Water Quality Summary, 2000-2010

Summary of water quality data from stream in Iowa from 2000 through 2010.

Water Quality Summary, 2010

Summary of water quality data collected from stream in 2010.

Water Quality Summary, 2000-2011

Summary of stream water quality data collected from 2000 through 2011.

Water Quality Summary, 2011

Summary of stream water quality data for 2011.

Lake Water Quality Summary, 2000-2011

Summary of lake water quality data collected from 2000 through 2011.

Lake Water Quality Summary, 2011

Summary of lake water quality data collected in 2011.

Water Quality Summary, 2000-2012

Summary of water quality data collected from streams in Iowa from 2000 through 2012.

Water Quality Summary, 2012

Summary of stream water quality data collected in 2012.

Stream Water Quality Summary, 2013

Summary of stream water quality collected in 2013.

Stream Water Quality Summary, 2000-2013

Summary of stream water quality data collected from 2000 through 2013.

Stream Water Quality Summary, 2014

Summary of stream water quality data collected in 2014.

Stream Water Quality Summary, 2000-2014

Summary of stream water quality data collected from 2000 through 2014.

Brazilian maize genotypes sensitivity to water deficit estimated through a simple crop yield model

The objective of this work was to determine the sensitivity of maize (Zea mays) genotypes to water deficit, using a simple agrometeorological crop yield model. Crop actual yield and agronomic data of 26 genotypes were obtained from the Maize National Assays carried out in ten locations, in four Brazilian states, from 1998 to 2006. Weather information for each experimental location and period were obtained from the closest weather station. Water deficit sensitivity index (Ky) was determined using the crop yield depletion model. Genotypes can be divided into two groups according to their resistance to water deficit. Normal resistance genotypes had Ky ranging from 0.4 to 0.5 in vegetative period, 1.4 to 1.5 in flowering, 0.3 to 0.6 in fruiting, and 0.1 to 0.3 in maturing period, whereas the higher resistance genotypes had lower values, respectively 0.2-0.4, 0.7-1.2, 0.2-0.4, and 0.1-0.2. The general Ky for the total growing season was 2.15 for sensitive genotypes and 1.56 for the resistant ones. Model performance was acceptable to evaluate crop actual yield, whose average errors estimated for each genotype ranged from -5.7% to +5.8%, and whose general mean absolute error was 960 kg ha-1 (10%).

Estimating water application efficiency for drip irrigation emitter patterns on banana

The objective of this work was to evaluate root and water distribution in irrigated banana (Musa sp.), in order to determine the water application efficiency for different drip irrigation emitter patterns. Three drip emitter patterns were studied: two 4-L h-1 emitters per plant (T1), four 4-L h-1 emitters per plant (T2), and five 4-L h-1 emitters per plant (T3). The emitters were placed in a lateral line. In the treatment T3, the emitters formed a continuous strip. The cultivated area used was planted with banana cultivar BRS Tropical, with a 3-m spacing between rows and a 2.5-m spacing between plants. Soil moisture and root length data were collected during the first production cycle at five radial distances and depths, in a 0.20x0.20 m vertical grid. The experiment was carried out in a sandy clay loam Xanthic Hapludox. Soil moisture data were collected every 10 min for a period of five days using TDR probes. Water application efficiency was of 83, 88 and 92% for the systems with two, four and five emitters per plant, respectively. It was verified that an increase in the number of emitters in the lateral line promoted better root distribution, higher water extraction, and less deep percolation losses.

Water Rights and Allocation, January 2010

Iowa Department of Natural Resources commitment to improving the management of both the quantity and quality of water resources, a committee was formed to assess the current policies and practices regarding water rights and allocation, and to make recommendations that would assist the state in moving toward a sustainable future. Water allocation concerns have been raised again in the past few years as increases in the demand for water are projected due to ethanol production, geothermal heating-cooling, and potential irrigation expansion.