Nitrogen Mineralization Potential for Different Soil Types of the Arid and Semi-arid Regions of Morocco

Nitrogen mineralization potential for different soil groups of the arid and semi-arid regions of Morocco based on an incubation study were estimated and the rate constants for the six soil groups were generated. Based on this data, a model for predicting N mineralization potential from chemical and physical soil characteristics was developed.Agronom

Fate of phthalic acid esters during composting of both lagooning and activated sludges

Among the phthalic acid esters (PAEs) targeted by the United States Environmental Protection Agency (USEPA) as priority pollutants, di-ethyl-hexyl phthalate (DEHP) is the major pollutant identified at high concentration level in lagooning sludge (LS), at about 28.67 mg/kg, andin activated sludge (AS), at about 6.26 mg/kg. Other phthalic acid esters, such as di-butyl phthalate (DBP) and di-methyl phthalate (DMP) show very low concentrations. During sludge composting, after the stabilization phase, the subsequent appearance of DEP and then DMPoccurred indicating that microbial metabolism begins by alkyl side-chain degradation before aromatic ring-cleavage. The appearance andaccumulation of PAEs with a short alkyl side-chain in the last stages of AS and LS composting is suggested originating from the degradationof phthalates with a much long side-chain. The DEHP showed a rate of biodegradation that follows a first-order kinetic model during composting of both AS and LS. The calculated DEHP half-lives are 45.4 days for LS and 28.9 days for AS. The better DEHP biodegradationrate (2.4 Â 10À2 dayÀ1) have been observed in the case of AS composting compared to LS compost (1.53 Â 10À2 dayÀ1). The mono-ethyl-hexyl phthalates MEHP has been shown to follow the same order of biodegradation as DEHP indicating that the same mechanism is followed(hydrolysis or dealkylation of each DEHP side-chain). Composting could be suggested as a detoxification process for the removal of PAEs(mainly DEHP) from sludges after a sufficient time of treatment to provide a safe end product

Assessment of the agronomic value of solar-dried sludge and heavy metals bioavailability based on the bioaccumulation factor and translocation index

This study aimed to assess the agronomic value of solar-dried sludge (SDS) and the transfer of Cr, Ni, Pb, and Cu to wheat (Triticum aestivum) and faba bean (Vicia faba). A greenhouse experiment was performed involving two rates of SDS (15 t/ha and 30 t/ha) from an activated sludge-based wastewater treatment plant. In addition to the single use of an SDS amendment, co-application of SDS and mineral fertilizers was also included to determine the best scenario resulting in high yields and less negative implications on the environment. Data for both wheat and faba bean showed that applying SDS at 30 t/ha led to competitive yields compared to the ones obtained previously, while 15 t/ha of SDS and mineral fertilizers were co-applied. The use of SDS increased soil organic matter, slightly decreased the pH value, and increased soil salinity. The contents of Ni, Cu, and Pb were not significantly affected by the application of SDS. Only Cr showed high soil concentrations in proportion to the increasing rates of SDS. The bioaccumulation of heavy metals in roots was more important in 30 t/ha than that in 15 t/ha amended soil. In the case of wheat, the bioconcentration factor (BCF) root values correspond to the following order: Cr (0.89) >Cu (0.85)> Ni (0.28)> Pb (0.22). In the case of faba bean, BCF roots were observed as follows: Cu (1.04 > Ni (0.37)> Cr (0.16)> Pb (0.15). Wheat excluded Cr, Ni, and Pb from the uptake by shoots, and Cu was translocated from roots to shoots with a percentage of 11% at 30 t/ha of applied SDS. Faba beans demonstrated more important values of HM’s translocation by respecting this order (Ni (37.7%) > cu (30.24%)> Cr (17.59%), while Pb was excluded from the translocation. No significant difference was observed regarding the translocation index when the sludge rate has been duplicated from 15 t/ha to 30 t/ha. Based on these outcomes, SDS used at the rate of 30 t/ha is the best scenario to amend the soil and provide nutrients to plants. Wheat is translocating less heavy metal to the edible part; it is, thus, the most suitable crop to be involved in the current context

Impact of applying composted biosolids on wheat growth and yield parameters on a calcimagnesic soil in a semi-arid region

A field trial in a semi-arid climate was carried out on wheat (Triticum aestivum var. Marchouch) growing on a calcimagnesic soil using compost applied at 42 T/ha during the three years of study, but in different ways: C1, C2 and C3. Over this period, the level of total Kjeldhal nitrogen (TKN) increased in the soil amended by high doses (C2 and C3, by about 33 and 50%) compared with steady low amendment (C1) and to both controls NF (soil without fertilisation) and MF (soil receiving mineral fertilization). Adding compost also led to a positive influence on cation exchange capacity (CEC) by increasing humic substance levels (HS) which doubled in plots C2 and C3 compared with both controls. In NF soil, the TKN, total organic carbon (TOC) and the pH of soil showed a clear negative correlation with the agronomic parameters. In the MF soil, most physico-chemical parameters correlated well with the agronomic parameters: input of mineral elements balancing export through harvest. In amended soil, especially in C3 plots, HS and CEC showed significant correlations with most agronomic parameters (P1: 69.1%) due to enhanced CEC and sequestration of available carbon in the form of stable humic structures.Key words: Compost, wheat growth, calcimagnesic soil, semi-arid region

Optimization of macronutrients for improved grain yield of quinoa (Chenopodium quinoa Wild.) crop under semi-arid conditions of Morocco

In the context of climate change, quinoa represents a potential alternative crop for increasing crops diversity, agricultural productivity, and farmer’s income in semi-arid regions. However, appropriate crop management practices under limited water supply are still poorly documented. Quinoa, like other cultivated crops, needs optimum quantities of nutrients, especially nitrogen (N), phosphorus (P), and potassium (K), for better growth and high grain yield. To determine the adequate levels of nutrient requirements and their effect on quinoa growth and productivity, a field experiment was conducted during two growing seasons (2020–2021 and 2021–2022). The experiment was conducted in Ben Guerir region, north-central Morocco, and consisted of a randomized complete block design (RCBD) with three replications. The treatments studied consist of a combination of four N rates (0, 40, 80, and 120 kg ha−1), three P rates (0, 30, and 60 kg P2O5 ha−1), and three K rates (0, 60, and 120 kg K2O ha−1). The physiological, nutritional, and production parameters of quinoa were collected and analyzed. The results showed that the highest total biomass (3.9 t ha−1) and grain yield (0.8 t ha−1) under semi-arid conditions were obtained with 40 kg N ha−1, 60 kg P2O5 ha−1, and 120 kg K2O ha−1. The application of 40–60–120 kg ha−1 of N–P2O5–K2O increased plant height by 44%, chlorophyll content index by 96%, total biomass by 134%, grain yield by 112%, and seed weight by 118%. Among the three macronutrients, N was the most limiting factor, followed by K and P. Nutrients uptake data showed that quinoa needs 60 kg N, 26 kg P2O5, and 205 kg K2O to produce 1 t of grain yield. Our field results provide future recommendations for improving the agronomic and environmental sustainability of quinoa cultivation in dryland areas in Morocco

Worldwide development of agronomic management practices for quinoa cultivation: a systematic review

Quinoa (Chenopodium quinoa Wild.) is a drought and salinity-tolerant crop that originated in the Andes over 7000 years ago. It is adapted to different agroecological areas and can be grown from sea level to an altitude of 4000 m. The outstanding nutritional status of quinoa, with its high content of proteins, vitamins, and minerals, makes it a promising crop able to combat hunger and malnutrition in different countries in the 21st century. Quinoa cultivation has expanded from South America to Africa, Europe, Asia, and North America. Reviewing quinoa cropping practices will provide farmers with adequate recommendations for improving the agronomic and environmental sustainability of quinoa cultivation worldwide. For this reason, we conducted a systematic review of agronomic management practices in 148 field experiments conducted worldwide from 2000 to 2022. The collected data from the literature were analyzed and presented by location to determine high-performing genotypes, optimal planting dates, and other adequate cropping practices affecting quinoa performance and yield. Results showed that quinoa could be successfully cultivated in the new farming areas. Quinoa yields were higher than those reported in its place of origin, ranging from 108 kg ha-1, obtained by KU-2 in Washington State, to 9667 kg ha-1, obtained by Longli in China. Although quinoa is considered a crop with low input requirements, positive grain yield response was observed following increasing fertilization rates. Quinoa needs 2 to 4.6 kg of nitrogen to produce 1q of grain yield. In terms of phosphorus and potassium, quinoa needs 3.7 kg P2O5 and 4.3 kg K2O to produce 1 ton of total biomass. Quinoa has low water requirements (300-400 mm). However, a positive response was recorded with water quantities up to 866 mm. During our investigation, weed control in quinoa crop is still undeveloped and usually done manually. Research addressing this issue can increase quinoa yields and decrease the production cost. Downey mildew and birds’ attack are the major phytosanitary problems affecting quinoa grain yield. Other pests such as miners and aphids can also affect the health of quinoa, but their injury is not a serious problem. After the harvest, saponins found in the out layer of the seed can be removed through washing and mechanical pearling process, but the latter technic was found to be efficient and cost effective to reduce the saponin content. Our results constitute the first recommendation base for the adequate worldwide agronomic practices of quinoa crop

A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas

peer reviewedIn the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider in this perspective. In this study, regression (MR) and Random Forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally , MR and RF models were calibrated both with or without remotely-sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (NRMSEs below 35%), but were always outperformed by APSIM model. Both RF and MR selected remotely-sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely-sensed LAI in the calibration process reduced NRMSE of 4.5% and 1.8 % on average for MR and RF models respectively. Calibration of region specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.SoilPhorLife-Projet

Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: a case study from Morocco

Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, models based on clay, silt and soil organic matter as input variables showed the best performance after calibration (R2 of 0.590, 0.785, 0.786 for w0, w330, and w15000, respectively). Regarding the techniques based on machine learning, random forest showed the best performance after calibration compared with other algorithms (R2 of 0.930, 0.955, 0.954 for w0, w330, and w15000, respectively). PTFs represent a low cost and easy technique to estimate soil hydraulic properties, to improve water management efficiency for the farmers

Changes in soil surface properties under simulated rainfall and the effect of surface roughness on runoff, infiltration and soil loss

Soil erosion by water is a result of detachment of particles or small aggregates from the soil surface followed by transport of the detached material. One of the elements that affects surface runoff and soil erosion is the soil surface roughness (SSR). Prior research reports that increasing SSR reduces generation of runoff and soil loss. In addition to that, it is widely reported that across-slope oriented roughness is better at controlling soil and water losses. However, to date there have been few studies into the effect of both magnitude and orientation of SSR on runoff, infiltration and soil erosion at the sub process level (i.e. by raindrop splash and overland flow), occurring simultaneously. In this study, the effects of up-down-slope oriented SSR (Treatment A), across-slope oriented SSR (Treatment B) and random SSR (Treatment C) were compared, along with a smooth surface (Treatment D). A moderate slope gradient of 10 %, a simulated rainfall intensity of 90 mm hr−1 and storm durations of 15 or 30 min were considered. The SSR was measured using the chain method, before and after the rainfall event. Images of the soil surface were taken using a hand-held laser scanner to monitor the effect of rainfall on the surface morphology. The outcome of this study shows that rainfall erosivity increases the SSR of the initially smooth surface, but decreases that of the initially rough surface, particularly in the random SSR treatment, where the decrease in SSR was 64 % of the pre-rainfall condition. This was due to the effects of raindrop impacts and overland flow. The random SSR treatment generated significantly more runoff and soil loss, and less infiltration than all other treatments (p < 0.001), but for raindrop splash erosion, there was no significant difference between random SSR and the other treatments. Contrary to expectations, the across-slope oriented SSR did not always reduce runoff and soil erosion compared to the up-down-slope orientation. This can be explained by degradation of surface microtopography by rainfall and runoff, as confirmed by the post-rainfall SSR measurements