Building capacity for assessing spatial-based sustainability metrics in agriculture

Crop yield is influenced over time and space, namely, by a wide range of variables linked with crop genetics, agronomic management practices and the environment under which the crop dynamically responds to maximize growth potential and survival. Such variability can pose substantial uncertainty and risks in the use of agricultural sustainability decision-making frameworks that include crop yield as a leading metric. Here, decision analytics can play a vital role by guiding the use of statistical-based analytics to build in a higher degree of intelligence to enable better predictive (i.e., crop yield forecasting both over the growing season and inter-annually) and prescriptive (optimization across crop areas and subdivisions) approaches. While inter-annual variability in yield can be modelled based on a deterministic trend with stochastic variation, quantifying the variability of yield and how it changes across different spatial resolutions remains a major knowledge gap. To better understand how yield scales spatially, we integrate in this study, for the first time, multi-scale crop yield of spring wheat and its variance (i.e., field to district to region) obtained within the major wheat growing region of the Canadian Prairies (Western Canada). We found large differences between the mean and variance from field to district to regional scales, from which we determined spatially-dependent (i.e., site specific) scaling factors for the mean and variance of crop yield. From our analysis, we provide several key recommendations for building capacity in assessing agricultural sustainability using spatial-based metrics. In the future, the use of such metrics may broaden the adoption and consistent implementation of new sustainable management protocols and practices under a precautionary, adaptive management approach

Prévision de la production nationale d’arachide au Sénégal à partir du modèle agrométéorologique AMS et du NDVI

As many subsaharian countries, the agriculture of Senegal is widely dependent on the climate. This agriculture takes up 60% of active population and contributes at 20% of the GDP. It’s dominated by many crops industries, whose groundnut industry. The aim of this study is to find a forecasting model of the national production of groundnut at the third decade of September and October. This model is based on the yield forecasting at departmental scale with the outputs of the agrometeorological model AgroMetShell, NDVI data and other meteorological data. This study which is one first approach in groundnut’s yield forecasting, shows the relation between yield and the explanatory variables at the third decade of October provide a best forecast of the yield of groundnut at national scale (R² = 0.55 and RMSE = 28 kg/ha).Au Sénégal, à l’instar de la plupart des pays subsahariens, l’agriculture est largement tributaire des conditions climatiques. L’agriculture paysanne occupe 60% de la population active et contribue pour 20% au PIB. Elle est dominée par plusieurs filières dont la filière arachide. L’objectif de cette étude est de trouver un modèle de prévision de la production nationale d’arachide à la troisième décade des mois de septembre et d’octobre. Ce modèle est basé sur la prévision du rendement de la culture au niveau départemental à partir des sorties du modèle agrométéorologique AgroMetShell, des données NDVI et de données météorologiques. Cette étude qui constitue une première approche dans la prévision du rendement de l’arachide, montre que la relation trouvée entre le rendement à l’échelle départementale à la troisième décade d’octobre et les variables explicatives fournit une bonne prévision du rendement de l’arachide à l’échelle nationale, avec un R² = 0.55 et une erreur de prédiction faible (RMSE = 28 kg/ha)

Sustainable production of Robusta coffee under a changing climate: a 10-year monitoring of fertilizer management in coffee farms in Vietnam and Indonesia

Assessing and prescribing fertilizer use is critical to profitable and sustainable coffee production, and this is becoming a priority concern for the Robusta coffee industry. In this study,annual survey data of 798 farms across selected Robusta coffee-producing provinces in Vietnam and Indonesia between 2008 and 2017 were used to comparatively assess the fertilizer management strategies in these countries. Specifically, we aimed to characterize fertilizer use patterns in the key coffee-growing provinces and discuss the potential for improving nutrient management practices.Four types of chemical (NPK, super phosphate, potassium chloride and urea) and two of natural (compost and lime) fertilizers were routinely used in Vietnam. In Indonesia, NPK and urea were supplemented only with compost. Farmers in Vietnam applied unbalanced quantities of chemical fertilizers (i.e., higher rates than recommended) and at a constant rate between years whereas Indonesian farmers applied well below the recommended rates because of poor accessibility and financial support. The overuse of chemical fertilizers in Vietnam threatens the sustainability of Robusta coffee farming. Nevertheless, there is a potential for improvement in both countries in terms of nutrient management and sustainability of Robusta coffee production by adopting the best local fertilizer management practices

Foliar Application of Boron during Flowering Promotes Tolerance to Cocoa (Theobroma cacao L.) Swollen Shoot Viral Disease

Boron nutrition is known to reduce the effect of some viral and fungal diseases on plant fitness. This study investigated the potential of boron application to improve yield and tolerance of cacao trees naturally infected by virulent cocoa swollen shoot virus (CSSV) strains and determined the effective dose and time of application. Foliar sprays of a commercial product containing 20.5% of boron were performed either at the onset of flowering’s peak of the little milking (early in November) or four weeks later (early in December) with four doses of boron (0, 31.25, 41.67, and 83.27 g/ha) in a randomized complete block design with four replications. We found that boron application improved foliar density and induced production of pods of normal shape meanwhile reducing the appearance of this misshapenness due to CSSV. Boron also increased the number of emitted flowers, cherelles and pods subsequently. Moreover, weight and size of fresh cocoa beans per pod were positively correlated to boron dosage. Interestingly, foliar sprays performed early in November resulted in less flat cocoa beans. Finally, the optimal dose of boron that reduced the adverse effects of the most virulent form of cocoa swollen shoot viral disease is 41.67 g/ha

Ecological Sanitation in Tropical Environments: Quantifying the Inactivation Rates of Microbiological Parameters During a Feces-Based Composting Process

Dry composting toilets are increasingly being used as practical sanitation systems in areas with inadequate sewage disposal and inefficient (or inexistent) hydraulic network. In Côte d’Ivoire, the by-products from such systems are progressively used in peri-urban agriculture as organic fertilizer, most of the times regardless of any hygienic quality assessment; constituting thereby a major health risk. The main objective of this study was to assess the inactivation rates of key microbiological parameters [i.e., total coliforms (TC), fecal coliforms (FC), fecal streptococci (FS) and Ascaris lumbricoides (AL)] during the composting process of fecal matters from dry composting toilets. Feces from dry composting toilets pits located at Abobo- Sabgé, Abidjan, Côte d’Ivoire, were collected every two weeks from February to June 2017 and their microbiological contents, along with two physico-chemical characteristics (moisture content and pH) were analyzed. Results revealed noticeable concentration decreases for all the microbiological parameters, except AL. The concentrations dropped from 7.72 to 3.93, 7.61 to 2.70, and 7.10 to 3.11 (log FCU/g) for TC, FC and FS, respectively, during the monitoring period. Regarding AL, there was an increase during the first 29 days, then a decrease in their concentrations over the last 3 months. Furthermore, the study revealed that all fecal bacteria followed a first-order kinetic with the inactivation rates being 0.31, 0.24, and 0.21 d-1 for FC, TC and FS, respectively. The amount of fecal bacteria in the composts suggests that an additional time is required for maturation before any uses of such material as fertilizer

Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between -1.1 and 0.99 and -1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions

Characterization of Mineral Macronutrients Kinetics During Faeces-Based Composting Process in Composting Toilets

Given the environmental concerns and public health risks that could arise, the use of composting toilets by-products requires compliance with quality standards beforehand. However, such quality assessment is often lacking for those by-products in rapidly urbanizing sub-Saharan cities. This study examines the kinetics of major mineral nutrients [i.e., nitrogen (N), phosphorus (P), and potassium (K), which are among the key indicators of a compost’s stability] during a composting process of fecal matters from composting toilets. The monitoring was carried out at Abobo-Sagbé, Abidjan, Côte d’Ivoire over a 4.5-month period. Feces-based compost data collected on 6 different dates (i.e., on the 28th , 48th , 62nd , 76th , 90th , and 133nd day from the start of the composting process) were analyzed, and screened for their contents in total N, total P and K. Results showed a rapid decrease of the content of all three elements during the first 29 days, followed by a sharp increase, especially for P and K, and then a quite stable variation during the last 2 months of the monitoring. Variations of C/N ratio during the study were similar to those reported previously. Although the proportions of P and K were satisfactory at the end of the monitoring period, the final C/N ratio was relatively high compared to suitable ratios characterizing mature composts, suggesting therefore additional time may be required before any use of the compost as fertilizer in agriculture

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg

Spatiotemporal assessment of irrigation performance of the Kou Valley irrigation scheme in burkina faso using satellite remote sensing-derived indicators

Traditional methods based on field campaigns are generally used to assess the performance of irrigation schemes in Burkina Faso, resulting in labor-intensive, time-consuming, and costly processes. Despite their extensive application for such performance assessment, remote sensing (RS)-based approaches remain very much underutilized in Burkina Faso. Using multi-temporal Landsat images within the Python module for the Surface Energy Balance Algorithm for Land model, we investigated the spatiotemporal performance patterns of the Kou Valley irrigation scheme (KVIS) during two consecutive cropping seasons. Four performance indicators (depleted fraction, relative evapotranspiration, uniformity of water consumption, and crop water productivity) for rice, maize, and sweet potato were calculated and compared against standard values. Overall, the performance of the KVIS varied depending on year, crop, and the crop's geographical position in the irrigation scheme. A gradient of spatially varied relative evapotranspiration was observed across the scheme, with the uniformity of water consumption being fair to good. Although rice was the most cultivated, a shift to more sweet potato farming could be adopted to benefit more from irrigation, given the relatively good performance achieved by this crop. Our findings ascertain the potential of such RS-based cost-effective methodologies to serve as basis for improved irrigation water management in decision support tools.Yurtdışı Türkler ve Akraba Toplulukları Başkanlığı (YTB) fon