system) receiver. Soil samples were collected and analyzed for soil-N residual and soil texture before planting. Cotton leaf-blade samples were collected and analyzed for N content. Amount of water applied in irrigated treatment was recorded during the growing season. The plant height showed a quadratic relationship with yield, and this relationship was stronger in the non-irrigated plots (R2=0.60) than that in irrigated plots (R2=0.16). Irrigation had a significant effect on plant height, leaf N content, and yield. Excess application of N would not improve cotton yield.

Automated Data Collection Using Simple and Inexpensive Microcontrollers and Semiconductor Sensors

The features, capabilities, and potential uses of a class of low-voltage, multi-featured, easily programmed microcontrollers are presented. A number of semiconductor sensors and auxiliary components are discussed, and a listing of equipment and informational resources is given.