Interaction of different irrigation strategies and soil textures on the nitrogen uptake of field grown potatoes.

Abstract Nitrogen (N) uptake (kg ha -1 ) of field-grown potatoes was measured in 4.32 m 2 lysimeters that were filled with coarse sand, loamy sand, and sandy loam and subjected to full (FI), deficit (DI), and partial root-zone drying (PRD) irrigation strategies. PRD and DI as water-saving irrigation treatments received 65% of FI after tuber bulking and lasted for six weeks until final harvest. Results showed that the irrigation treatments were not significantly different in terms of N uptake in the tubers, shoot, and whole crop. However, there was a statistical difference between the soil textures where plants in the loamy sand had the highest amount of N uptake. The interaction between irrigation treatments and soil textures was significant, and implied that under non-limiting water conditions, loamy sand is the suitable soil for potato production because plants can take up sufficient amounts of N and it could potentially lead to higher yield. However, under limited water conditions and applying water-saving irrigation strategies, sandy loam and coarse sand are better growth media because N is more available for the potatoes. The simple yield prediction model was developed that could explains ca. 96% of the variations of fresh tuber yield based on the plant evapotranspiration (ET) and N uptake in the tuber or whole crop

Assessing nitrogen mineralization from soil organic matter by routine procedures

Careful management of N in agriculture is important for plant and environment since both N deficiency and surplus of mineral N after crop harvest need to be avoided. As mineralization of N from soil organic matter remains an important source for plant uptake, reliable prediction of N from this pool is crucial to meet nutrient availability and crop needs. This study was developed working on nine dissimilar agricultural soils collected across Europe, in Italy, Poland, Denmark, Portugal, Slovakia, Czech Republic. Aerobic incubation was performed in triplicate over 24 weeks at 25\ub0C. Net N mineralization was determined each two weeks. After 3 weeks of pre-incubation, cumulative values of mineral N showed a linear trend over the 24 weeks period. The relationship between N mineralization rate and organic C of the soil, representing the quantity of the organic pool, was poor, especially due to the three soils whose mineralization rates were much lower than the other soils. For the nine dissimilar soils, the net mineralization was very well explained (r2=0.98) by 2 parameters that can be measured in a laboratory routine basis: the organic N content (quantity of the pool) and the C/N ratio of the organic matter (quality of the pool)

Comparison of the performance of net radiation calculation models

The original publication is available at: www.springerlink.comDaily values of net radiation are used in many applications of crop-growth modeling and agricultural water management. Measurements of net radiation are not part of the routine measurement program at many weather stations and are commonly estimated based on other meteorological parameters. Daily values of net radiation were calculated using three net outgoing long-wave radiation models and compared to measured values. Four meteorological datasets representing two climate regimes, a sub-humid, high-latitude environment and a semi-arid mid-latitude environment, were used to test the models. The long-wave radiation models included a physically based model, an empirical model from the literature, and a new empirical model. Both empirical models used only solar radiation as required for meteorological input. The long-wave radiation models were used with model calibration coefficients from the literature and with locally calibrated ones. A measured, average albedo value of 0.25 was used at the high-latitude sites. A fixed albedo value of 0.25 resulted in less bias and scatter at the mid-latitude sites compared to other albedo values. When used with model coefficients calibrated locally or developed for specific climate regimes, the predictions of the physically based model had slightly lower bias and scatter than the empirical models. When used with their original model coefficients, the physically based model had a higher bias than the measurement error of the net radiation instruments used. The performance of the empirical models was nearly identical at all sites. Since the empirical models were easier to use and simpler to calibrate than the physically based models, the results indicate that the empirical models can be used as a good substitute for the physically based ones when available meteorological input data is limited. Model predictions were found to have a higher bias and scatter when using summed calculated hourly time steps compared to using daily input data.Financial support for this research was granted through The International Research School for Water Resources, The Faculty of Life Sciences of the University of Copenhagen and projects HID96-1295-C04-04 and AGL2000-1775-C03-03 (Spanish Ministry of Education), and P030/2000 (Autonomous Government of Aragón, Spain).Peer reviewe

Estimating plant root water uptake using a neural network approach

Water uptake by plant roots is an important process in the hydrological cycle, not only for plant growth but also for the role it plays in shaping microbial community and bringing in physical and biochemical changes to soils. The ability of roots to extract water is determined by combined soil and plant characteristics, and how to model it has been of interest for many years. Most macroscopic models for water uptake operate at soil profile scale under the assumption that the uptake rate depends on root density and soil moisture. Whilst proved appropriate, these models need spatio-temporal root density distributions, which is tedious to measure in situ and prone to uncertainty because of the complexity of root architecture hidden in the opaque soils. As a result, developing alternative methods that do not explicitly need the root density to estimate the root water uptake is practically useful but has not yet been addressed. This paper presents and tests such an approach. The method is based on a neural network model, estimating the water uptake using different types of data that are easy to measure in the field. Sunflower grown in a sandy loam subjected to water stress and salinity was taken as a demonstrating example. The inputs to the neural network model included soil moisture, electrical conductivity of the soil solution, height and diameter of plant shoot, potential evapotranspiration, atmospheric humidity and air temperature. The outputs were the root water uptake rate at different depths in the soil profile. To train and test the model, the root water uptake rate was directly measured based on mass balance and Darcy's law assessed from the measured soil moisture content and soil water matric potential in profiles from the soil surface to a depth of 100 cm. The 'measured' root water uptake agreed well with that predicted by the neural network model. The successful performance of the model provides an alternative and more practical way to estimate the root water uptake at field scale.Sunflower Pedotransfer function Water stress Soil salinity