Phytoremediation efficacy of native vegetation for nutrients and heavy metals on soils amended with poultry litter and fertilizer.

Poultry litter on agricultural lands could introduce nitrogen (N), phosphorus (P), heavy metals in soil and ground water. Native vegetations were identified to assess efficacy for phytoremediation of nutrients and metals from soil and water. Objective was to measure capability of multi-year native species to remove metals, nutrients, and prevent Nitrate-N leaching below the rooting zone. Treatments were distributed in four replicates with/without fertilization. Suction lysimeters were installed at 30, 60, and 90-cm depths in 3 of 4 replicates. Species were identified, recorded, five specified cuttings sampled. Plant, soil, water samples were prepared and analyzed by spectroscopy. Nitrate-N extraction, nitrates in water samples were determined using flow injection analysis. Fertilized plots (NVM) had 39% more biomass yield than unfertilized plots (NVN). In plants, nutrient and metal concentrations varied significantly with 14% increase in Zn, 36% and 26% in K and Mg over NVN for first and second year. Uneven between NVM and NVN, topsoil had higher values for most nutrients and metals. Largest P and (NO3-)-N in plant and water were observed from NVM. Cultivation of native vegetation appears to be an effective approach for remediation of excess nitrates-N, P, heavy metals from surface and sub-surface zones of the soil.

Native vegetation has been used for soil fertility, specific reasons like the removal of pesticides or agrochemicals, and other chemical related exposures. Studies on the use of native vegetation for phytoremediation on agricultural lands are uncommon. This research looked at the capability of native vegetation of different species as a viable tool for the remo+val of excess nutrients and heavy metals from agricultural lands. Results indicated native vegetation can take up significant amounts of excess nutrients from soils, proportional to their biomass accumulation. Native Vegetation was therefore found to be a nutrient sink, capable of removing excess nutrients/metals from the soil.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 µmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Projected long-term climate trends reveal the critical role of vapor pressure deficit for soybean yields in the US Midwest.

Extreme climate events including heat waves and droughts are projected to become more frequent under future climate change conditions. However, the mechanisms between soybean yields and climate factors, specifically involving variable rainfall and high heat episodes, are still unclear, particularly with respect to spatial trends in the United States (US) Midwest. A recently modified version of the model GLYCIM was used to evaluate rainfed soybean production across 12 states at a 10 km spatial resolution for three time periods (2011-2020, 2051-2060, 2091-2099) under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5. Results showed that except for the northernmost Midwest counties, most of the current rainfed cropping system in the Midwest would suffer a 24.6-47.4 % yield loss without considering the CO2 fertility effect. Incorporating the effect of elevated CO2 showed a smaller yield loss of 11.6-29.5 %. The increased frequency of extreme degree days (EDD) or accumulation of hourly temperatures above 30 °C associated with increased vapor pressure deficit (VPD) played a key role in contributing to water deficits and resultant crop losses under these future climate conditions. Although a relatively weak relationship between summer rainfall and crop yield was observed, decreased rainfall caused VPD to increase which induced crop water deficits. These findings suggest that it is crucial to consider VPD along with high temperature and low rainfall trends simultaneously for development of potential management or breeding-based adaptative strategies for soybean.

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes.

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m-2 day-1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m-2 day-1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

Drought-Induced Responses in Maize under Different Vapor Pressure Deficit Conditions.

Water stress in plants depends on the soil water level and the evaporative demand. In this study, the physiological, biochemical, and molecular response of maize were examined under three evaporative demand conditions (low­1.00 kPa, medium­2.2 kPa, and high­4.00 kPa Vapor pressure deficit (VPD)) at three different soil water content (SWC); well-watered, 45%, and 35% SWC. Plants grown at 35% SWC under high VPD had significant (p < 0.01) lower leaf weight, leaf area, and leaf number than low VPD. Plants under low, medium, and high VPD with drought stress (45% and 35% SWC) showed a 30 to 60% reduction in their leaf area compared to well-watered plants. Gas exchange parameters including photosynthesis, stomatal conductance, and water use efficiency exhibited significant differences (p < 0.01) between treatments, with the highest reduction occuring at 35% SWC and high VPD. Both drought and VPD significantly (p < 0.01) increased C4 enzyme levels and some transcription factors with increased stress levels. Transcription factors primarily related to Abssisic Acid (ABA) synthesis were upregulated under drought, which might be related to high ABA levels. In summary, severe drought levels coupled with high VPD had shown a significant decrease in plant development by modifying enzymes, ABA, and transcription factors.

Impact of water stress under ambient and elevated carbon dioxide across three temperature regimes on soybean canopy gas exchange and productivity.

The present study investigated the interactive effects of three environmental stress factors elevated CO2, temperature, and drought stress on soybean growth and yield. Experiments were conducted in the sunlit, controlled environment Soil-Plant-Atmosphere-Research chambers under two-level of irrigation (WW-well water and WS-water stress-35%WW) and CO2 (aCO2-ambient 400 µmol mol-1 and eCO2-elevated 800 µmol mol-1) and each at the three day/night temperature regimes of 24/18 °C (MLT-moderately low), 28/22 °C (OT-optimum), and 32/26 °C (MHT-moderately high). Results showed the greatest negative impact of WS on plant traits such as canopy photosynthesis (PCnet), total dry weight (TDwt), and seed yield. The decreases in these traits under WS ranged between 40 and 70% averaged across temperature regimes with a greater detrimental impact in plants grown under aCO2 than eCO2. The MHT had an increased PCnet, TDwt, and seed yield primarily under eCO2, with a greater increase under WW than WS conditions. The eCO2 stimulated PCnet, TDwt, and seed yield more under WS than WW. For instance, on average across T regimes, eCO2 stimulated around 25% and 90% dry mass under WW and WS, respectively, relative to aCO2. Overall, eCO2 appears to benefit soybean productivity, at least partially, under WS and the moderately warmer temperature of this study.

Runoff losses of suspended sediment, nitrogen, and phosphorus from a small watershed in Korea.

Nutrients and sediments in runoff lead to the degradation of water quality of lakes and streams. The development of schemes to mitigate such degradation requires a characterization of the underlying transport processes. The objectives of this study were to develop annual and seasonal load-discharge relationships for suspended sediment (SS), total nitrogen (TN), and total phosphorus (TP) losses from a small mixed land use watershed and to use these relationships to explicate the annual and monthly patterns of losses of these species. Data from 1996 to 2004 were used to develop load-discharge relationships for SS, TN, and TP at the HP#6 watershed, a subwatershed of the Balhan reservoir watershed located in Bongdam-myun and Paltan-myun, Gyeonggi-do, Korea. Standard least squares curve fitting and S-estimation procedures were used to fit power functions to the data collected over this time period. The fitted load-discharge relationships are indicative of seasonal variations in SS and TN and of TP losses from HP#6. The exponents of the fitted power functions for TN and TP in the fall, for TP in summer season, and for SS in all seasons are >1, indicating that the concentrations of these species increase as flow rate increases. Most of the SS, TN, and TP transported in runoff left the watershed between April and September; thus, cost-efficient strategies can be established by focusing on this period. Further study of the seasonal variations is required for a better characterization of seasonal losses of SS, TN, and TP in runoff from the HP#6 watershed.

Coupled Gas-Exchange Model for C4 Leaves Comparing Stomatal Conductance Models.

Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball-Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.

Phosphorus Nutrition Affects Temperature Response of Soybean Growth and Canopy Photosynthesis.

In nature, crops such as soybean are concurrently exposed to temperature (T) stress and phosphorus (P) deficiency. However, there is a lack of reports regarding soybean response to T × P interaction. To fill in this knowledge-gap, soybean was grown at four daily mean T of 22, 26, 30, and 34°C (moderately low, optimum, moderately high, and high temperature, respectively) each under sufficient (0.5 mM) and deficient (0.08 mM) P nutrition for the entire season. Phosphorus deficiency exacerbated the low temperature stress, with further restrictions on growth and net photosynthesis. For P deficient soybean at above optimum temperature (OT) regimes, growth, and photosynthesis was maintained at levels close to those of P sufficient plants, despite a lower tissue P concentration. P deficiency consistently decreased plant tissue P concentration ≈55% across temperatures while increasing intrinsic P utilization efficiency of canopy photosynthesis up to 147%, indicating a better utilization of tissue P. Warmer than OTs delayed the time to anthesis by 8-14 days and pod development similarly across P levels. However, biomass partitioning to pods was greater under P deficiency. There were significant T × P interactions for traits such as plant growth rates, total leaf area, biomass partitioning, and dry matter production, which resulted a distinct T response of soybean growth between sufficient and deficient P nutrition. Under sufficient P level, both lower and higher than optimum T tended to decrease total dry matter production and canopy photosynthesis. However, under P-deficient condition, this decrease was primarily observed at the low T. Thus, warmer than optimum T of this study appeared to compensate for decreases in soybean canopy photosynthesis and dry matter accumulation resulting from P deficiency. However, warmer than OT appeared to adversely affect reproductive structures, such as pod development, across P fertilization. This occurred despite adaptations, especially the increased P utilization efficiency and biomass partitioning to pods, shown by soybean under P deficiency.

Random Forests for Global and Regional Crop Yield Predictions.

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Responses of growth and primary metabolism of water-stressed barley roots to rehydration.

Barley seedlings were grown in pots in controlled environment chambers and progressive drought treatments were imposed 11 d after sowing. Soil water content decreased from 92 to 10% following 14 d without watering. Increases of biomass in shoots and roots slowed after 4 and 9 d of water stress, respectively. Thirty barley root metabolites were monitored in this study and 85% were significantly altered by drought. Sucrose, raffinose, glucose, fructose, maltose, malate, asparagine and proline increased and myo-inositol, glycerate, alanine, serine, glycine and glutamate decreased during drought. Primary metabolism was likely involved in various crucial processes during water stress including, osmotic adjustment, nitrogen sequestration and ammonia detoxification. Rates of photosynthesis and stomatal conductance recovered in 2 d and shoot growth commenced the 3rd day after rehydration. Root growth also exhibited a lag after rehydration but this was attributed to high nutrient concentrations during water stress. Malate and proline recovered within 1 d but serine was only partially reversed 6 d after rehydration. Malate, aspartate and raffinose decreased below well-watered, control levels following rehydration. Variation in the magnitude and time necessary for individual compounds to fully recover after rehydration suggested the complexity of metabolic processes initiated by re-watering.