ABSTRACT. Tillage practices have a sig- nificant influence on the soil hydro-physi- cal properties. This work evaluates the ef- fect of tillage on the soil bulk density (ρ b ), the van Genuchten (1980) water retention parameters (α y n), saturated sorptivity (S) and hydraulic conductivity (K). Three dif- ferent tillage systems were compared: con- ventional (CT) reduced (RT) and no-tillage (NT) systems. Measures were performed along an 18 month long fallowperiod. Under structured conditions, NT present- ed the highest values of ρb and n, and the lowest values of α, K and S. Loosening of soil due to primary tillage practices increased α, S and K. These parameters tended to recover their pre-tillage values after post-tillage copious rainfalls; which were also responsible of the surface crust formation. After the post-secondary tillage rainfalls, ρ b and n increased and α, S and
W inter wheat is the dominant crop grown within the semiarid cen- tral Great Plains of the United States. In this region, approximately 4,650,000 hectares of wheat are grown annually in a dryland wheat– fallow or wheat–summer crop–fallow rotation (NASS, 2012). Approximately, half of this wheat is grown in a wheat–fallow rotation (J. Holman, personal commu- nication, 2012). The fallowperiod, which lasts approximately 16 mo, is intended to store soil water for the subsequent wheat phase. Precipitation storage efficiency (fraction of precipitation that is stored in the soil) duringfallow, however, ranges only from 10 to 40% (Nielsen and Vigil, 2010; Hansen et al., 2012). The lower values correspond to conventional till, but even with the use of reduced till and no-till, fallow phase may not store more than 40% of precipitation (Hansen et al., 2012). In eastern Colorado, Nielsen and Vigil (2010) reported that mean pre- cipitation storage efficiency of a 14-mo fallow under wheat–fallow systems was 20% for conventional till and 35% for no-till. The use of fallow is also often at the expense of increased SOC losses (Peterson et al., 1998; Sherrod et al., 2003; Blanco-Canqui et al., 2010), increased soil erosion by wind and water (Merrill et Humberto Blanco-Canqui*
Model application. The meteorological variables used as driving variables in the simulations presented here were daily air temperature, relative air humidity, wind speed, precipitation, and global radiation. In addition, soilproperties such as hydraulic conduc- tivity and soil water retention curves were used as model input (Zhang et al. 2007b), while the Brooks- Corey equation (Brooks & Corey 1964) was used to describe soil water retention and, in combination with Mualem’s equation (Mualem 1976), to esti- mate unsaturated hydraulic conductivity (Zhang et al. 2007b). The same soilhydraulicproperties were applied for all treatments. The measured soil water content profile and soil temperature profile (apart from measured values, other layers assumed to be 15°C) were used as initial conditions for each treatment. The simulated soil profile (0–240 cm) was composed of 11 soil compartments (0–5, 5–15, 15–25, 25–35, 35–45, 45–55, 55–65, 65–80, 80–120, 120–160, and 160–240 cm deep). The simulations were conducted under the assumption that the soilhydraulicproperties remained unchanged during the experimental period. The calibration procedure of the model was the same as that reported by Zhang et al.
removal at high rates (56%) may not be sustainable. We suggest that threshold levels of corn residue removal should be established for this region to reduce degradation of soilhydraulicproperties and soil organic C levels. Otherwise, residue removal at high rates could negatively impact soils’ ability in the region to sustainable produce crops by reducing water infiltration, plant available water, and soil organic C levels. For example, the reduction in water infiltration could lead to increased risks of water erosion and runoff, and reduced water storage. Additionally, this study suggests the need for the development of CC management strategies or guidelines (planting date, planting method, termination date) to increase CC biomass production and the probability of improving soilproperties with CCs. In this study, as discussed, CC biomass production was 0.8 Mg ha -1 yr -1 , which may not be sufficient to exert significant changes in soilproperties and offset the negative effects of the high rate (5.9 Mg ha -1 yr -1 or 56%) of corn residue
Abstract: The knowledge of soilhydraulicproperties and processes leads to better predictions of both agricultural and environment impact. The objectives of this research are to determine, predict and compare the relationship between measured and estimated soilhydraulicproperties and also spatially characterize these properties using geostatistics. Mini disc infiltrometer at a suction rate of 2 cm per second was used for the determination of soilhydraulicproperties at different points of an alfisol in Nigeria. Soil samples (100, 200 and 300 mm depths) were also analyzed to determine soil bulk density (BD), total porosity (PT) and water holding capacity (WHC). The coefficients of variation (CV) of the textural classes indicate a non-considerable variability of the sand (CV=6%), silt (CV=20%) and clay (CV=3%) contents. From the statistical and spatial analysis for the different parameters, the variability of hydraulic conductivity (48%>33%>31%), cumulative infiltration (40%>26%>23%), soil water sorptivity (19%>11%>8%), followed the trend upper soil layer (0–100 mm) > middle (100–200 mm) > lower (200–300 mm) soil layers. Hydraulic conductivity and infiltration were more pronounced in soils with higher organic matter content (OMC) and PT. Pedotransfer models (PTF) for prediction of hydraulic conductivity (K), soil water sorptivity (Sw) and cumulative infiltration (I) from basic soilproperties such as OMC, PT were developed and validated using multiple-linear regression method. K, Sw and I predicted by the PTF models were significant for the upper and middle soil layers respectively (r = 0.812 and 0.670; 0.825 and 0.670, and 0.820 and 0.670). Contour and wireframe representation were used to spatially analyze the soilhydraulicproperties across the field. These contour and 3D surface plots are useful for establishing farm operating conditions, especially in water, fertilizers or pesticides management.
Soil moisture was measured using a gravimetric method. Soil temperature was measured in the field using an electrical soil thermometer. Soil bulk density was measured with the clod method (Black, 1986). Mungbean samples were collected five crops from each plot. Plant shoot and root were separated, and then washed three times with tap water. Plant fresh weights were measured before dried. Plant dry weights were measured after dried at 80 o C for 48 hours. Mungbean nodules were calculated after the nodules removed from the roots and washed three times with tap water. All data were analysed using the analysis of variance factorial of randomized completely block design. To compare
In this work, we change the perspective and associate the model with our quantitative understanding of reality that is tested against the given measurement data. To analyze the required model complexity, we prescribe temporally constant material properties, calculate the maximum likelihood of in- creasingly complex models and analyze the corresponding structural model–data mismatch. We show that this structural error analysis indicates limitations of these models and quan- tifies the effect of the respective unrepresented model errors on the inversely estimated material properties. Specifically, we analyze measurement data acquired at the test site (AS- SESS) while it as forced with a fluctuating groundwater table which ensures a high dynamical range of the hydraulic state. We set up a basic representation accounting for uncertain- ties of the hydraulic material properties and the forcing. Fol- lowing an uncertainty analysis, we additionally estimate the sensor position and small-scale heterogeneity. These increas- ingly complex models are applied to (i) three 1-D profiles in ASSESS with an increasing number of sensors per material and (ii) the full 2-D profile to additionally analyze the impli- cations of the restriction to a 1-D subsurface architecture and to few sensors per material.
or ‘hidden hunger’ has become more conspicuous in the dryland regions. The trends presented in previous section noted that more than 860 million people do not have enough food to meet their basic daily energy needs. Far more – an estimated three billion – suffer from the insidious effects of micronutrient deficiencies because they lack the money to buy enough meat, fish, fruits, lentils and vegetables. Women and children are most vulnerable to disease, premature death and impaired cognitive abilities because of diets poor in crucial nutrients, particularly iron, vitamin A, iodine and zinc. A great proportion of the 0 million children in developing countries who die each year of malnutrition are from the dryland regions. Today, micronutrient malnutrition diminishes the health, productivity and well-being of over half the global community, with its impact primarily on women, infants and children from low-income families. The consequences consist of ) greatly impaired national development efforts; 2) reduction in labor productivity, educational attainments in children, school enrolments and attendance; and 3) increase
Abstract: Intervening cropping period perhaps the most ignored period, which could be exploited for cultivating the intervening crops which further add to the soil, crop and water productivity and finally livelihood of the farmers of the region. The present investigation was carried out after rice- 2014, to monitor the residual effect of different tillage (wheat), establishment methods and tillage (rice) on the fluctuating behaviour of the soil moisture during intervening period. Our findings suggested that CTW-DSRZT (conventionally tilled wheat and zero till direct seeded rice) plots conserved more moisture than ZTW-DSRZT (zero till wheat and zero till direct seeded rice) plots an exception of CTWDSRCT plots which were almost equally effective in conserving the soil moisture. On an average, soil matric tension (SMT) was reported to be 36% higher in CTWDSRZT than CTWDSRP plots at 10cm soil surface. Further, ZTW-DSRZT plots on an average dried 8% faster than ZTW-DSRP plots. At 20cm, DSRZT plots dried 3% faster than its allied plots while at 30cm depth, in DSRP plots, SMT values increased 12% and 11% higher under CTW block and ZTW blocks, respectively than its allied plots. SMT readings in all the ZTW plots on an average increased at much more faster rates (24%) than CTW plots. The ZT plots had 1.4% higher water depths than the CT plots. Evaporation losses pragmatic to be higher (17.2% and 7.3%) in ZTW-DSRZT plots as compared to the ZTW -DSRCT and CTW-DSRCT plots which might improve declining crops and water productivity in the region.
which impresses us that a proper model of a specific system has been acquired. However, there are a number of issues in this algorithm and this back-calculate process is often overlooked, where the underestimated potential uncer- tainties in the assessments are hidden. One of the issues is equifinality, which means that different hydraulic parame- ter combinations can result in a similar response. Hence, many sets of model variables may be considered equal or almost equal simulations of the system, which can lead to huge uncertainties in model predictions and explanations, particularly in the case of limited observations and complex models. Another issue is that NLLS attempts to only find the sole ‘optimal’ parameter set that best fits the observed data, not a set of acceptable parameters. As a result, if the inverse solution has many local minima, using NLLS will lead the simulation to a local optimum, which is exactly not the ‘optimal’ parameter set at all.
spring wheat yields were reduced compared with tra- ditional summer fallow as a result of soil water depletion by lentil. However, in the second half of the study, lentil was planted in April and turned under in early July to reduce water consumption. Subsequent spring wheat yields in the second half of the study were similar between green manure and traditional fallow, plus there was a gradual increase in wheat grain protein and a gradual decrease in N fertilizer requirement following lentil. In Montana, a similar 12-year study was conducted with lentil as a partial summer fallow replacement 9 . Although the authors did not see a difference in starting soil water for spring wheat following green manure killed at full bloom or fallow, they did see reduced grain yield following green manure in the ﬁrst 5 years, which they explained by low soil nitrate following green manure. In the last 6 years of the study, grain yields following green manure and fallow were similar and the green manure treatment had 26% greater spring soil nitrate than fallow. In a conventional production system in Colorado, winter wheat yield following annual legume green manures [Austrian winter pea (Pisum sativum L. subsp. sativum var. arvense (L.) Poir.), spring ﬁeld pea and black lentil] was reduced from 400 to 1050 kg ha − 1 compared with summer fallow depending on the termination date of the green manure 10 . Water use by the green manure explained 88% of the variability in winter wheat yield. The bene ﬁts of green manure fallow were highly weather- dependent and inconsistent. The impact of replacing summer fallow in a no-till production system with various spring-planted crops prior to winter wheat seeding was studied in western Nebraska 11 . Winter wheat yields following summer fallow replacement crops were reduced from 22 to 58% following oat (Avena sativa L.) + pea for forage and corn (Zea mays L.), respectively, compared with wheat following summer fallow. This was largely explained by a 27 –41% reduction in soil water at wheat planting following these two crops compared with following summer fallow. Winter wheat yield has been reported to be strongly correlated with the available soil water at wheat planting 12 .
Received: 1 April 2020; Accepted: 13 April 2020; Published: 17 April 2020 Abstract: Cover crop (CC) management in vineyards increases sustainability by improving soil chemical and biological fertility, but knowledge on its effects in semiarid soils is lacking. This study evaluated the effect of leguminous CC management on soil organic carbon (SOC) sequestration, soil nitrate content and microbial diversity in a semiarid vineyard, in comparison to conventional tillage (CT). SOC and nitrate were monitored during vine-growing season; soil respiration, determined by incubation experiments, microbial biomass and diversity was analyzed after CC burial. The microbial diversity was evaluated by bacterial and fungal automated ribosomal intergenic spacer analysis (ARISA) and high-throughput sequencing of 16SrDNA. CC increased nitrate content and, although it had no relevant effect on SOC, almost doubled its active microbial component, which contributes to SOC stabilization. An unexpected stability of the microbial communities under different soil managements was assessed, fungal diversity being slightly enhanced under CT while bacterial diversity increased under CC. The complete nitrifying genus Nitrospira and plant growth-promoting genera were increased under CC, while desiccation-tolerant genera were abundant in CT. Findings showed that temporary CC applied in semiarid vineyards does not optimize the provided ecosystem services, hence a proper management protocol for dry environments should be set up.
Together with the water content distribution, the time- lapse GPR data also contain information about the subsurface architecture. However, separating signal contribution from the subsurface architecture and the hydraulicdynamics is not always possible. Here, this is most prominent for the reflec- tion of the material interface (V). Initially, the amplitude of this reflection is large, because the water content in material C is near the residual water content, whereas the water con- tent in material A is significantly higher at the material inter- face. As soon as both materials are water saturated, the am- plitude of the material interface reflection (V) is low since the effective porosities of the two materials are similar. Thus, the amplitude of the reflected signal originating from the mate- rial interfaces may change depending on the hydraulic state. Additional information about the subsurface architecture can be inferred from the reflection at the material interface be- tween material A and the gravel layer (VI) and from the re- flection at the material interface of the gravel layer and the concrete basement (VII). These reflections are in particular suitable to analyze the total change of water content over time.
Many contributions to the soil hydrological literature have demonstrated the limited information content of in situ mea- surements of soil water state variables under natural bound- ary conditions to estimate the soilhydraulicproperties. A general approach, which has yet received very little at- tention in the soil hydrological literature, is to use an infor- mative prior distribution of the soilhydraulic parameters and to combine this distribution with the in situ observations us- ing Bayes’ theorem. In this paper, we investigated to which degree prior information about the soilhydraulic parameters can help improve parameter identifiability in inverse mod- elling of in situ soil water dynamics under natural boundary conditions. We used percentages of sand, silt, and clay as input variables to the ROSETTA pedotransfer function that predicts the soilhydraulic parameters. Textural data consti- tute basic soil information that is readily available in most vadose zone studies. In addition to the standard ROSETTA prediction that provides the mean values and standard devi- ations of the predicted parameters, we tested a Monte Carlo approach to derive the correlation structure of the predicted parameters. It was one objective of this study to explore the value of this additional information on parameter correlation in Bayesian inverse modelling. Another objective was to test the robustness of the Bayesian approach in case of biased prior information. We formulated three different prior distri- butions that incorporate to different extents the prior infor- mation derived with ROSETTA. We illustrated our approach using synthetic and real-world observations of in situ soil wa- ter dynamics under natural boundary conditions.
were closely associated with vegetation characteristics (Ta- ble 2). Vegetation factors are indispensable to the stability of soil status, such as the formation of SOC, porosity and structure. In fact, root activity and litter fall input decrease significantly or disappear as degradation degree increases, and thus the decomposition process and organic matter ac- cumulation in soil are hindered with degradation. Depletion of SOC greatly alters the soil micro-environment and might trigger a series of changes in soil physical, chemical and bio- logical processes (Nelson and Sommers, 1996). For instance, it has been confirmed that clay and silt contents are largely dependent on the release of organic acid from soil organic matter, which can corrode coarse minerals and transfer large grains into fine particles (Fan et al., 2015). Organic matter can also act as “glue” in soil aggregate formation and deter- mine water stability (Lipiec et al., 2009). Therefore, a de- crease in SOC will strongly influence soil structure, and thus brings about overall changes in soil physical and chemical properties. Furthermore, the absence of plant coverage and root grasp will cause topsoil to become vulnerable to wind, raindrops, surface flow and compaction, directly resulting in soil erosion and degradation, and the particle distribution of soil samples in the soil texture triangle (Fig. 4) clearly shows the sandification trend with increasing degradation. Insignif- icant correlations between vegetation characteristics and soilproperties of the deep layers (40–80 cm; Table 2) were in ac- cordance with the fact that no significant differences in soil basic properties were found among degradation degrees in the deep layers.
are interrelated can be modified by reduced or mini- mum tillage (Thomas et al., 2007). The No-till system augment the deposition of diverse plant biomass like weeds on undisturbed soil surface especially in fallow season due to increases its moisture content after rain- fall. However, it can accelerate soil microbial activity, improve aggregate structure and soil physic-chemical properties predominantly nitrogen content, organic matter percentage, organic carbon content and cation exchange capacity of soil; whereas, it decreases car- bon nitrogen ratio (Madejon et al., 2009; Naudin et al., 2010; Derpsch et al., 2010; Moussa-Machraoui et al., 2010; Benitio, 2010; Celik et al., 2011; A’lva- ro-Fuentes et al., 2012). Conservation agriculture is spreading rapidly worldwide, but a little work has been done in Pakistan’srainfed cropping system es- pecially in Pothwar Region. As all agro-ecosystems are different; therefore, this study could be very much helpful for better planning of moisture conservation, soil improvement and yield enhancement of different crops for this region (Derpsch, 2007).
by cover crop with tillage (CC-Till), no cover crop with tillage (NC-Till), cover crop with no till (CC-NT), and no cover crop with no till (NC-NT) managements. Note: (a) Bar indicates least significant difference (0.05) value for bulk density. (b) The least significant difference (LSD) (0.05) value for Ksat is listed on the graph due to log scale…. ..................................................... 75 Figure 3.2 Soil water retention curves at (a) 0 to 10 cm (b) 10 to 20 cm (c) 20-30 cm (d) 30 to 40 cm depths
At both Akron, Colorado and Sidney, Nebraska in the central Great Plains, USA, simulated grain yields of corn, canola, and proso millet and forage yields of foxtail millet and triticale increased as PAW at planting increased, especially when PAW changes were considered for the whole soil proﬁle. When the ﬁve crops consid- ered here were planted under similar initial PAW conditions, they differed in yield and economic returns due not only to price differ- ences of their harvest products but also to differences in harvest yields resulting from differences in growing season lengths and associated precipitation received. Greater net returns were found for the two forage crops than for the three grain crops. The data and ﬁgures generated in this study can be used to estimate rela- tive crop yields, net returns, and risk involved in selecting one of the ﬁve studied spring- or summer-planted crops to intensify the WF system into, potentially, a winter wheat-spring/summer crop- fallow rotation, when a measure or estimate of the PAW at planting is available. Intensifying the wheat-fallow system to two crops in three years is not likely to greatly inﬂuence wheat yields following the production of the spring or summer crop, as the 12–14-month fallowperiod prior to wheat planting allows for signiﬁcant recharge of soil water. Nielsen et al. (2002) showed 9-yr average soil water contents at wheat planting and wheat yields that were the same for both wheat-fallow and wheat-corn-fallow no-till production systems. However, farmers would need to be aware of the fact that changes in net returns are likely to occur when intensify- ing from a wheat-fallow system to a three-year rotation where a crop is planted in the growing season following wheat production. These changes in net returns will be a result of the productivity and expenses associated with producing both crops in the system rather than from any of the individual crops involved (wheat or the summer crop) ( Peterson et al., 1993, 1996; Halvorson et al., 2002; Peterson and Westfall, 2004 ).