Evapotranspiration adjustment for irrigated maize-soybean rotation systems in Nebraska, USA.

Irrigation water requirements are commonly estimated based on the estimated crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETr) and crop coefficient (Kc). Recent studies show that, at high evaporative demand (high ETr), Kc tends to decrease, creating an inverse ETr-Kc relationship. The focus of this long-term study is to, if at high atmosphere demand, there is the same inverse ETr-Kc relationship in Nebraska, USA, one of the most intensely irrigated regions in the world, and as a result, propose an adjustment to the Kc-ETr approach. The study was carried out in eastern Nebraska for maize-soybean rotation fields for the period 2002-2012. The Kc was estimated based on energy balance data from eddy covariance flux towers installed in the field and a nearby automated weather station throughout the growing seasons. We found that average Kc values varied depending on the year under high ETr; measured ETc agreed reasonably well with the FAO-56 manual predicted values, but in years with high ETr such as 2012 and 2002 affecting ETc values over the growing season. It was observed that Kc decreased as ETr increased, mainly when ETr reaches values greater than 6 mm d-1 (P values < 0.001). This most likely was due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high atmospheric demands. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETr to improve the required irrigation depth and irrigation management.

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize.

Agriculture is considered a hotspot for wireless sensor network (WSN) facilities as they could potentially contribute towards improving on-farm management and food crop yields. This study proposes six designs of unmanned aerial system (UAS)-enabled data ferries with the intent of communicating with stationary sensor node stations in maize. Based on selection criteria and constraints, a proposed UAS data ferrying design was shortlisted from which a field experiment was conducted for two growing seasons to investigate the adoptability of the selected design along with an established WSN system. A data ferry platform comprised of a transceiver radio, a mini-laptop, and a battery was constructed and mounted on the UAS. Real-time monitoring of soil and temperature parameters was enabled through the node stations with data retrieved by the UAS data ferrying. The design was validated by establishing communication at different heights (31 m, 61 m, and 122 m) and lateral distances (0 m, 38 m, and 76 m) from the node stations. The communication success rate (CSR) was higher at a height of 31 m and within a lateral distance of 38 m from the node station. Lower communication was accredited to potential interference from the maize canopy and water losses from the maize canopy.

Water productivity in meat and milk production in the US from 1960 to 2016.

Global demand for livestock products is rising, resulting in a growing demand for feed and potentially burdening freshwater resources to produce this feed. To offset this increased pressure on water resources, the environmental performance of livestock sector should continue to improve. Over the last few decades, product output per animal and feedstuff yields in the US have improved, but before now it was unclear to what extent these improvements influenced the water productivity (WP) of the livestock products. In this research, we estimate changes in WP of animal products from 1960 to 2016. We consider feed conversion ratios (dry matter intake per head divided by product output per head), feed composition per animal category, and estimated the water footprint of livestock production following the Water Footprint Network's Water Footprint Assessment methodology. The current WP of all livestock products appears to be much better than in 1960. The observed improvements in WPs are due to a number of factors, including increases in livestock productivity, feed conversion ratios and feed crop yields, the latter one reducing the water footprint of feed inputs. Monogastric animals (poultry and swine) have a high feed-use efficiency compared to ruminants (cattle), but ruminants consume relatively large portion of feed that is non-edible for humans. Per unit of energy content, milk has the largest WP followed by chicken and pork. Per gram of protein, poultry products (chicken meat, egg and turkey meat) have the largest WP, followed by cattle milk and pork. Beef has the smallest WP. These data provide important information that may aid the development of strategies to improve WP of the livestock sector.

Variable Rate Irrigation of Maize and Soybean in West-Central Nebraska Under Full and Deficit Irrigation.

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R 2 < 0.4); however, they correlated better with modeled ET in 2018 (R 2 = 0. 69, VRI-Full; R 2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient.

Water, Energy, and Carbon Footprints of Bioethanol from the U.S. and Brazil.

Driven by biofuel policies, which aim to reduce greenhouse gas (GHG) emissions and increase domestic energy supply, global production and consumption of bioethanol have doubled between 2007 and 2016, with rapid growth in corn-based bioethanol in the U.S. and sugar cane-based bioethanol in Brazil. Advances in crop yields, energy use efficiency in fertilizer production, biomass-to-ethanol conversion rates, and energy efficiency in ethanol production have improved the energy balance and GHG emission reduction potential of bioethanol. In the current study, the water, energy, and carbon footprints of bioethanol from corn in the U.S. and sugar cane in Brazil were assessed. The results show that U.S. corn bioethanol has a smaller water footprint (541 L water/L bioethanol) than Brazilian sugar cane bioethanol (1115 L water/L bioethanol). Brazilian sugar cane bioethanol has, however, a better energy balance (17.7 MJ/L bioethanol) and smaller carbon footprint (38.5 g CO2e/MJ) than U.S. bioethanol, which has an energy balance of 11.2 MJ/L bioethanol and carbon footprint of 44.9 g CO2e/MJ. The results show regional differences in the three footprints and highlight the need to take these differences into consideration to understand the implications of biofuel production for local water resources, net energy production, and climate change mitigation.