Impact of grassland contract policy on soil organic carbon losses from alpine grassland on the Qinghai–Tibetan Plateau

Carbon storage in the soils on the Qinghai–Tibetan Plateau plays a very important role in the global carbon budget. In the 1990s, a policy of contracting collective grasslands to smaller units was implemented, resulting in a change from the traditional collective grassland management to two new management patterns: a multi‐household management pattern (MMP: grassland shared by several households without enclosures) and a single‐household management pattern (SMP: grassland enclosed and used by only one household). In 2016, 50 MMP and 54 SMP winter pastures on the Qinghai–Tibetan Plateau were sampled to assess the differences in soil organic carbon (SOC) between the two management patterns. Results showed that average SOC was significantly greater under MMP than under SMP, with an estimated 0.41 Mg C/ha/yr lost due to SMP following the new grassland contract. Based on the government's grassland policy, four grassland utilization scenarios were developed for both summer and winter pastures. We found that if the grassland were managed under SMP, likely C losses ranged between 0.31 × 10⁷ and 6.15 × 10⁷ Mg C/yr across the Qinghai–Tibetan Plateau relative to MMP, which more closely resembles pre‐1990s grassland management. Previous estimates of C losses have only considered land use change (with cover change) and ignored the impacts driven by land management pattern changes (without cover change). The new data suggest that C losses from the Qinghai–Tibetan Plateau are greater than previously estimated, and therefore that the grassland contract policy should be reviewed and SMP households should be encouraged to reunite into the MMP. These findings have potential implications for land management strategies not only on the Qinghai–Tibetan Plateau but also other grazing regions globally where such practices may exist.     

Meiotic behavior as a selection tool in the breeding of <Emphasis Type="Italic">Brachiaria humidicola</Emphasis> (Poaceae)

Brachiaria humidicola is a tropical grass that grows in seasonally swampy grasslands in Africa. In Brazil, two apomictic cultivars (2n = 54) of this species are widely used as pastures in poorly drained soils. The recent discovery of a sexual polyploid accession (2n = 36) in the germplasm collection at the Embrapa Beef Cattle Research Center allowed intraspecific hybridization with the objective of broadening the genetic variability and selection of superior genotypes in this species. Hybridization, however, depends on accessions with the same ploidy level. Cytological analyses of 55 accessions revealed that 19 apomictic accessions also presented 2n = 36 chromosomes. Chromosome pairing in hexavalent association at diakinesis and metaphase I suggested that the basic chromosome number for this species is x = 6. Cytological analysis revealed abnormalities in variable frequencies in the meiosis of these hexaploid (2n = 6x = 36) accessions. The most common were those related to irregular chromosome segregation which led to unbalanced gamete formation, but chromosome stickiness was also recorded. These results clearly demonstrate the value of cytogenetics in the choice of genitors and for superior hybrids to be obtained in the breeding of this species. For that both the ploidy level and the frequency of abnormalities need to be considered, besides other favorable agronomic characteristics.     

A New Approach to Predict Daily pH in Rivers Based on the “à trous” Redundant Wavelet Transform Algorithm

Prediction of pH is an important issue in managing water quality in surface waters (e.g., rivers, lakes) as well as drinking water. The capacity of artificial neural network (ANN), wavelet-artificial neural network (WANN), traditional multiple linear regression (MLR), and wavelet-multiple linear regression (WMLR) models to predict daily pH levels (1, 2, and 3 days ahead) at the Chattahoochee River gauging station (near Atlanta, GA, USA) was assessed. In the proposed WANN model, the original time series of pH and discharge (Q) were decomposed (after being split into training and testing series) into several sub-series by the the à trous (AT) wavelet transform algorithm. The wavelet coefficients were summed to obtain useful input time series for the ANN model to then develop the WANN model for pH prediction. The redundant à trous algorithm was used for data decomposition. Model implementation indicated the values of 1-day-ahead pH predicted by the WANN model closely matched the observed values (with a coefficient of determination, R²?=?0.956; Root Mean Square Error, RMSE?=?0.019; and Mean Absolute Error, MAE?=?0.015). It is therefore possible that the WANN model’s accuracy can be attributed to its better predictive ability (due to the use of the AT) to remove the noise caused by pH shifts (e.g., acid precipitation). Peak pH values predicted by the WANN model were also closer to observed values compared to the other machine learning models.     

Failure of cytokinesis and 2n gamete formation in Brazilian accessions of Paspalum

Cytological analysis on 112 Brazilian accessions of Paspalum showed that 52 were affected by failure of first or second cytokinesis during microsporogenesis. When the failure of cytokinesis occurred only in the first division and in the second division the cytokinesis was normal, a dyad of microspores was formed. In most of them the two cells remained binucleate, but in some a restitutional nucleus was observed. When the first division was normal and the failure of cytokinesis occurred only in the second division, dyads and triads were observed. A restitutional nucleus in one or both cells of the dyad, or in the binucleate cell of the triad was also observed. The percentage of dyads and triads varied among accessions, reaching 15.75 of the sporads in some. The absence of cytokinesis indicates the possibility that the widespread occurrence of polyploidy in the genus Paspalum originates from 2n pollen grains. This revised version was published online in July 2006 with corrections to the Cover Date.     

Grassland Degradation on the Qinghai-Tibetan Plateau: Reevaluation of Causative Factors

In light of Harris (2010) finding insufficient evidence to assert a causal linkage between any of the seven previously proposed causative factors and grassland degradation on the Qinghai-Tibetan Plateau (QTP), more recent empirical studies on QTP grassland degradation were explored to ascertain whether, in fact, these factors are casually linked to grassland degradation. The mischaracterization of the underlying causes of grassland degradation among policymakers has and continues to be an obstacle to sustainable regional grassland management practices. Accumulating evidence suggests that privatization and sedentarization, small mammals, climate change, harsh environments, fragile soils, and overgrazing contribute to grassland degradation. However, neither obsolete livestock husbandry methods nor the recent conversion of rangelands to agriculture had a meaningful influence. Estimates of the total area of degraded grasslands and the establishment of grassland degradation criteria have not been properly addressed in the literature. Both omissions constitute the basis for investigating the causes of grassland degradation across the QTP and the adoption of measures to manage these grasslands sustainably.     

Investigating monthly precipitation variability using a multiscale approach based on ensemble empirical mode decomposition

Paddy and Water Environment - In environmental and hydrological studies, the issue of variability in precipitation is of great importance, particularly for regions situated in arid and semiarid...     

Rainwater harvesting for the management of agricultural droughts in arid and semi-arid regions

Human activities, which affect ecosystem dynamics, pose a continuous challenge to individuals and communities trying to survive in arid and semi-arid regions. The development of a method to employ rainwater harvesting (RWH) in the management of agricultural drought in arid and semi-arid regions comprised two phases: (i) detection of agricultural drought in Egypt’s El-Beheira governorate using a normalized difference vegetation index differencing technique and (ii) the delineation of RWH locations potentially suitable for the management of agricultural drought in the region using a GIS decision support system (DSS). Temporal vegetation cover analysis showed significant spatio-temporal changes that have occurred in the last 40 years: a general decrease in vegetation cover reflecting a trend towards ecosystem degradation, contrasted by a greening trend in some pockets within the region. Potentially suitable rainwater harvesting areas for agricultural drought management and attendant vegetation recovery were delineated in the region using DSS. The model generated a RWH map with five categories of suitability: excellent, good, moderate, poor and unsuitable. On average, 10.9 % (1104.17 km²) and 12 % (1215 km²) of the study area was classified as excellent and good for RWH, respectively, while 11.7 % (1185.21 km²), 15.4 % (1560 km²) and 50 % (5065 km²) of the area were classified as moderate, unsuitable and poor, respectively. Most of the areas with excellent to good suitability predominantly lie in areas which faced severe drought between 2010 and 2014. To successfully implement the drought management plan, a number of RWH sites within the excellent areas must be developed.