Research Project: Improving the Sustainability of Irrigated Farming Systems in Semi-Arid Regions

Location: Water Management and Systems Research

2020 Annual Report

Objectives
1. Improve water use efficiency (WUE) by identifying plant traits, mechanisms, and agronomic practices that increase productivity per unit of water used by the crop. 2. Develop simple and accurate methods to quantify evapotranspiration (ET) in agricultural systems under limited water availability to improve the efficiency of irrigation scheduling. 3. Create Water Production Functions (WPF, yield per ET) for alternative crops under limited water availability.

Approach
Increased productivity of cropping systems as well as yield stability is vital to meet the challenge of expanding human populations and increased needs for food and fiber. Effective management of cropping systems and irrigation water will depend on our ability to maximize crop water productivity (yield per unit water used by the crop). This, in turn, requires a better understanding and evaluation of complex plant traits, better management of interacting agricultural inputs, and better tools to more efficiently manage agricultural water supplies, especially in the face of greater competition and less water availability. Finally, there is increased efficiency at the farm scale that can be realized with better farm-scale decision making. The overarching goal of this research is to improve the sustainability of irrigated farming systems for agronomic producers in semi-arid and arid regions. These producers vary both in control over the timing and amount of irrigation, and in methods of irrigation; thus multi-faceted solutions are required. Solutions are in three parts: 1) increasing the knowledge base of plant traits, mechanisms and agronomic practices related to crop productivity under limited water; 2) developing tools to assist with real-time decision making for irrigation management; and 3) developing information and tools for farm-scale decision-making regarding crop selection, land area partitioning among crops, and within-farm irrigation distribution. This research will lead to better understanding of crop physiology needed to improve germplasm, increased productivity of cropping systems, and improved irrigation management.

Progress Report
Objective 1a. Data collected in fiscal year 2019, combined with previous years, were used to build conceptual and empirical trait network models to better understand how crop traits interact to confer improved performance under water limiting conditions. The scientific community’s theoretical understanding of crop physiology is improving from this work, which was the impetus for replacing the Nested Association Mapping greenhouse study with a different greenhouse study in fiscal year 2019. As such, the greenhouse data collected in fiscal year 2019 focused on understanding the physiological determinants of stomatal conductance in maize. This past year, these data have been successfully collected and summarized and are being used to parameterize and improve two process-based crop growth models that are already allowing us to evaluate different crop trait combinations. This strategic inclusion of physiologically-based modeling has resulted in meaningful progress towards Identifying plant trait combinations that confer productivity under water stress (i.e., Sub-Objective 1A) and has forged valuable collaborations among the ARS, France’s National Research Institute for Agriculture, Food and Environment (INRAe), the University of Tasmania, and the University of Buffalo. Furthermore, data from the experiment in the “north small plot field” have been successfully collected and are being used by ARS scientists to develop better conceptual models of how xylem tissues (water transporting tissues) develop in maize stems, which is a process related to drought tolerance in this species, i.e. progress towards completing Sub-Objective 1A. Objective 1b. We quantified the interactions between water and nitrogen from several experiments (field and greenhouse) to determine the physiological mechanisms underpinning these interactions, measured the effects on crop growth and productivity, and assessed the movement of nitrogen under different levels of water availability. Examining the effects of deficit irrigation from multiple cumulative years of treatment on soil structure and microbial functional groups, we found that deficit irrigation promoted greater maize root growth, and, although microbial biomass was reduced, shifted microbial communities to more drought tolerant groups. This shift in microbial community structure has the potential to impact soil organic carbon and nitrogen mineralization beyond the relatively short timeframe that treatments were in effect. Nearly completed analysis of two years of nitrogen emissions data suggests that full irrigation (rather than deficit) is a more climate-smart practice in terms of several production efficiency metrics, as well as input use efficiency metrics. Data from a field experiment is currently under analysis to explore pathways of nitrogen movement under varying water availability. The first phase of a greenhouse experiment is under analysis to explore microbial community shifts associated with lower stomatal closure point in maize. The identification of microbial groups associated with lower stomatal closure point from this greenhouse experiment will be used for screening mechanisms that underpin the interacting effects between water and nitrogen on maize growth and productivity in next year’s field experiment. Objective 2. Data collected in fiscal year 2019, along with preliminary data collected in fiscal year 2018, have shown promise for new techniques that use canopy temperature to indicate not only water stress, but also quantify reductions in water use while crops are under water stress. Although infrared temperature sensors and cameras are readily available, as are variable rate irrigation (VRI) systems, the decision support systems and related algorithms are a missing link. Our data suggest that, by obtaining canopy temperature and canopy cover paired with nearby micrometeorological stations, an estimate of crop water use (i.e. actual evapotranspiration) can be made, which can inform irrigation management decisions in real-time. By linking these techniques with our unmanned aerial vehicle (UAV) systems, this concept is scalable to the field, where spatially variable data can inform VRI systems. Objective 3. A consolidated water production function (WPF) dataset for sorghum, including data collected in fiscal years 2017-2019, was used to enhance the Unified Plant Growth Model (UPGM) module of the Agricultural Ecosystem Services (AgES) model to explore the suitability of grain sorghum for cropping systems across the High Plains. This modeling effort is in initial stages but has already resulted in improved representation of water-stress effects on crop phenology in AgES/UPGM, provided planting-date recommendations for sorghum studies, and offers an additional opportunity to advance the capacity of the AgES/UPGM model as a decision-making tool for cropping systems under limited water availability.

Accomplishments
1. Improved monitoring of spatially variable crop water stress with remote-sensing. While many crops are currently managed uniformly within fields (e.g., same plant spacing, nutrient rates, and irrigation amounts), precision agriculture can optimize management across landscapes and on a spatial and temporal basis. However, many challenges to precision management remain such as understanding the meaning and nuance of the spatial and temporal variability and how this variability should influence management decisions. ARS scientists in Fort Collins, Colorado, demonstrated: a) how maize canopy temperature, which increases due to water stress, is related not only to crop water status but more closely to the interaction of water availability and soil characteristics; b) how a remotely-sensed soil salinity related vegetation index enhanced crop yield prediction for water stressed maize during reproductive and maturation stages; and 3) how the integration of high-resolution thermal and Red-Green-Blue images taken by unmanned aerial systems provides accurate maps of maize canopy temperature spatial variability. These advancements have demonstrated the benefits of collecting spatial data, as well as how these data can be used to improve irrigation management. Additionally, this body of work provides the scientific foundation for enhanced remote sensing-based variable rate irrigation technologies within the agricultural sector.

2. Identified novel avenues for crop improvement via quantitative plant trait networks. Developing crop plants that perform better under water limited conditions is an international priority for agriculture. Towards this goal, ARS scientists in Fort Collins, Colorado, developed an important and novel understanding of how plant traits and the physiological connections between these traits (trait networks) could potentially be manipulated via gene editing and plant breeding to improve productivity. Specifically, non-conventional crop traits (water transport capacity, maximal stomatal conductance, and xylem “safety”) are key to improving crop yield under drought. This research established the importance of explicitly considering these traits in research models and evaluating them simultaneously with other connected traits (e.g., root system traits, stomatal conductance, and photosynthesis) when considering improvements to crop plants. This paradigm shift may ultimately lead to societal benefits that come from crop improvements through gene editing and genomic-assisted breeding. This research demonstrates that this novel approach has achieved a key milestone of acceptance within the international scientific community working towards crop improvement, as evidenced by invitations of ARS scientists to present two invited talks at Harvard University, a Keynote presentation at the Xylem International meeting in Padua, Italy, and participation in working groups at the University of Montpellier, France, and the University of Helsinki, Finland.

Review Publications
He, P., Gleason, S.M., Wright, I., Weng, E., Liu, H., Zhu, S., Lu, M., Luo, Q., Li, R., Wu, G., Yan, E., Song, Y., Mi, X., Hao, G., Reich, P.B., Wang, Y., Ellsworth, D.S., Ye, Q. 2019. Growing-season temperature and precipitation are independent drivers of global variation in xylem hydraulic conductivity. Global Change Biology. 00:1-9. https://doi.org/10.1111/gcb.14929.
Zhang, D., Chen, G., Zhang, H., Gu, C., Wang, Q., Chen, Y. 2020. Comprehensive analysis of fusarium head blight in wheat kernel using hyperspectral spectrum and image. Spectrochimica Acta. 236. https://doi.org/10.1016/j.saa.2020.118344.
Zhang, D., Fang, S., She, B., Zhang, H., Jin Ning, Xia, H., Yang, Y., Ding, Y. 2019. Winter wheat mapping based on Sentinel-2 data in heterogeneous planting conditions. Remote Sensing. 11(22):2647. https://doi.org/10.3390/rs11222647.
Zhang, H., Yemoto, K.K. 2019. Unmanned aerial system-based remote sensing applications at the Northern Colorado Limited Irrigation Research Farm. International Journal of Precision Agricultural Aviation (IJPAA). 2(2):1–10. https://doi.org//10.33440/j.ijpaa.20190202.50.
Zhang, L., Niu, Y., Zhang, H., Han, W., Li, G., Tang, J., Peng, X. 2019. Extracting maize canopy temperature based on unmanned aerial vehicle thermal and RGB imagery and its application to water stress monitoring. Frontiers in Plant Science. 10:1270. https://doi.org/10.3389/fpls.2019.01270.
DeJonge, K.C., Thorp, K.R., Marek, G.W. 2020. The apples and oranges of reference and potential evapotranspiration: Implications for agroecosystem models. Agricultural and Environmental Letters. 5(1). https://doi.org/10.1002/ael2.20011.
Ale, S., Harmel, R.D., Nejadhashemi, P.A., DeJonge, K.C., Irmak, S., Chaubey, I., Douglas-Mankin, K.R. 2020. Global water security: Current research and priorities for action. Transactions of the ASABE. https://doi.org/10.13031/trans.13839.
DeJonge, K.C., Zhang, H., Taghvaeian, S., Trout, T.J. 2020. Canopy temperature bias from soil variability enhanced at high temperatures. Transactions of the ASABE. https://doi.org/10.13031/trans.13554.
Niu, Y., Zhang, L., Zhang, H., Han, W., Peng, X. 2019. Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery. Remote Sensing Reviews. 11(11):1261. https://doi.org/10.3390/rs11111261.
Costa Filho, E., Chavez, J.L., Comas, L.H. 2020. Determining maize water stress through a remote sensing based surface energy balance approach. Irrigation Science. https://doi.org/10.1007/s00271-020-00668-1.
Kattge, J., Bonisch, G., Diaz, S., Lavorel, S., Prentice, I.C., Leadley, P., Tautenhahn, S., Werner, G., Gillison, A., Wirth, C., Gleason, S.M., Blumenthal, D.M. 2020. TRY plant trait database - enhanced coverage and open access. Global Change Biology. 26:119-188. https://doi.org/10.1111/gcb.14904.
Zhang, H., Han, M., Comas, L.H., DeJonge, K.C., Gleason, S.M., Trout, T.J., Ma, L. 2019. Response of maize yield components to growth stage-based deficit irrigation. Agronomy Journal. 111:14-9. https://doi.org/10.2134/agronj2019.03.0214.
Liu, X., Liu, H., Gleason, S.M., Zhu, S., He, P., Hou, H., Li, R., Ye, Q. 2019. Water transport from stem to stomata: The coordination of hydraulic and gas exchange traits across 33 subtropical woody species. Tree Physiology. 39(10):1665-1674. https://doi.org/10.1093/treephys/tpz076.